The dawn of AI agents promises a shift in how enterprises build and deploy applications. By now, we’ve seen big tech and SaaS giants going all-in on agents. However, other companies have been quietly working on providing AI solutions for enterprises and have also rolled out no-code AI agents. Case in point: Kore.ai. 

The eleven-year-old Orlando-based Kore.ai, founded by Raj Koneru, has its second major hub in Hyderabad, which includes an R&D centre. The company helps businesses create AI chatbots and virtual assistants, and its customers span banking, healthcare, and airlines. 

Kore.ai is now enabling companies to build and deploy AI agents without extensive coding knowledge. 

Conversational AI to AI Agents 

“We provide a platform which is like a set of platform core capabilities, services, and a lot of no-code tools for builders as well,” said Prasanna Arikala, CTO of Kore.ai, in an exclusive interaction with AIM. 

The platform has evolved from conversational AI to a comprehensive agent development ecosystem. It now includes the ability to build agents, create tools for agents to interact with, and develop sophisticated RAG (retrieval-augmented generation) pipelines for multi-agent applications.

“If you have a system of record, you can build pretty much any application with agents. The application development and deployment paradigm is significantly changing, and enterprises have quickly realised that,” explained Arikala, emphasising the paradigm operational shift of enterprises. 

On the database front, in an earlier interaction with AIM, Redis VP of AI product management Manvinder Singh confirmed that Kore.ai uses Redis as a data platform to power their virtual AI agents. 

Kore.ai’s AI agents are making significant impacts across various industries. The company boasts about 450 customers, including some of the world’s largest banks and healthcare companies. 

One of its customers, a major wealth management company, deploys AI agents for its 60,000 employees. Arikala explained how these agents have dramatically reduced the time required for tasks such as creating customer proposals. 

“Previously, it would take weeks for them to compile, collect all the information in accordance with the enterprise guidelines, corroborate, build, templatise, and deliver a report. Now, they just upload the data, and it gives the outcome within minutes,” he said. 

Arikala claims that the platform’s versatility allows it to be applied across various domains, including customer service, process automation, and enterprise work management. 

“One out of the top five banks in the US deploy AI agents for their customer service,” Arikala said. 

During the COVID-19 pandemic, Pfizer leveraged Kore.ai’s AI agent platform to deploy a multilingual support system in 17 languages globally. This system assisted healthcare professionals in efficiently accessing critical vaccination-related information.

Challenges Remain

Despite the platform’s success, Kore.ai acknowledges the challenges of deploying AI agents at scale, particularly the governance part when the number of agents keeps growing. 

Arikala emphasised the need for oversight in agent development, questioning who built them, how they are being used, and whether they comply with enterprise guidelines and SOPs. Unlike workflows, agents don’t follow a deterministic approach, making safeguards essential.

To address these challenges, Kore.ai is developing solutions, such as a built-in agent evaluation service, as part of its platform. It allows for periodic assessments of AI agents, generating comprehensive reports on their performance and behaviour. 

Kore.ai envisions a future where AI agents become ubiquitous in enterprises. “In the future, the enterprise will be all about a network of AI agents, and there will be centralised orchestrators that allow for a hub for the internet sorts, “ predicts Arikala. 

As confirmed by Arikala, Kore.ai has been witnessing growth rates of 100% year-over-year for the past two years and a projected ARR of close to $150 million for the current fiscal year. With this growth rate and IPO plans in the coming years,  it seems that Kore.ai is well-positioned to lead the AI agent revolution. 

The post Kore.ai’s No-Code Agents are Out to Democratise AI Developments appeared first on Analytics India Magazine.

We have all heard the saying – cricket is not just a sport; it’s an emotion. With a global audience of 2.5 billion, including 600 million viewers in India alone, cricket stands as one of the most-watched and widely played sports. While outdoor cricket remains the ideal choice, the passion for the game has seamlessly transitioned indoors with interactive and immersive experiences shaping the gaming format.

Bengaluru-based startup MetaShot is redefining home entertainment by merging cricket with immersive gaming technology. Co-founded by Prince Thomas, Ranjit Behera, and Ajith Sunny, Metashot has developed an innovative hardware-based gaming system that allows users to play cricket in their living rooms. The idea stemmed from their love for the sport and the realisation that motion-based gaming remained largely expensive and inaccessible in India.

Launched in September 2023, the startup claims to have sold out two months’ worth of inventory in just 10 days. So far, MetaShot has sold approximately 25,000 units, with revenue growing fivefold year-over-year. 

Unlike traditional gaming consoles that rely on expensive hardware, MetaShot’s system integrates with a smart bat and a motion-tracking sensor. “Whatever you do in your living room, your avatar will do the same thing on the screen,” Thomas said, comparing the system with a Nintendo Wii-like experience at a fraction of the cost. 

This innovation has also struck a chord with parents. “We’ve had many testimonials from mothers who are happy that their kid is at least having some kind of physical exercise,” Thomas added.

Gaming with Smart Tech

MetaShot’s cricket simulation works without cameras or infrared tracking, unlike traditional gaming consoles such as Xbox Kinect or VR headsets, which use multiple cameras for motion detection. Instead, it relies solely on 9-axis sensors embedded in the smart bat, which track a player’s movements, shot angles, and power. 

The data is processed using a smartphone, tablet, or laptop, eliminating the need for an expensive console.

Interestingly, MetaShot has expanded its customer base, with 37% of users being both children and adults, 33% being adults alone, and 30% kids. 

“There are adults playing, they use it for party games, they connect with their friends, they buy in bulk, three or four friends will buy and they will play together. So, we are increasingly seeing those kinds of use cases,” said Thomas.

Made in India 

One of MetaShot’s core principles is to manufacture locally. “From the beginning, we were very clear [that] we would try to make it in India,” Thomas said. While sensors are imported, the bat moulds, components, and assembly are done entirely in Bengaluru. This decision aligns with India’s push for domestic manufacturing and avoids reliance on Chinese components, despite the challenges of building a hardware ecosystem locally.

Metashot has also expanded its retail presence, partnering with Blinkit for quick deliveries, making their product accessible within minutes for last-minute purchases, especially for parties and group play. 

Long Way Ahead

As of January 2025, MetaShot has raised approximately $2.19 million across multiple funding rounds, including a ₹11 crore seed round led by Sauce.vc. Moreover, the startup secured ₹1.6 crore for a 5% equity stake in Shark Tank India.

Despite its unique positioning, MetaShot competes with major global gaming brands. Products like the Nintendo Wii and Xbox Kinect introduced motion-based gaming years ago but failed to gain traction in India due to high costs. 

“A similar product at this price point doesn’t exist,” Thomas pointed out. While a Nintendo Switch or PlayStation costs between ₹40,000 and ₹50,000, MetaShot’s cricket game is priced significantly lower at ₹5,000, making it far more accessible.

Gaming giants such as Sony, Microsoft, and Nintendo dominate the high-end console market, selling 2-3 lakh units annually in India. Thomas believes that if MetaShot reaches its full potential at a lower price point, the company could surpass those numbers. “If we start hitting our potential, we can do much more.”

MetaShot is not alone in the sports-tech gaming market. Companies such as StanceBeam and Freebowler have developed smart cricket equipment, while Motion Sense AI is working on AI-powered motion tracking for gaming. 

As MetaShot expands, its next frontier includes multisport gaming. The team is already developing a universal motion-tracking device that allows users to switch between cricket, tennis, and other sports by simply changing the bat or racket attachment. 

“We are working on a universal device that lets you play multiple games. Most likely, we will launch it [in the] next financial year,” Thomas concluded. 

The post This Shark Tank-Backed Bengaluru Startup Brings Cricket to Your Living Room appeared first on Analytics India Magazine.

Sridhar Vembu, former CEO of Zoho Corp, recently highlighted the need to stop glorifying English in India’s R&D ecosystem. “There is a lot of R&D talent in India if we get rid of the English barrier and the social stigma of not knowing English well,” he posted on X. 

“I am right now working with extremely capable engineers on some advanced tech, and we converse in Tamil because that is what they are most comfortable with,” he added.

While Vembu’s emphasis on local languages was one aspect of the discussion, the localisation of AI solutions is gaining momentum. Indian companies are increasingly preferring homegrown AI startups over big-tech firms.

The Preferred Choice 

In a past interaction with AIM, MN Anucheth, the JCP of Bengaluru Traffic, spoke about the traffic department working with several homegrown AI startups to leverage AI solutions. 

“Since Bengaluru is the tech capital of India and a lot of AI-based startups are based in the city, we have been lucky enough to be able to work with many such companies,” he said. “AI has been made accessible to us, for which we would otherwise rely on some foreign import or off-the-shelf product, which generally do not work in real-time conditions.”

The Bengaluru Traffic Police has collaborated with many Indian startups to enhance AI-driven traffic management. For instance, Monday Technologies supports AI avatars for awareness videos and drone-based monitoring to detect road blockages and accidents. Other key partners include IBI (the developer of ASTraM), Skita, and Matrix Technologies, with Videonetics as the OEM. 

Anucheth explained that continuous feedback helps refine models, such as improving seatbelt detection accuracy. Though big-tech firms such as Google and Cisco offer traffic management solutions, authorities prefer to maintain flexibility and control over their infrastructure.

“Nothing against big tech companies, but I think our experience has been that we can’t work with them to give tailor-made solutions to us,” said Anucheth. 

Nuanced Approach

From an investor’s perspective, VCs look to invest in Indian AI startups that offer nuanced solutions that big-tech companies cannot deliver. 

Citing Google’s PaLM models as an example, Capria Ventures’ explained to AIM how startups have an edge in understanding vertical-specific needs. In the health sector, especially hospitals, where the radiology department requires in-depth analysis, a local player has a better edge.  

“The big-tech models are going to be there to prove the science of the underlying technology they have, but they are not good at solving the vertical needs of what the radiology department at hospitals need, top to bottom,” said Will Poole, co-founder and managing partner of Capria Ventures, in an interview with AIM earlier.

Poole cites 5C Network, which has spent seven years developing a specialised, end-to-end solution tailored for radiology departments. While AI models play a role, they are just one component of a much larger system.

Big tech may develop powerful models, but access to high-quality, diverse medical imagery is essential for training effective AI. “What Kalyan, the founder of 5C, built was a network of hospitals from which he takes images. A radiologist can read those images… He’s done 11 million of them, adding, I don’t know, half a million per month,” Poole said. 

“Without that kind of data… you’re not going to be able to build an AI that’s as good as somebody who has it,” he added. 

Poole explains that for investors, the key lies in backing founders with proprietary data access, enabling them to scale rapidly and outpace competition. 

Indic Models

Navana AI, a Bengaluru-based voice AI startup that develops indigenous AI-powered speech recognition and NLP solutions, told AIM that big tech companies posed a lot of problems when it was in the research phase of trying out interfaces with NLP. 

“We started using Google, Microsoft, AWS, all of the existing services that were out there, but none of them worked for Indian languages at that time. Even today, most don’t work for non-major languages or low-resource languages,” said Raoul Nanavati, co-founder and CEO of Navana ai. 

While big-tech companies have been collecting and building language data, it has mostly been English and Western languages such as French, German, and Spanish, as these native language users have been on the internet for decades. 

This has been a challenge for these companies when they build for India. “So there’s a cold start problem in India for language AI,” said Nanavati. 

Recognising this, the team decided in 2018-19 to develop indigenous speech recognition AI and NLP solutions for all Indian languages, overcoming the data scarcity that had hindered progress in this space.

With the nuanced approaches each startup offers, enterprises are actively seeking to collaborate with Indian AI startups, a trend that shows promising potential.

The post Why Indians Prefer Homegrown AI Startups to Big-Tech Companies appeared first on Analytics India Magazine.

By now, you must know that China’s latest AI model, DeepSeek-R1, has been the centre of all conversations for having built a SOTA model with scant resources with respect to compute and cost. It shattered the idea that building a model as capable as OpenAI’s on a $10 million budget is impossible—remember Sam Altman’s last visit to India? 

With DeepSeek setting a precedence for everyone, India has gotten the boost it always needed. 

India’s DeepSeek Ambitions

When US President Donald Trump announced Project Stargate a week ago, India got talking about building in the country. India building its own Project Stargate was portrayed as a necessity, with many tech leaders weighing in on the conversation. 

The discussions had barely died down when DeepSeek brought forth the next idea of why India can’t build one just like it.

Well, India is already building it. 

“Yes, we definitely are! It won’t be a 671B parameter one (to begin with), but it’ll be a frontier model in its parameter category,” said Abhishek Upperwal, founder and CEO of Soket AI Labs, in an exclusive interaction with AIM.  

“The pace of development will depend on the kind of funds we get access to, but we are gonna definitely build it,” the founder of the Gurgaon-based AI research startup added. 

Upperwal stated that Pragna-1B (Soket’s AI model) marks the team’s initial step toward developing frontier models. The 1.25 billion-parameter model was trained on a budget of just $100K, covering both synthetic data and compute costs. 

“The plan is to bootstrap bigger models using smaller ones and any open-source model with a permissive license—while keeping compute costs dirt cheap,” he said. 

He highlighted that high-quality data and training optimisations make this approach feasible, pointing to DeepSeek as a successful example.

Upperwal noted that if  “less resources” translates to $2-3 million, the prospects for building frontier models are either bleak or significantly slow. In such a scenario, companies would have to prioritise revenue-generating products over AI model development.

“I think we need at least $10 million to start working on frontier tech, and this money should be purely dedicated to R&D for building these models—no distractions like building applications or even thinking about GTM. This is where investors and founders need to align with patient capital,” said Upperwal. 

Similarly, Reliance-backed Indian AI startup TWO AI is building a cost-efficient multilingual AI model family with speech, search, and visual processing in 50+ languages. It believes it has already been building DeepSeek-like models. 

“DeepSeek’s RL-only post-training approach and insights like distilling reasoning into smaller models really resonate with what we’re doing at TWO AI,” said Pranav Mistry, founder and CEO of TWO to AIM. 

Mistry believes the AI race now demands rapid innovation rather than massive compute power. “Gone are the days when you needed a 20,000 GPU farm to train a single model,” he said. 

He added that TWO AI has demonstrated this with its SUTRA model, which outperforms SOTA models in the official MMLU for Indian languages despite being trained on a $2 million budget. 

While greater resources can accelerate innovation, optimised approaches are proving just as crucial. “Of course, more resources can help accelerate the speed at which we can innovate,” he added. 

Pratyush Kumar, co-founder of Sarvam AI, another Indian AI startup that is developing LLMs and GenAI solutions for Indian languages, recently posted on X inviting Perplexity co-founder Aravind Srinivas to join their mission. 

“Aravind, at SarvamAI we are building sovereign models that combine deep reasoning and Indic language skills. Would love to have you join this mission!” he wrote. However, when AIM reached out, Sarvam AI declined to comment on DeepSeek. 

Multimodal AI platform Krutrim AI, started by Ola’s Bhavish Aggarwal, is also on a mission to cater to the Indian audience via their multilingual platforms. 

What is Stopping India? 

“Very soon, we will also have our own LLMs,” said IT minister Ashwini Vaishnaw, at the recent Utkarsh Odisha Conclave. “In the India AI compute facility, we have received compute bids for creating 18,000 GPUs,” he said. 

While the government is slowly encouraging and providing incentives to promote AI in India, VCs are still sceptical about investing fully in it. 

“The problem is that the benefit here isn’t immediate revenue generation, which is why VCs run away from these kinds of ventures. But the real ROI is in gaining the know-how of building intelligence at scale, which can create value in a hundred other ways (just imagine the kind of leverage DeepSeek holds today),” said Upperwal. 

“Intelligence and the know-how to build one will be the most valuable IP in the future,” he added.

Upperwal believes that to reach DeepSeek R1’s level, we will need at least $50 million. “DeepSeek is already on its 3rd version, plus multiple other models. The cost to get here should be the aggregate of everything they’ve spent so far. I’d estimate $50-100 million,” he said. 

He believes the key lies in securing adequate R&D funding (ranging from $5-10 million per startup) for at least 4-7 teams. “Sarvam is the only startup with access to such funds, but it’s splitting its focus between figuring out use cases and building models, which slows down progress,” he said. 

In a blog post, Zerodha co-founder Kailash Nadh shared his views on DeepSeek, focussing on research and human capabilities as a priority. 

Nadh believes that India’s AI sovereignty and future depends not on a narrow focus on LLMs or GPUs but on building a foundational ecosystem that encourages breakthroughs through a blend of scientific, social, and engineering expertise across academia, industry, and civil society. 

“In fact, the bulk of any long-term AI sovereignty strategy must be a holistic education and research strategy. Without the overall quality and standard of higher education and research being upped significantly, it is going to be a perpetual game of second-guessing and catch-up,” he said. 

The post DeepSeek-Style Innovation Already in the Works in India appeared first on Analytics India Magazine.

OpenAI finally introduced the ChatGPT moment for AI agents with its new AI agent ‘Operator’. The agent can perform tasks on the web without human intervention based on users’ instructions. Notably, the focus of every enterprise and startup is on AI agents capable of performing tasks independently. Indian founders are no exception as they advance into the next phase of AI agents, now powered by voice capabilities.

The Rise of AI Voice Agents

Moving from text-based interaction to using voice to activate tasks and agents to run them is a trend that AI startups in India are actively pursuing. 

Sudarshan Kamath, founder of smallest ai, which builds text-to-speech models and voice agents, shared his views on voice agents. The company’s journey into voice AI began with the realisation that everyone has a very different voice that they like. To address this, smallest.ai introduced voice cloning, which allows users to create customised voices with reference audio. 

Smallest.ai’s focus on AI agents is rooted in their potential to handle complex tasks in real time. “There are companies who are moving away from IVR-based systems to voice bot-based systems, and these voice bot-based systems are smarter, more interactive, and more realistic,” Kamath said while interacting with AIM. 

He explained the use case of these voice agents in content creation, such as companies producing product videos or marketing campaigns. “Or, it could be individuals who are influencers or social media accounts who are basically trying to create content on Instagram, YouTube,” he added. 

Why Voice? 

Kamath highlighted how large enterprises, including publicly listed ones, are increasingly exploring voice-based workflows. “This shift has happened because generative AI has made these voices much more realistic while maintaining very low latencies,” he said. 

Kamath also believes that voice-based solutions offer a high return on investment (ROI) and significantly enhance engagement and user experience. “So, investors are fairly bullish about the voice as the market itself is going to grow.”

Bengaluru-based conversational AI and voice automation startup Gnani.ai claims to currently handle 30,000 concurrent conversations and a few million voice AI conversations daily. Their voice-first SLMs for Indian enterprises are trained on millions of audio hours and billions of Indic language conversations. 

“Indian AI startups are focusing on building voice agents due to the country’s diverse linguistic landscape, the rapid adoption of smartphones, and the increasing demand for seamless customer interactions across industries,” Ganesh Gopalan, co-founder and CEO of Gnani.ai, told AIM. 

“The rise of vernacular voice interfaces also aligns with the push for digital inclusion in India, enabling startups to cater to a broader audience while tapping into the growing demand for localised, AI-driven solutions,” he added.  

Gnani.ai caters to industries such as banking, finance, and insurance and helps them use AI-powered solutions for tasks such as customer support, lead qualification, EMI collection, and insurance renewals.

“Some focus on multilingual support with high accuracy in regional languages, while others emphasise industry-specific solutions, such as BFSI, healthcare, or retail, tailoring their AI to address niche requirements,” he said.

Another Bengaluru-based voice AI startup, Navana.ai, develops indigenous AI-powered speech recognition and natural language processing (NLP) solutions. Having worked with institutions such as IISc Bangalore, IIT Madras, and Bhashini on open-source data collection efforts and co-authoring academic papers. Their voice agents are integrated into applications for industries such as BFSI, agriculture and government services. Ujjivan and Bajaj Finserv are a few of their customers.

“In the last year and a half to two years, the big shift came when LLMs came around and made telephony a very viable channel to reach all of India and plug in AI to do all sorts of services,” Raoul Nanavati, co-founder and CEO of Navana ai, said during an interaction with AIM. 

Nanavati believes that voice agents are gaining traction because they make digital services accessible to first-time internet users and address India’s linguistic diversity. “None of them [Google, Microsoft, AWS] worked for Indian languages at that time. Even today, most don’t work for non-major languages or low resource languages,” he emphasised. 

What Next? 

With voice gaining prominence as a key mode for AI agents, the focus is now probably shifting toward identifying the next trend in the field.

“After voice agents, the next trend in AI is likely to revolve around multimodal AI agents that integrate voice, text, and visual interactions for more immersive and context-aware experiences,” Gopalan said.  

He believes that such systems can improve user engagement and create a more intuitive and enriched interface.

“Additionally, the focus will shift toward hyper-personalisation powered by generative AI, where conversational agents predict and adapt to user needs in real time,” he concluded. 

Similarly, Praveer Kochhar, co-founder of Kogo Tech Labs, which recently unveiled universal voice assistants for automobiles, believes the agentic systems will move towards larger goal accomplishment. “This transition from task-oriented agentic flows to goal-oriented flows is the next big thing that you’ll start seeing, whether it’s in front office, back office, direct to customers, everywhere,” he told AIM.

The post Why India Loves AI Voice Agents appeared first on Analytics India Magazine.

Talking to your car isn’t a new concept anymore. Voice assistants like Google Assistant and Siri made this possible years ago. Now, an Indian AI agentic assistant is taking this innovation to the next level and aiming to transform the driving experience for millions with an agentic mesh framework.

Bengaluru-based AI startup Kogo Tech Labs recently unveiled India’s first universal voice assistant for automobiles at the Bharat Mobility Global Expo 2025. A few months ago, the startup announced an AI agent store offering AI tools, agents and plugins.

“Unlike Siri and Alexa, these agents not only give you access but can also operate apps and services on your behalf,” said Raj K Gopalakrishnan, co-founder and CEO of Kogo Tech Labs. 

Unlike existing voice assistants such as Siri or Alexa, Kogo’s assistant operates at a deeper level, directly interacting with applications to perform actions such as initiating navigation rather than opening a navigation app alone. It can execute a variety of commands, from controlling car hardware (e.g., switching on wipers or adjusting air conditioning) to managing apps and services.

“This assistant is not just a showcase but a practical application of what’s possible with our technology. Whether it’s a truck driver topping up a FASTag or a family planning a vacation, the assistant provides a versatile, multi-language platform to meet diverse needs,” Gopalakrishnan noted.

According to him, the new capability extends across domains, from navigation to booking airline tickets and provides users with a unified, intelligent assistant that can handle diverse tasks seamlessly.

Enter MapmyIndia 

As a voice for automobiles, navigational capabilities become critical and, to address that, Kogo has strategically partnered with geo-intelligence company MapmyIndia.

Through the partnership, MapmyIndia’s advanced geo-intelligence stack, along with Kogo’s AI assistant, offers navigation and location-based services. Kogo’s assistant will be able to support a wide range of automotive applications, from real-time navigation to enterprise logistics. The partnership will also provide access to MapmyIndia’s extensive customer base, including 30 original equipment manufacturers (OEMs) and numerous enterprise clients. This will position Kogo to scale its solutions effectively.

“Mobility, by definition, requires geo-intelligence, and who better than MapmyIndia? They are the leaders in geo-intelligence, at least in India. They also have a lot of deep learning and access, through their partnership with ISRO and NAVIC, etc,” Gopalakrishnan highlighted. 

Notably, a few years ago, MapmyIndia acquired more than 26% stake in Kogo Tech Labs. 

Google, ChatGPT? 

Big tech companies have partnered with automobile manufacturers to host their cloud and assistant features. Recently, Google Cloud announced its agentic integration with Mercedes. Similarly, ChatGPT is also integrated with Mercedes. 

Drawing parallels, Gopalakrishnan explained that while they are catering similarly, their platform works on an agentic mesh. “So right now, what this means is that it’s not dependent on one service. It potentially gives access to millions of apps and businesses because they can all now talk to each other. So, we’re platform agnostic,” he said.  

Kogo is already testing its platform with four OEMs – two in North America and two in India. “It’s a similar kind of approach where we will go in right at ground zero and implement this.”

They are also working on integrating their platform at a stack level with major semiconductor players, which will be announced in the coming months. 

Way Ahead

While voice assistants have become a popular trend, Kogo’s leaders caution against viewing them as a one-size-fits-all solution. “Voice agents are fascinating, but their true potential lies in specific use cases where they outperform traditional interfaces, such as while driving or during hands-free tasks,” said Praveer Kochhar, co-founder of Kogo Tech Labs. 

He predicted that by 2025, real-world deployments of voice technology at scale will become more commonplace.

Looking ahead, Kogo is focusing on enhancing the cognitive capabilities of its agents. “We’re working on rolling out goal-oriented agents in three months, capable of solving complex problems independently,” Praveer shared.

He also envisions a shift in AI capabilities from task-oriented to goal-oriented frameworks. 

“You will see a huge capability in the cognitive ability of agents and tools, which means they will be able to solve complex tasks. They will be able to chart out their own pathway of solving problems,” he concluded.  

The post This Bengaluru AI Startup Could Change How Your Car Thinks appeared first on Analytics India Magazine.

Imagine watching a dubbed movie, but the characters’ lip movements stubbornly stick to the original language, completely breaking the illusion that movies are meant to be. While dubbing, translation, and subtitling may be sorted, if this aspect is ignored, the movie experience is dampened. 

In comes NeuralGarage, which is on a mission to transform the dubbing industry.

AI Facial Sync

The founders of NeuralGarage

Bengaluru-based NeuralGarage was founded in 2021 by Anjan Banerjee, Subhashish Saha, Subhabrata Debnath, and Mandar Natekar, who have known each other for decades. The platform is looking to solve the problem of mismatched facial movements in dubbed content. 

NeuralGarage was one of seven Indian AI startups selected for the AWS Global Generative AI Accelerator Program. It received $1 million in AWS promotional credits, among other support initiatives. 

Its technology, VisualDub, not only addresses lipsync movements but also ensures the final product looks natural and high-quality. “The challenge is maintaining the video quality when adjusting facial expressions, especially with no limitations on camera angles and lighting,” the company stated. 

“It cannot look AI-ish,” it emphasised, particularly when working with renowned actors whose facial movements are unique.

“For example, if you’re watching Money Heist, the professor is still speaking in Spanish while you are listening to him in English. What we do is change the facial movements of the actor so it looks like he has spoken in English,” explained Subhabrata Debnath, co-founder and CTO of NeuralGarage. 

Advertising and Movie Industry 

While dubbed content has become popular, especially on streaming platforms such as Netflix, NeuralGarage sees an opportunity to further enhance the viewing experience. “Once you see it with sync, it is very difficult to go back,” they noted. 

Prime Video’s global head recently acknowledged this challenge, stating that content is still not consumed well because facial expressions and lips don’t always match.

NeuralGarage is already being leveraged by major players such as Amazon India, Coca-Cola, and others in advertising, allowing brands to repurpose content with different messages. Debnath explains advertisements shot in December can be updated in February to reflect changing discounts or festivals. 

“Amazon India recently started using our solution to repurpose the same video footage with different messages for seasonal campaigns,” said Debnath. 

In the film industry, NeuralGarage collaborates with movie distributors, such as Europa Movies, which has a 95% market share in India. Through these partnerships, they address challenges related to global distribution, piracy prevention, and dubbing with realistic synchronisation.

Hurdles Ahead

NeuralGarage faces significant challenges in perfecting its dubbing and facial synchronisation technology. A primary issue is ensuring that facial adjustments seamlessly match the original high-quality video. The technology also needs to adapt to varying camera angles and lighting conditions, as films are not shot with AI’s constraints in mind.

Maintaining a natural, non-AI-generated look is especially crucial for high-profile actors. “Shah Rukh Khan’s face is his identity. For movie stars, the expectations are very high,” he said.

The scalability of the solution presents another challenge, with films often exceeding 300 GB in size. 

However, the challenge of competition persists. Additionally, several AI startups, both globally and in India, are working in the same space. Dubverse.ai and Vitra AI are a couple of names. However, NeuralGarage is looking to go big in 2025. 

The platform plans to boost its visibility in 2025 by actively investing in marketing and sales, transitioning from its reliance on organic word-of-mouth within the post-production industry. “This year, we will invest in marketing and sales so that the solution reaches more hands,” he said. 

After raising $1.45 million from Xfinity Ventures in 2022, the company is preparing for a Series A funding round to scale operations and refine its product. The company’s goal is to ensure that more businesses try its solutions, which streamline subtitling and localisation processes. 

The post This AWS-Backed Indian AI Startup is Transforming the Dubbing Industry appeared first on Analytics India Magazine.

Computer-aided design (CAD) is considered the backbone of the construction industry, but the global market doesn’t seem to believe this. While CAD is indispensable in India, the US has restrictions that prevent vendors from relying on it, causing construction inefficiencies, especially in landscaping. 

An Indian AI startup has come forward to address this problem on US soil. 

Why USA, Not India? 

Founded in 2017 in Delhi by three friends from IIT-D, Attentive.ai provides AI-powered solutions for the landscaping and outdoor services industry. 

Attentive.ai has deliberately focused on North America, citing distinct market dynamics as the reason for bypassing the Indian market for now. In the US, takeoffs (measuring site areas) from PDFs are standard due to intellectual property concerns, making the process more labour-intensive. 

In contrast, Indian firms often use CAD files, which allow for quicker and easier measurements.

“The Indian market operates differently. CAD files are more widely circulated here, which simplifies takeoffs. In the US, PDF files dominate, and extracting measurements from them is significantly harder,” explained Shiva Dhawan, co-founder and CEO of Attentive.ai, in an exclusive interaction with AIM. 

This fundamental difference in workflows makes the US a more suitable market for Attentive.ai’s solutions. 

Additionally, Dhawan pointed out the economic advantages of targeting North America. “We aren’t focusing on India in the short- or medium-term because the US market offers better opportunities and cost arbitrage,” he said.

Attentive.ai initially focused on AI services, building expertise in computer vision. However, a strategic pivot in 2021 led the company to develop SaaS solutions tailored for the commercial landscaping and construction industries in the US and Canada markets. 

“We realised services were a means to an end, and recurring revenue is where valuable businesses are built,” Dhawan said.

The Promising Landscaping Industry

Attentive.ai’s flagship product, AutoMeasure, automates takeoff processes for landscaping companies, while Beam serves construction firms. 

Traditionally, takeoffs required hours of manual effort with drawing tools. Professionals would click and trace polygons to calculate dimensions manually, which was tedious and time-consuming. Attentive.ai’s platform eliminates this hassle by using AI to automate 60% of the work. Human experts verify the remaining for quality assurance.

“The way it works is simple. Customers upload a site plan or input an address, and within hours or a day at most, they receive accurate measurements,” Dhawan explained. This process not only improves efficiency but also enhances accuracy, enabling firms to redirect resources to more critical tasks. 

“Our tagline is ‘get time back,’ as we save companies significant time, allowing them to focus on other activities,” he added.

This efficiency has driven rapid adoption in North America; Attentive.ai now serves over 1,000 clients in the US and Canada. The company’s ability to cater to an underserved yet essential market has set it apart in a competitive landscape.

Addressing a Niche Market

Its success stems from its ability to address a niche yet massive market. AI companies have historically underserved the billion-dollar landscaping and construction industries. Attentive.ai identified this gap and leveraged its expertise in computer vision to create tailored solutions.

“These industries have huge potential but are largely ignored by technology firms. If you can build for them, it creates a moat,” Dhawan explained. This focus on niche AI applications, rather than saturated areas such as call centres, has differentiated Attentive.ai and helped them attract significant investments.

Since its pivot to SaaS, the company has raised $18 million from PeakXV Surge, Vertex Ventures, Tenacity Ventures and others. “These investors believe in our vision of AI-enabled services transforming trillion-dollar industries,” Dhawan said.

Challenges Remain

While the company has made impressive strides, it faces its share of challenges. Chief among them is talent acquisition. “Finding good talent and convincing them to work in an unconventional industry is tough,” Dhawan admitted. 

The niche nature of the landscaping and construction sectors often makes it harder to attract top-tier talent. Despite this, Dhawan believes the company’s outsider perspective has been an advantage. 

“I’m not from the construction industry or a civil engineering background, but that allows us to see problems more clearly. Our team includes both industry outsiders and experts, which helps us balance fresh perspectives with domain expertise,” he said.

Another challenge is maintaining customer trust in an industry historically reliant on manual processes. However, Dhawan highlighted that the shift is already underway. “Many firms that previously managed takeoffs in-house now outsource them to us because they trust our service and see the time savings,” he noted.

Attentive.ai’s future plans are focused on scaling current offerings rather than diversifying into new products. “For 2025, our priority is doubling the revenue and expanding our reach in North America. Global expansion may follow, but not immediately,” Dhawan stated.

“AI-enabled services are not a fad but a reality that will transform trillion-dollar industries,” he asserted. As more firms adopt AI solutions, Attentive.ai’s early mover advantage in the landscaping and construction sectors gives it a significant edge.

Dhawan mentioned that companies such as Bluebeam, StackCT, and PlanSwift might be considered competitors, which cumulatively add to $6 million in revenues. However, they are into manual drawing tools in the construction space. For now, none offered similar services using AI, but Dhawan is sure they’ll arrive soon. 

The post This PeakXV-Backed Indian AI Startup is Landscaping US Lands appeared first on Analytics India Magazine.

Microsoft’s $3 billion commitment to expand the Azure infrastructure in India may have grabbed the limelight at the Microsoft AI Tour in Bengaluru; however, the company’s push to revolutionise sectors such as healthcare and agriculture also led to some key announcements by CEO Satya Nadella. 

An AI agritech startup that has partnered with Microsoft is working to eliminate guesswork in farming and empower farmers with science-backed insights to make effective decisions. 

Meet AgriPilot.ai 

Maharashtra-based AI startup AgriPilot.ai has collaborated with Microsoft Research for over five years, integrating AI, satellite imagery, and other tools to transform the farming sector. 

Identifying critical factors such as soil nutrient levels, water availability, and suitable weather conditions are some of the areas AgriPilot.ai specialises in to optimise crop yield and resource usage. 

“We have started seeing the results. Satya took that in his showcase because these are proven models now,” Prashant Mishra, founder of Click2cloud Inc., which hosts the platform AgriPilot.ai, told AIM in an exclusive interaction. Mishra confirmed that experimentation has been done for more than 2,50,000 hectares of land around the world. 

“This [AgriPilot.ai] is precisely for the marginalised farmer because Microsoft wanted to work with those with less than two acres of land. So, about a thousand farmers, with less land and resources, are currently benefitting from it,” said Mishra, emphasising that their goal is to prevent farmer suicides and distress by making them self-sustainable. 

The startup has conducted experiments, such as cultivating sugarcane thrice the size of conventional crops. It claims that the yield has doubled. Akin to conventional farming, the startup has also enabled the farming of exotic vegetables such as strawberries and dragon fruits on local farms. 

“Normally, five-star hotels import strawberries and dragon fruits from other countries, but with AI, we are able to grow them on local farms. Thanks to these exotic vegetables, the poor farmers are able to multiply their earnings, probably by 10 times or more,” he said. 

Agripilot.ai has collaborated with the Agriculture Development Trust, Baramati, which claims that these new tools have increased crop production by 20%, as presented in the Microsoft keynote session.  

AgriPilot employs a ‘no-touch’ approach, utilising satellite and drone imagery to gather farm data remotely. This allows them to provide detailed crop management plans, from pre-planting to harvesting, customised for farmers. This has all been made possible with the help of AI. 

The Microsoft Bond

AgriPilot has built a strong partnership with Microsoft Research, leveraging its advanced tools and platforms to transform farming practices. Though Microsoft has not directly invested in AgriPilot.ai, the latter depends on it for critical technological support and open-source tools. 

Nadella also met with the team at ADT Baramati, which uses AI tools to help farmers achieve healthy and sustainable harvests. 

The startup integrates Microsoft Azure Data Manager and FarmBeats to analyse soil health, monitor water availability, and optimise fertiliser usage through precise, data-driven insights. 

Empowering Women

Besides, AgriPilot partners with Pratham, a non-profit organisation, to train farmers in using these advanced technologies. This collaboration supports farmer education and provides employment opportunities for women, enabling them to conduct AI-powered soil testing independently using on-site machines.  

To ensure accessibility, instructions are provided in local languages, such as Marathi, Kannada, Hindi, Telugu, Tamil, Farsi, and Hebrew, translated through Microsoft Copilot. The collected data is then fed into AgriPilot’s platform, where AI models analyse it to deliver actionable results and outcomes for the farmers.

AgriPilot is also conducting experiments in countries like Qatar, Dubai, Peru, USA, Malaysia and India. “Baramati [Maharashtra] in India was the first [place where we experimented], and now we are doing the same in Uttar Pradesh with Microsoft,” he said. 

Big Tech’s Agri Mission

Meanwhile, other big tech companies have also been actively involved in the agri space. Last year, Google announced the availability of its Agricultural Landscape Understanding (ALU) Research API, which integrates satellite imagery with AI to deliver farm-level insights. 

The API is designed to support India’s agricultural sector by enabling data-driven decision-making, optimising farm management, and addressing productivity challenges.

Google recently partnered with the UP government to launch a Gemini-powered open network for farmers. This DPI for agriculture utilises Google’s DPI-in-a-box solution and the Beckn protocol. 

Big tech is also powering agritech startups such as Bengaluru-based Cropin, which helps farmers make informed decisions based on historical, present and future data. 

When asked about future collaborations with other companies, Mishra was clear that their focus is currently on Microsoft alone. This year, they will work towards improving accuracy before proceeding to full-scale expansion. 

The post ‘Microsoft’s AI Tools Help Maha Farmers Increase Yield by 20%’ appeared first on Analytics India Magazine.

Google and Meta have revolutionised marketing by turning data into performance analytics. Can something similar be done to ease the burden on teachers by making the process more data-driven? 

This idea sparked the journey of Kushal Sinha and Piyush Kumar, the founders of Chicago-based LearnQ.ai, a smart learning platform. Their efforts culminated in VEGA (Virtual Entity for Guidance and Assistance), a specialised AI agent designed to guide and assist with any task.

It represents their effort to develop a teaching assistant that can support educators and institutions, all while maintaining the core AI and data-driven approach.

In an exclusive interview with AIM, Sinha, an IIT Guwahati alumni, said, “At this stage, AI is deeply integrated into the platform, consuming vast amounts of data to provide recommendations and insights. It thereby enhances the teaching experience and empowers educators with actionable intelligence.”

The duo wanted to take a focused approach. So, instead of going too broad, they decided to test the platform on a specific use case. “We chose the SAT exam, which is widely taken in the US for undergraduate admissions. That was our initial use case. Recently, however, we’ve started allowing early customers and clients to create their own courses,” Sinha added. 

Now, they are enabling people to build courses tailored to their needs. In about a month, they’ll complete the rollout and open the platform to everyone, allowing users to build courses from scratch and deploy them.

“When we say “deploy,” the idea is to capture student interaction data—what they’re learning, where they’re struggling—and expose this data to both teachers and our AI assistant, VEGA. 

“VEGA acts as an AI tutor or avatar, leveraging the student’s knowledge graph built through interactions, assessments, quizzes, and even chat history. It also integrates the professor’s expertise,” Sinha explained. 

AI Assitant is Not AI Tutor

Kumar, the other co-founder and a former home tutor, told AIM that unlike traditional AI tutors, which often rely solely on prompts or pre-trained large models, their approach integrates real-time student data.

“For example, think of a smartwatch. If you’re self-motivated, you’ll act on its suggestions; if not, someone—like a family member—would nudge you. Learning works similarly. Some students are self-motivated, but most need additional support.”

Sinha mentioned that they use data pipelines to build a detailed knowledge graph for each student. This enables their AI system to cater responses to the student’s specific understanding level. It’s not just a wrapper around a large language model, it’s an agentic AI system that integrates data from multiple streams to deliver personalised, actionable insights.

People are now creating courses for AP, JEE, and even unique topics like teaching the Mahabharata and Ramayana. “This highlights the flexibility of our platform. Institutions like The Doon School in India are already using it to teach K-12 students,” said Sinha. Another organisation they are actively in discussions with is Allen in India. 

Why Edtechs Fail? 

“The first generation of ed-tech platforms was largely driven by the rise of the internet. Platforms like Coursera and Udemy focused on accessibility. They allowed teachers to record lectures and broadcast them widely, building platforms that connected learners to content,” Sinha mentioned. 

According to him, these platforms didn’t address efficiency. Even with all the advancements in ed-tech, students relied on tutoring services, marketplace-based learning models, and physical tuition centres. 

These platforms were more of an add-on rather than a replacement or an efficiency enhancer in education. That’s where LearnQ.ai’s approach differs, as they aim to solve for efficiency in education.

What’s Next?

Recently, Sinha and Kumar met with American entrepreneurs working with public schools in several states, who narrated a compelling success story. Four years ago, their schools ranked last in the district. 

However, by implementing a data-driven approach and using simple Excel sheets, their performance saw a sharp improvement, pulling them to the top spots in the district. Most students now score within a tight 70 to 90 range. 

That explains how any small technological adoption in the education system can bring vast improvements in a student’s performance. With AI, things are looking even better. 

In the current landscape, many AI tutors lack a robust data layer. A truly effective one must be able to understand where each student is in their learning journey and provide tailored guidance. This becomes specifically important since teachers often struggle to give personal attention to each student due to time constraints.

For instance, if you’re watching a lecture on Coursera and don’t understand a concept, there’s no way to directly ask the instructor for clarification. This is where AI avatars for teachers and virtual assistants that provide 24/7 access step in.

Another much-needed AI intervention would be a feature to transcend language barriers. For example, a teacher who speaks only Hindi and English can have an avatar that interacts in Korean or Mandarin. This would dramatically expand accessibility, allowing the same educator to engage with thousands of students simultaneously, addressing doubts and providing real-time support.

Another upcoming feature empowers educators and creators to design their own AI tools, which will align with their unique processes and expertise. For instance, a blogger could train the AI to replicate their specific writing style or workflow using a knowledge base they provide. 

These customisations open up endless possibilities for personalisation and efficiency across educational and creative fields.

The post How These Indian Entrepreneurs Help US Students Crack SAT appeared first on Analytics India Magazine.

The year 2024 witnessed the global adoption of an agentic AI force. From big tech companies to emerging startups, AI agents took centre stage. Keeping the momentum going, 2025 is set to be even bigger in the agentic space. A noticeable trend shows that startups building AI agents are largely being founded by Indian developers. 

The AI agents market has been witnessing promising growth. From $5.1 billion in 2024, the market is expected to hit $47.1 billion by 2030. In particular, Indian entrepreneurs have been significantly driving the growth. 

Source: X

India and AI Agents

“If you look at how LLMs have matured, it’s clear that their potential plateaus once they run out of fresh training data. AI agents pick up the slack by plugging into external sources [such as] databases, APIs, real-time feeds, and letting the model stay relevant as events unfold,” Ramprakash Ramamoorthy, director of AI research at Zoho and ManageEngine, told AIM. 

“This is why you’re seeing founders worldwide, including those from India, pushing AI agents to the forefront; it’s simply the next logical milestone for the technology after robust LLMs.”

Ramamoorthy believes that the effort to integrate AI with new data streams represents an evolution rather than a revolution and is not defined by nationality or geography but by the determination to prevent AI from becoming stagnant. “AI agents give us that infusion of timeliness and context that static LLMs alone can’t deliver.” 

Bengaluru-based AI startup Kogo AI, founded by Praveer Kochhar and Raj K Gopalakrishnan, is building AI agents and solutions to simplify workflows and improve productivity for businesses. Recently, they also launched an AI agent store. 

“We are currently building an agent that can look at a database and actually think like a data scientist or a business analyst and generate extremely intelligent questions,” Kochhar said in a recent podcast with AIM. 

The founders also emphasised the advancements in foundational models, which have made it easier and cheaper to build AI agents.

Kochhar explained how building customer support and voice-based AI agents was initially expensive, costing around ₹50 per interaction, which was unsustainable for practical use. However, advancements significantly reduced this cost to approximately ₹2.5 per interaction, making such deployments more feasible.

A number of Indian AI agents have been making the mark. A few Indian founders under the Y Combinator cohort are also building AI agents. For example, Floworks, founded by Sudipta Biswas and Sarthak Shrivastava, selected under the Winter 2023 cohort of Y Combinator, is building AI agents that will address sales functions. 

Prominent AI startups such as Sarvam AI, backed by PeakXV Partners, Khosla Ventures and CoRover, backed by Venture Catalysts, and educational institutions also have AI agents.  

AI Agents are Everywhere

With AI agents quietly becoming the norm, choosing the right sector where these agents will become the most beneficial becomes critical. “Departments such as sales, marketing, and finance usually have well-established software systems like CRM (customer relationship management), ERP (enterprise resource planning), analytics dashboards, etc., so they can plug AI agents directly into these data pipelines,” Ramamoorthy said. 

Industry leaders who have moved on to start their ventures have also gotten into AI agents. CP Gurnani, co-founder of AlonOS and former CEO of Tech Mahindra, recently spoke about how AI agents can make people more productive and efficient. “Agentic AI is the software version of a personal robot. Each one of us will have an AI agent that knows us really well,” he wrote on social media. 

AlonOS, co-founded by Gurnani and Rahul Bhatia, provides organisations with AI-as-a-service and data engineering solutions. The Singapore-headquartered AI startup recently partnered with Indosat, Indonesia’s telecom company, to accelerate AI sovereignty in their country. Though not many details have been revealed, AI agents will probably be implemented, considering that they are first catering to the travel and hospitality sector. 

Gaurav Aggarwal, who has industry experience working with NVIDIA and autonomous mobility, is now the founder and CEO of RagaAI – an AI testing platform. They recently released test frameworks for testing AI agents too. “We’re seeing AI agents evolve into so much more than just tools. They’re becoming collaborators, problem-solvers, and decision-makers,” Aggarwal said recently.  

Notably, leading database company Redis is supporting AI startups such as Kore AI in powering their virtual AI agents. “The Indian tech ecosystem is going to play a very critical role in agentic AI,” Manvinder Singh, VP of AI product management at Redis, told AIM. 

The post Why Indian Founders Love AI Agents appeared first on Analytics India Magazine.

Ashwin Raguraman’s Bharat Innovation Fund began to invest in AI in 2018, starting with traditional technologies like computer vision, voice recognition, and recommendation systems. It was only in 2021 that GenAI emerged as a game-changer driven by large language models. 

This shift helped freshly minted Indian startups like Sarvam AI and Krutrim develop localised solutions. And now, it is layering into middleware (security and observability) and applications leveraging traditional and generative AI. 

So, as AI startups stagger into 2025, let’s find out just how difficult it is to build an AI startup in India. If you have a great startup idea, a business plan, and a suitable location, you just need to tap into the funds and get started. Unfortunately, it’s not as simple as it sounds. 

Stanford’s 2024 AI Index Report ranked the nations that have witnessed the most growth in AI startup activity over the last decade. According to the report, the US and China ranked at the top, while India held the seventh position. 

India’s AI Funding Game

AIM earlier reported that it is now a prime time to build an AI startup in India due to funding opportunities and acquisition potential. According to AIM Research, 43 Indian AI startups received $864 million in funding as of August 2024. Among these, Ema, an enterprise AI startup, raised $36 million in Series A funding. 

Established players such as Uniphore and Gupshup are leading the pack with late-stage funding rounds, as per Tracxn data. Uniphore raised $400 million in Series E funding (January 2022) and $140 million in Series D (November 2020). 

Similarly, Gupshup secured $240 million in Series F funding (July 2021) and multiple $100 million rounds, showcasing sustained investor confidence in AI-driven solutions.

Emerging players like Sarvam AI and Krutrim are also making waves in the industry. Sarvam AI raised $41 million in Series A funding (December 2023), signalling strong early-stage support. Krutrim, a generative AI-focused startup, has attracted $50 million in Series B funding (January 2024) and $24 million in Series A (July 2023), demonstrating consistent growth and innovation in cutting-edge AI applications.

Institutional investors such as Tiger Global Management, Lightspeed Venture Partners, and Alpha Wave Global are leading the funding of Indian AI startups. These global and angel investors are enthusiastically backing AI innovations, highlighting the country’s growing prominence in the global AI landscape.

In terms of valuations, companies receiving higher funding amounts, such as Uniphore and Gupshup, have valuations ranging from $1 billion to $2.5 billion, contributing to India’s expanding unicorn ecosystem. This reflects the increasing confidence in the potential of Indian AI companies to create large-scale global impact.

The diversity of AI applications is another hallmark of the Indian startup ecosystem. While established companies focus on conversational AI solutions, emerging players are diving into generative AI and specialised areas like text-based chatbots. For example, Senseforth is carving a niche in enterprise chatbots, while Krutrim and Sarvam AI are pioneering generative AI platforms.

Funding for AI startups in India totalled $8.2 million in the April-June 2024 quarter. In contrast, AI startups in the US received $27 billion in the same period, representing nearly half of all startup funding in the country. 

The upside? Building products in India is far less costly than in the West.

Abhijeet Kumar, CEO of Tablesprint, underscores India’s unique cost advantage. “Building a solution like Salesforce would cost millions in the US, but in India, it’s a fraction. India is no longer just a service provider; we’re creating products that compete globally. For AI startups, now is the time to build in India, with talent, resources, and cost advantages all in place.”

Are Bengaluru’s AI Startups Tempted by the Bay Area? 

For some founders, India provides what’s needed to build impactful, cost-effective technology. Amritanshu Jain, co-founder and CEO of SimpliSmart, who returned from Silicon Valley, is even more convinced. “In India, we have a deep pool of tech talent. Many think Indian engineers leave for the US due to a lack of opportunities here, but that’s changing.”

Yet, Vedant Maheshwari, CEO of Vidyo.ai, believes India’s core challenge in AI lies elsewhere. “Foundational AI requires significant capital and patience, which is harder to secure in India. While funding here is substantial, it’s mostly application-focused rather than foundational,” he explained. 

“In the US, there’s more support for deep-level work, but in India, targeting specific AI applications allows us to leverage existing models without huge initial investments.”

Vishnu Ramesh of Subtl.ai, who has ties to both the Bay Area and India, sees it as a matter of investor confidence. “The Bay Area draws investors because of its track record. Once India has its ‘Google moment’, confidence here will rise.”

Also, most IITians prefer to move to the US for better opportunities. According to the US-based National Bureau of Economic Research, one-third of those graduating from the country’s engineering schools, particularly the IITs, live abroad.

Brendan Rogers, co-founder of 2am VC, shared on LinkedIn that most of these IITians are unicorn founders. 

The Hybrid Model

India excels in application development; however, GenAI demands fresh talent and innovation. While government initiatives like the National AI Mission foster upskilling, startups continue to struggle. 

As a result, many look abroad, especially to the US, which offers faster adoption cycles, larger contract sizes, and better ROI on AI products.

As there is an evident pattern of startups moving to the US, the country continues to be a key market for Indian AI startups. It also provides higher annual contract values (ACVs), making it attractive for startups seeking rapid growth. 

Many founders adopt a hybrid model – operations and talent in India with customer bases in the US – leveraging India’s cost advantage and the SaaS model to scale globally.

Raguraman said Indian AI startups are mostly focused on enterprises rather than consumers. “The enterprise market here is an excellent testbed due to discerning customers who demand rigorous product evaluations. However, the slower adoption rates and smaller ACVs compared to the US remain a challenge. This disparity often compels startups to focus on international markets for growth while maintaining a foothold in India,” he said.

What’s Next?

What venture capitalists look for while evaluating AI startups is, how much effort they have invested in their technology, what proprietary data they control, and the unique value they’re adding over existing models. 

This effort should be substantial, so they are confident it’s not just a surface-level improvement but something with real depth.

Indeed, India offers a more capital-efficient startup environment, whether for AI, or otherwise. However, the question is whether this will remain the case as these businesses scale globally. Once startups expand and start competing internationally, they will need to invest in talent from around the world, which could increase their expenses.

In terms of capital, India requires less funding for startups compared to the US, but there is also significantly less capital available here. While the Indian VC and startup ecosystem has grown substantially, it’s still relatively young and about 60 to 70 years behind the US ecosystem. 

The post The Struggles of Building an AI Startup in India appeared first on Analytics India Magazine.

Critical care in India faces a major crunch, with estimates suggesting only 2.3 ICU beds per 100,000 population. Furthermore, intensivists or critical care doctors are also in short supply, with only 5,000-6,000 trained professionals in our country. This shortage becomes a bigger threat in smaller towns and non-metro regions, leading to unfortunate, preventable deaths. 

A Bengaluru-based healthcare startup, Cloudphysician, aims to address this disparity with AI. 

ICU Care 2.0

Cloudphysician was founded in 2017 by Dileep Raman and Dhruv Joshi, two US board-certified intensivists who have witnessed the healthcare system in the West. They built the platform with a mission to use AI and telemedicine to bridge the skill and resource gap in India’s critical care infrastructure. 

By connecting ICUs through high-quality video and bedside data analytics, Cloudphysician looks to improve patient outcomes in both neonatal and adult critical care. 

“We have approximately 3.5 lakh ICU beds in the country. However, for a country our size, we need between 8 to 10 lakh ICU beds,” said Raman in an exclusive interaction with AIM. 

“It’s not that you put a bed and a ventilator and add some devices, and it becomes an ICU bed. Besides the must-have hardware and the infrastructure, you also need skilled people to run it and a set of processes that make the high-quality ICU function. That’s what makes an ICU bed,” explained Raman.  

Healthcare Operational AI

Cloudphysician uses a combination of multimodal AI models, incorporating inputs from video feeds, lab results, medical records, ambient audio, and established medical guidelines. This integrated approach allows the AI to detect critical issues, such as potential infections or tube disconnections, and provide actionable insights to doctors in real time. 

“So it is not about predicting who is going to get worse or better. It’s more about analysing what exactly is going on because that is what enhances the efficiency of the doctor with us,” explained Raman.

They use a combination of computer vision models for visual analysis and LLMs for reasoning and recommendations. Raman said they also leverage platforms like Google Cloud and OpenAI alongside their in-house models. 

The startup currently covers over 1,500 ICU beds across 200 hospitals in more than 100 cities throughout India and has even demonstrated a significant impact on reducing mortality by up to 47% in certain ICUs. 

A few months ago, the startup raised $10.5 million in a funding round led by PeakXV Partners, Elevar Equity and Panthera Peak. 

Humans in the Loop

The platform extensively employs AI, albeit as an augmenting tool. “The AI is not making any patient care decisions. It’s still the doctor and the nurse, but they’re doing it in a far more efficient manner now,” said Raman. If an ICU doctor can see 8-10 patients, Cloudphysician will be able to increase that by 6-8 times. 

Hands-on clinical training is a requirement for the startup, so half of its workforce consists of clinicians, doctors, and nurses who undergo intense training. Currently, the startup has a 280-member team, and HCG, Motherhood, and Cytecare cancer hospitals are some of its customers.

AI in Healthcare

A number of AI-based health tech startups have emerged recently, with the goal of addressing the critical staff shortage in patient care. The AI in healthcare market is expected to grow to $173.55 billion by 2029. 

Dozee, a Bengaluru-based startup, offers an AI-based contactless remote patient monitoring system. It tracks key metrics such as alert sensitivity, specificity, response time, and healthcare activity. The system is said to have the potential to save 21 lakh lives annually and reduce healthcare costs by INR 6,400 crore.

Cloudphysician has an ambitious vision to become the global engine for delivering healthcare, “Just like what happened in IT services decades ago,” remarked Raman. 

The post This Bengaluru AI Startup Claims to Cut ICU Mortality by 47% appeared first on Analytics India Magazine.

In India, cancer treatment often follows a standardised approach, bucketing the treatments based on the stage and type of cancer. However, the West adopts a rather nuanced strategy with genome research to understand the exact type of cancer within a certain stage and type and administer targeted therapy, thereby hitting a higher recovery rate. 

Hitesh Goswami and Kshitij Rishi founded the Bengaluru-based oncology precision startup 4baseCare in 2018 to introduce a similar approach to treating oncology patients in India. Recently, Infosys Innovation Fund invested close to $1 million in the leading precision oncology firm. They were also the past winners of the Karnataka government’s ‘Elevate’ initiative that supports innovative early-stage startups. 

A few months ago, deep tech-focused venture fund Yali Capital led a Series A funding round and raised $6 million.

Genome Technology for Targeted Care

In an exclusive interaction with AIM, co-founder and CEO of 4baseCare, Goswami, explained the methodology behind cancer treatment. Previously, cancer treatment was uniform. All patients received the same therapy based on the cancer type and stage, and individual differences were not considered in treatment plans.

Goswami said that today, lung cancer stage-2 and stage-3 patients are subgrouped into 12 to 15 categories; each requiring distinct treatments as therapies effective for one group may not work for another and could even cause adverse reactions.

According to him, sequencing one human genome took around 15 years and $3.2 billion. Now that the Human Genome Project has concluded, Goswami believes the procedure can be done at a cost of $100 for a genome in a couple of years.

Notably, genome testing for oncology has grown rapidly in India. What stood at 5,000-6,000 tests in a year in 2019 has now touched 2 lakh. Goswami estimates that the market will easily hit 3 lakh tests.  

India has its Own Struggles

While the results help in targeted care for cancer patients, precision oncology in India faces key challenges, including limited awareness among oncologists in tier-2 and tier-3 cities, who often rely on traditional chemotherapy. Many patients are also unaware of advanced targeted therapies. 

Further, when effective drugs are identified, they are often unavailable in India. This becomes a cause for frustration among both patients and doctors. Affordability also remains a key challenge as treatments like immunotherapy cost ₹2.5-₹3 lakh per cycle and require multiple rounds. 

“Although many pharma companies are running a lot of patient support programs where they give free access to a certain extent to some drugs. But overall…it is very expensive,” Goswami pointed out. 

With increased awareness and reduced costs, the method is expected to become more accessible and be adopted in the coming years. 

Analysis of Gene Fusions across functional categories and cancer types using 4baseCare’s study. Source: 4baseCare

AI at Play 

4baseCare has been adopting AI for a number of its use cases, including data interpretation, where it uses AI models to get the right insights from the huge amount of data it generates. 

“So, right now, we are telling patients and doctors that you can look at your applications and see what you can work on. The next level that we are working on is directly giving the recommendation, best recommendation, best three recommendations in terms of application,” Goswami explained. 

The startup has also received a grant from the Indian government to develop 3D cell models of tumour tissues in a lab setting. These models are used to test the effects of various drugs on the tumour, and the data collected from these experiments is being used to train an AI model to predict which treatments are most effective for specific types of patients.

In addition to this, the startup is working on a novel concept referred to as the CG Twin, which stands for clinical genomic digital twin of cancer patients. The method involves integrating a patient’s clinical and genomic data into a comprehensive profile. This system will allow doctors to compare a new patient’s profile with a database of similar patients, known as twins, and by analysing these matches, doctors can access insights about similar patients’ treatments, outcomes, and risk factors, thereby enabling a more personalised and informed treatment plan based on real-world data and previous cases. 

“We have done close to 15,000 plus tests now, and we have actually developed a machine learning algorithm, which has been trained in 15,000 patients to build this twin model,” said Goswami, who looks to build a future which will be more evidence-based and outcome-based decisions rather than an empirical way of providing treatment. 

CG Twin is already being tested by doctors and is slated to be released in the upcoming months. 

Notably, the concept of digital twins in healthcare is gaining traction. Several healthcare platforms have used NVIDIA’s Omniverse to build a simulated environment of patients to help understand and administer more efficient treatment. 

The Mission Continues Amidst Struggles

Goswami explained that the unique name of the startup is inspired by the four bases of DNA – adenine (A), cytosine (C), guanine (G), and thymine (T) – and the four pillars of cancer care – allied care, technology, global genomic research, and clinical care.

“What we realised is that these four bases or these four pillars are working in silos. So, we wanted as a company to bring all these four bases together just like the four bases of DNA to provide cancer care,” he said.

Notably, 4baseCare has partnered with well-known hospital chains such as Apollo Hospitals, Fortis Healthcare and Tata Memorial Centre.

The journey has not been an easy one, with the startup having faced a fair amount of funding struggles. “When you’re talking about genomics, genomics-driven data and all, there was a lot of apprehension because genomics is something new. And, it is something that’s not easy for everyone to understand.” 

Goswami even recounted how they had to withstand “a hundred noes” before one “yes”, something that is a common reality for many founders in deep tech. However, eventually, things have a way of falling into place. “You need that one yes from the right people to believe in your vision, and I think that’s what we got with Yali and with Infosys.”

Above all, the biggest motivation for Goswami and his team, which has close to 200 members, comes from the profound impact their work has on cancer patients.

Goswami believes that seeing patients, once suffering from salivary gland cancer, cancer-free because of their recommendations is what keeps them motivated.

“The whole team, right from the logistics team who picks up a sample, I tell them, ‘Guys, you have no idea which sample you might pick up, and that will change the whole family’s life. So right from every level, someone is somehow impacting a family which they don’t even know. So that has kept us going, and…it’s a very exciting journey,” Goswami concluded. 

The post This Infosys-backed Bengaluru Startup is Beating Cancer with Genomics & AI appeared first on Analytics India Magazine.

Meta AI chief Yann LeCun, in a recent interview with Zerodha co-founder Nikhil Kamath, advised budding AI entrepreneurs in India to pursue an academic degree, such as a master’s or PhD, particularly in technical and complex fields like artificial intelligence, before building a startup.

“Doing a PhD or graduate studies trains you to invent new things and ensures that your methodology prevents you from fooling yourself into thinking you’re being an innovator when you’re not,” he said.

LeCun added that while a PhD is not a strict requirement for success, it offers significant advantages for entrepreneurs. “It gives you a different perspective,” he said. “In a complex, deeply technical area like AI, it’s useful to learn about what exists out there, what’s possible, and what’s not.”

His comment resonated with many founders. “If you are building a deep-tech startup, which is more than just a GPT wrapper, you will need technical people in the core AI team,” said Pijush Bhuyan, computer vision engineer at Awiros. He added that companies need people who have got their hands dirty with PyTorch, and who have spent hours implementing state-of-the-art research papers, not prompt engineers or people hailing from an SDE background without prior experience.

Amit Sheth, the chair and founding director of the Artificial Intelligence Institute at the University of South Carolina (AIISC), agreed with LeCun, saying that this was definitely true for deep-tech startups. “Several of my PhDs have started successful startups (and I have done four). Of course, there are plenty of cases where founders did not get advanced degrees but have succeeded, so this is unnecessary, but in general, those with deeper experience in technical innovation have an edge,” he said.

Startups founded by Sheth’s students include AppZen, thunk.ai, Objective, Inc., and Clinical AI Assistance.

Rajan Anandan, managing director at Peak XV Partners (formerly Sequoia Capital India), believes the Indian startup ecosystem would benefit from more founders with deep expertise in artificial intelligence and software development. According to a research from Private Circle, only 8% of newly launched AI startups have founders with a PhD degree.

A 2017 study by the National Bureau of Economic Research showed that startups with at least one PhD founder are more likely to succeed and have higher valuations at IPO. The research found that having a PhD founder increases a startup’s chances of a successful exit by over 50%.

Vishnu Vardhan, the founder of Vizzhy and SML, aptly described the sad state of Indian AI startups in an exclusive interview with AIM, stating, “Indian startups often focus on business applications instead of foundational innovation, with investors prioritising quick returns over long-term deep-tech investments.”

He said that the so-called deep tech investors clearly have no theses whatsoever. “I met a few VCs who said they were deep tech investors. I asked them their ticket size; they don’t even understand the scale of investment required for true deep tech.”

Exceptions Galore 

Elon Musk, the man behind companies like Tesla and SpaceX, never pursued a PhD or master’s degree. Despite this, he successfully built and led some of the most innovative companies in the world.

Interestingly, earlier this year, LeCun engaged in a banter with Musk, who questioned the former’s contribution to AI by asking how much research he had conducted “in the last five years.” Pat came LeCun’s reply: “Over 80 technical papers published since January 2022.”

“SpaceX would not exist without the thousands of scientific papers on rocket engine design, propellant chemistry, rocket control, material science, orbital mechanics, heat dissipation, trajectory planning, and the hundreds of scientists who got where they are by studying these papers,” claimed LeCun.

Deep Tech Startups in India 

Similarly, Vishnu Ramesh, founder of Subtl.ai, couldn’t agree more. “We have come so far with Subtl.ai thanks to people like Manish and Pranav coming in as my partners. Super excited to disrupt the custom SLM RAG space!” he said.

Manish Shrivastava, co-founder and chief scientist at Subtl.ai, has a PhD in computer science from IIT Bombay. Similarly, CTO Pranav Goyal has a dual degree, a BTech in computer science and an MS in computational linguistics from the International Institute of Information Technology, Hyderabad (IIITH).

Notably, Sarvam AI, one of the most popular AI startups in India, was founded by Vivek Raghavan, who has a PhD in electrical and computer engineering from Carnegie Mellon University. His co-founder, Pratyush Kumar, also holds a PhD from ETH Zurich.

Similarly, Pranav Mistry, founder of TWO AI, completed his BE in computer science from Gujarat University, followed by an MDes in Design from IIT Bombay. Ritwika Chowdhury, the founder of Unscript, holds an MTech in electronics and electrical communication engineering from IIT Kharagpur.

Rise of AI Startups by Researchers

In the West, many AI researchers have recently founded their own AI startups. François Chollet, the creator of Keras, recently announced his departure from Google. Not long after, Toby Shevlane, a scientist at Google DeepMind, also revealed that he was leaving the company to pursue his own venture.

Nearly all researchers who co-authored Google’s Transformers paper ‘Attention Is All You Need’, which shaped the foundation of modern AI, later left the organisation to start their own companies to tackle specialised niches within AI.  

“…At that time GPT-2 had just come out and the trajectory of the technology was pretty clear…So I called up my co-founders and I said ‘Maybe we should figure out how to build these things,’” said Cohere CEO Aidan Gomez in a recent podcast, elaborating on the need to capitalise on the wave of future internet models. 

Fei-Fei Li, co-director of Stanford’s Human-Centered AI Institute, co-founded World Labs in 2024. The institute, valued at over $1 billion, aims to develop AI systems with advanced spatial intelligence for 3D interaction. 

Similarly, Ilya Sutskever, co-creator of ChatGPT, shifted his focus to AI safety by founding Safe Superintelligence, reportedly valued at $5 billion. Meanwhile, in March, computer scientist Kai-Fu Lee founded 01.AI, valued at $1 billion, to develop open-source LLMs specific to China.

The post The Secret to Building a Successful AI Startup in India Might Be a PhD appeared first on Analytics India Magazine.

No-code platforms are not dead yet. They are very much alive with the help of AI. Bengaluru-based Tablesprint aims to stand out as an AI-powered low-code platform, designed to bring enterprise-level applications to companies of all sizes. 

Following the recent $1 million seed funding round led by Ola’s co-founder Ankit Bhati and other notable investors, Tablesprint co-founder and CEO Abhijeet Kumar discussed with AIM the company’s vision, its distinct position in the market, and its potential to transform business operations globally, competing with the likes of Salesforce.

Kumar’s background includes founding and scaling a successful startup that eventually became BigBasket’s backbone. His experience in handling operations-heavy systems provided the foundation for Tablesprint’s solutions. “BigBasket is an enormous operation, especially from an operational and logistics perspective,” Kumar noted. 

“It’s a consumer tech business that relies heavily on robust technology. That experience equipped us with the knowledge to tackle complex tech challenges, which now fuels Tablesprint’s mission.”

Another Low-code/No-code?

While many platforms provide low-code options, Tablesprint aims to address a crucial gap in enterprise technology: accessibility and control over data. Traditional low-code platforms often offer limited control over sensitive information due to rigid structures and limited permissions. 

Tablesprint’s platform addresses this by incorporating robust data controls, permissions, and a secure backend infrastructure that integrates seamlessly with AI, ensuring that businesses can safely and effectively leverage their data. Rather than attempting to build AI models from scratch, Tablesprint focuses on integrating and optimising existing open-source and commercial models to provide a comprehensive, end-to-end solution that enterprises can trust.

The moat of Tablesprint? Unique blend of affordability and flexibility, positioning it as an alternative to established players like Salesforce and Zoho. “We’re offering the flexibility of Salesforce but at a Zoho price,” Kumar explained. “Our focus is on creating a customisable, scalable system that can handle complex enterprise needs without the high costs usually associated with such solutions.”

Tablesprint’s competitive advantage, Kumar emphasised, lies in its capacity to offer a Salesforce-like experience at a fraction of the cost, with a user interface (UI) that simplifies complex tasks. “With us, you’re not just getting a software tool; you’re gaining a system that adapts to your needs, allows for high-level customisations, and integrates AI capabilities directly into the workflow.”

Chirag Jadhav, co-founder and CTO, earlier highlighted the platform’s multi-tenant system designed for both developers and business users. “Creating a system that resonates with both developers and business stakeholders is a significant challenge. We are excited with our progress so far and look forward to tackling more challenges ahead on this journey.”

Founded earlier this year, Tablesprint’s AI-first SaaS platform allows companies to rapidly build customisable applications for various business functions, including HR, sales, operations, and vendor management, using open-source and closed-source models available in the market. 

Its no-code solution features modular building blocks such as AI write/image tools, workflows, and charts, enabling businesses to start with simple tasks like surveys and scale to full-fledged workflows.

Staying Alive and Sprinting Ahead

Kumar is aware of the competition from established giants like Salesforce and Zoho, but he believes Tablesprint offers unique advantages that make it attractive, particularly for businesses conscious of both costs and customisation capabilities. 

“Try making a form in Zoho, and you’ll see it’s not designed with seamless user experience in mind,” he said. “Our UI is optimised to make processes intuitive and efficient, making it appealing to users who need functionality without the overhead.”

In addition, Kumar highlighted the often limited adoption rate of Salesforce due to its complexity and high price point, particularly in markets like India, where cost sensitivity is paramount. According to him, only around 34% of Salesforce licences end up being used because companies struggle with its complexity. “We’re targeting that space by offering a similarly robust platform that’s also simpler to use and much more affordable.”

The company’s go-to-market strategy involves partnering with implementation providers, giving entrepreneurs and developers the flexibility to use Tablesprint to build apps, which they can then offer to clients as solutions. “Tablesprint is not just an India-specific product; it’s a global solution designed to help businesses worldwide streamline operations, build apps, and generate revenue by offering our platform as a customisable solution,” Kumar explained.

Tablesprint’s roadmap is ambitious. The company plans to expand its offerings by building a complete AI-based ERP system and form builder, with the goal of becoming a one-stop shop for enterprise applications across industries. 

Despite the increasing saturation in the low-code, no-code market, Kumar is confident that Tablesprint’s model and global reach will help the company thrive in the long term.

“We’re still a startup, so bandwidth is sometimes an issue,” he admitted. “We have so much interest that we have to pick and choose our clients carefully because we want to ensure each implementation is successful and adds value.”

The post This Bengaluru Startup is Building a Cheaper Alternative to Salesforce and Zoho appeared first on Analytics India Magazine.

Floworks, the cloud-based enterprise automation startup, recently released a novel ThorV2 architecture allowing LLMs to perform functions with better accuracy and reliability. The YC-backed company collaborated with IIT Bombay and Kharagpur to build this architecture. 

In an interview with AIM earlier this year, Floworks claimed that its AI agent, Alisha, is 100% reliable for tasks involving API calls. Sudipta Biswas, the co-founder of Floworks, said, “Our model, which we are internally calling ThorV2, is the most accurate and the most reliable model out there in the world when it comes to using external tools right now.”

Further, he claimed that ThorV2 was 36% more accurate than OpenAI’s GPT-4o, 4x cheaper, and almost 30% faster in terms of latency.

These sensational claims were recently backed by an in-depth research paper that dives deep into ThorV2 architecture and how all of its novel features work to solve several crucial challenges in agentic workflows inside some of the market-leading LLMs today. 

Edge of Domain Modeling is ThorV2’s Hero Technique

The edge of domain modelling, used in ThorV2 architecture, involves providing minimal instruction upfront, allowing the agent to begin the task, and then providing the remaining information through error corrections post-task.

This approach differs from providing knowledge of all possible scenarios regarding the function calling. 

Edge of domain modelling reduces the need for extensive instructions, which in turn reduces the number of tokens in the prompt. It can further lead to cost saving measures. 

The authors mentioned that “Function schemas can be lengthy, leading to large prompt sizes. This increases deployment costs, time consumption, and can result in decreased accuracy on reasoning tasks.” 

Additional instructions added to the LLM in the error correction process are performed by a static agent implemented through an Agent Validator Architecture inside ThorV2. 

You Don’t Need an LLM to Evaluate Another LLM 

The Agent Validator architecture overcomes several limitations of agentic workflows, where the primary LLM agent performing a task receives feedback from other LLMs that act as critics.

The authors argue that using an additional LLM not just increases the deployment costs, but decreases the rates of accuracy. 

The introduction of a static agent written fully in code, includes a component called Domain Expert Validator (DEV) which inspects the output generated by the LLM for errors. The DEV contains all the knowledge and information required to perform the function calls inside a specific platform.

While building a validator requires a significant amount of effort, it helps reduce processing time and improve accuracy. This is because the knowledge contained inside the DEV contains information regarding the most common and repetitive errors that occur in the function calling process. 

Multiple API Functions in a Single-Step 

One of ThorV2’s other advantages is that it can generate multiple API calls in a single step. With ThorV2, a single query is sufficient for both tasks, even if the first task needs to retrieve information from the API for use in the second task.

The approach involves using a placeholder to represent unknown values, and once the first task retrieves the API response, the value is then injected into the second task.

“Generating multiple API calls at once requires sophisticated planning and reasoning capabilities, which is very challenging for ordinary LLMs. Our Agent-Validator architecture simplifies this process as well by correcting errors in the planning step”, added the researchers. 

This approach is a significant improvement over the traditional, sequential handling of API calls in current LLMs, which often require a step-by-step execution process.

And the Numbers Don’t Lie – 50% Cost Reduction With 100% Reliability

The ThorV2 architecture was compared to OpenAI’s GPT 4o, GPT 4 Turbo, and Claude 3 Opus for a set of operations on HubSpot’s CRM.

The authors developed a dataset called HubBench on which the model was evaluated. The models were tested for accuracy, reliability, speed, and cost. In a conversation with AIM, Sudipta mentioned that ThorV2 was connected to the Llama 3 70B model for comparison. 

ThorV2 came out on top in every single test, and a 100% score in the reliability test, which seeks a consistent output when the model is put out to perform the task ten times.

In the single API call function, ThorV2 scored 90% accuracy, second to Claude 3 Opus’ 78% score.

The test also revealed that it only took $1.6 for a thousand queries, which is 3 times cheaper than OpenAI’s models. Even with multiple API calls, ThorV2 performed better on every single metric. 

While reading the comparison benchmark scores, one wonders if these scores are relevant five months after the tests were conducted, with several new and capable models like Claude 3.5 Sonnet and GPT o1 having been launched.

However, it is important to understand that ThorV2 is an architecture built to enhance the performance and capabilities of an existing LLM. The integration will, in fact, work better with new and more capable models. 

“We will soon come up with Thor v3, which will definitely compare with other models that have come up recently. But again, the framework is not a model-level innovation that we’re doing,” Sudipta said.  “So even if the underlying model keeps on getting better, our framework will keep supporting even better than that.” 

It Isn’t Perfect, But Floworks Wants to Get There 

One of ThorV2’s limitations is that it relies on knowledge from the DEV based on common, and well established error patterns and it may face difficulties approaching an unseen one. Moreover, the research currently tests ThorV2’s architecture for just single, and two API call functions.

The authors acknowledge the limitations, and plan to perform a comparison with three or more function calls in future research. 

In the conversation with AIM, Sudipta revealed that ThorV3 is currently in the works, and it will challenge some of the latest market leading models today. That said, one can also expect other limitations to be resolved in the future iteration. 

A Vision to Solve More Real-World Problems

The authors envision ThorV2 to overcome the limitations of existing LLMs and solve problems that can truly create an impact. 

They mentioned that LLMs have revolutionised NLP and AI, demonstrating remarkable capabilities across a wide range of tasks. However, their economic impact has been somewhat limited, particularly in domains requiring precise interaction with external tools and APIs.

Over the last few months, we’ve also seen a meteoric rise in AI Agents and their tremendous capabilities, and frameworks like ThorV2 can only propel their powers further in sectors that require a large amount of automation and knowledge transfer between different applications. 

“LLMs seem very cool, but to front-load them with a high amount of tokens, the cost will be prohibitively, very high. For large-scale operations where lots of automation is needed to be done, that price point will not suit enterprises, and small businesses,” Sudipta said. 

The post Stop Paying for GPT-4o—This YC Startup Offers 4x the Savings appeared first on Analytics India Magazine.

With a mission to build AI employees, Bengaluru-based generative AI startup Alchemyst AI has raised $300K in its pre-seed round from key investors, including Inflection Point Ventures, 100 Unicorns (formerly 9U), and EarlySeed Ventures.

“We want to build an entire ecosystem of generative AI based digital employees, backed by our own AI infrastructure,” said Uttaran Nayak, the founder of Alchemyst AI, in an exclusive interview with AIM.

“We want to create a stagnant and simple ecosystem where these GenAI employees will work as hyper-intelligent AI assistants for humans and interact with each other to solve high-level intelligent problems in these enterprises,” added Nayak. 

Inside Alchemyst’s AI Infrastructure

Nayak told AIM that they have created their own model, AlchemystC1, by combining Phi-3 vision and an Indic LLM from AI4Bharat. He said that they are not using any of the popular models like OpenAI’s GPT-4o or Google Gemini due to concerns from enterprises about their data being exposed on the web. 

“In the next three to four months, we are planning to extend into 28 languages, particularly to cater to Indian customers,” he said.

Regarding the issue of hallucinations, he said, “We have fine-tuned our model to say ‘I don’t know’ rather than producing gibberish. This requires more data, as maximum reliability in instruction-following is essential from any employee across an organisation.”

Alchemyst’s chief technology officer, Anuran Roy, explained that the company has developed a dedicated mesh architecture solely for storing and auditing data over time. 

Is Employer’s Data Safe with AI Employees? 

Roy said that AlchemystC1 does not have direct access to employees’ emails. “We have stored the data in a separate mesh,” he said and explained that customers can define scopes, restricting LLMs from accessing certain data.

“We also have a BYOM (bring your own model), but AlchemystC1 guarantees that no data prompts leave the company. Everything is secured in compliance with GDPR, HIPAA, SOC, and other strict standards,” he added. 

The company currently utilises multiple cloud service providers, including AWS, Microsoft Azure and GCP, for LLM inference. 

“We use a self-hosted database for our needs. For LLM inference, we host containers on AWS and bare metal providers. As we move forward, our goal is to consolidate our infrastructure to better manage costs while scaling over time,” shared Roy.  

The startup is in talks with E2E Networks to acquire more GPUs for LLM inference.

Digital Employees to the Rescue

Founded by school friends Nayak and Anuran Roy, Alchemyst AI was officially launched around the end of November 2023. The team consists of six members, all technical experts.

Nayak said that he worked with a B2B SaaS company during his third year of engineering when the idea came to him. “That was the first time I entered a whole new world of SaaS and realised that if you’re building a product for $500, you can sell it for $50,000 to all those clients,” he said.

Alchemyst’s AI digital employees currently cater to various sectors, including sales, marketing, HR, customer support, and analytics. Nayak told AIM that these digital employees will assist in lead generation, email management, and meeting scheduling, among other tasks, thereby optimising overall productivity. 

The company’s flagship product, Maya, is an AI sales development representative (SDR) that automates enterprise sales processes. Maya can research prospects and compose hyper-personalised messages without human intervention, performing 50 times faster than humans.

The company plans to introduce more AI employees in the near future. Currently, Moh serves as the AI marketer, Leela is the AI HR manager, Ron is the AI customer support representative, and Sam is an AI analytics specialist.

“We want these AI employees to interact with each other and solve problems within the enterprise. They don’t need to shift their entire architecture or infrastructure; they just have to adopt a plug-and-play concept,” explained Nayak. 

These AI employees can be integrated into Hubspot, Gmail, Jira, Salesforce, WhatsApp, Telegram, Zoho and ServiceNow. “We will be integrating with WhatsApp and Telegram very soon. These integrations will happen quickly, especially for sales activities on these platforms. This is particularly relevant in the Web3 space, where there are numerous use cases,” said Nayak. 

Notably, Salesforce recently launched Agentforce Partner Network that brings together tech giants such as AWS, Google Cloud, IBM, and Workday to enhance the AI-powered Agent Force platform’s capabilities.

Meanwhile, OpenAI has introduced a new approach for creating and deploying multi-agent AI systems, called the Swarm framework. It simplifies the process of creating and managing multiple AI agents that can work together seamlessly to accomplish complex tasks.

Similar to Ema? 

The company’s solution is similar to what former Coinbase CPO Surojit Chatterjee’s Ema is building. Recently, Ema launched the ‘Universal AI Employee’, which can take on various roles within an enterprise. The company also raised an additional $36 million in Series A funding, bringing its total capital raised to over $61 million.

The company’s Persona Builder Platform allows businesses to create and deploy custom AI Personas tailored for specific roles without extensive model training. This new tool is expected to enhance how companies integrate AI into various enterprise functions including customer support, sales, and compliance.

“Ema is essentially building what we are—one universal AI employee for enterprise teams, complete with its own AI architecture,” said Nayak, acknowledging their presence in the agentic AI market.  

Building $200 Mn Company in the Next Three Years 

Nayak further shared that he recently met with Apoorva Ranjan Sharma, the co-founder and director of 9Unicorns and Venture Catalyst. “He showed me the entire trajectory and roadmap of how the companies go from there. We are looking to build a $200 million company in the next three years,” he said. Moreover, the company plans to expand into the US, UAE, and Southeast Asia. 

The post This Bengaluru Startup Secures $300K to Build AI Employees for Enterprises  appeared first on Analytics India Magazine.

While tools like Anthropic Claude, Cursor, Zed, Microsoft Copilot, and Replit Agents have been designed to automate coding and software development, only a few address the code after it has been generated. This is where YC-backed Patched comes in.

Founded in 2023 by Asankhaya Sharma and Rohan Sood, Patched is an open-source AI framework that automates repetitive tasks like code reviews, documentation, and patches. Besides, it also tackles maintenance tasks such as vulnerability fixing and linting, all while running autonomously without active developer involvement.

It also allows developers to create custom workflows to fit their specific needs and preferences. 

“We’re not focused on the IDE. I think a lot of people are trying out various things around co-pilots and IDE assistants, such as Cursor AI. Our focus is to automate other tasks – that may be non-coding related or not best done in the IDE – like documentation generation and pull request reviews,” Sharma told AIM.

He added that their focus has always been on the outer loop, which is everything after one commits the code and it goes into the CI/CD pipeline. “We are looking at all the places in that pipeline where you can attach an LLM or improve processes using GenAI, and so on.”

Sharma said the company uses LLMs to generate code, review changes, and manage workflow tasks, while integrating with users’ preferred LLM APIs for enhanced privacy and customisation. 

“We are an LLM-agnostic, open-source framework. You can use any LLM, whether from API providers like OpenAI and Google Gemini or your own locally-hosted model,” said Sharma.

He added that they open-sourced Patched because they do not want developers to pay separate fees for fixing bugs, reviewing vulnerabilities and generating test cases. 

Breaking Down the Business Model 

Apart from the open-source framework, the company also offers an app. Patched’s app features a drag-and-drop workflow builder, eliminating the need for writing Python code. 

“We are monetising through the API, which provides the user experience along with the drag-and-drop ease of building and customisation,” said Sharma. 

The company charges $99 per workflow. Unlike other players, Patched did not price the product on a per-developer basis as Sharma feels that it discourages people from using it.

Moreover, Sharma said that they created a benchmark last year to evaluate the performance of different frontier models on vulnerability fixing, called the Static Analysis Eval.

He explained that for other tasks, which may not be as easily quantifiable, such as pull request reviews or documentation generation, they look at other metrics like RTC Eval, which stands for round-trip correctness. 

In simple terms, they run the model a few times to ensure the results are consistent and don’t change over time.

He further explained that they do not like to compete with or compare themselves to tools like Devin, though he acknowledges that there is room for such comparisons.

Patched’s Growing Clientele 

One of Patched AI’s clients is KairosWealth in Singapore. Sharma explained that their requirement was for developers to follow a specific set of instructions when committing code. 

“They had a document with a list of instructions, so the idea was to take that document and convert it into something that can be integrated into a patch flow,” said Sharma.

He said that the company designed two new patch flows for them. The first one generates a style guide autonomously from a given repository. 

“It reviews all your existing pull requests that have been closed in the past. Based on the comments and changes in those pull requests, it automatically infers the style guide you’re following in your code. This style guide is then used as a reference for reviewing subsequent pull requests,” said Sharma.

“Two, to actually use that to review pull requests, and three, to actually fix the issues that are found using the pull request,” he added.

Another customer, Stack Auth, an open-source alternative for cloud key and authentication providers, had a workflow built by Patched using TS Morph combined with LLMs to generate SDK documentation.

He further explained that their tool is more suited for experienced developers rather than those who are new to coding and experimenting with generative AI tools. “If you’re an experienced developer, you are responsible for maintaining code quality, reviewing pull requests, or generating documentation,” he said.

He said that if one has experience in coding for large enterprises, the initial setup is not where most of the time is spent. 

“Eighty percent of the time is spent making small changes to large codebases, which are deployed to thousands or millions of users, and ensuring these small changes are implemented in a way that prevents things from breaking,” he added.

Sharma said that initially it was difficult to convince consumers that LLMs could be effective in discovering code vulnerabilities. He explained that 80% of the vulnerabilities people encounter daily are not particularly unique; they are often recurring mistakes that people make.

YC Opens the Door to GPT-5 

Sharma said that in the near future, they will add a new feature to their app, allowing users to save all pull requests and responses to curate their own datasets, which can later be fine-tuned for better results.

Moreover, the company is working closely with OpenAI, which Sharma says wouldn’t have been possible without support from Y Combinator. 

He revealed that GPT-5 is around the corner and that people from OpenAI, including CEO Sam Altman, have advised them to ensure that their product remains relevant when GPT-5 is released and to integrate the next-generation model in a way that makes tasks easier.

The post This YC-Backed Startup Automates Code Reviews and Documentation for Enterprises appeared first on Analytics India Magazine.

With so many companies developing automation tools and AI agents, it has become increasingly challenging to identify the differentiator or the moat of your company. 

Three industry veterans—Sidu Ponnappa, Aakash Dharmadhikari, and Steve Sule—found themselves at the crossroads. Originally setting out to build an IT services company, their journey took an unexpected turn with the arrival of ChatGPT in November 2022, which would redefine their approach to business and ultimately lead to the founding of realfast.ai.

Backed by PeakXV, RTP Global and DeVC, the leading project of realfast.ai is its Vayu platform, which emerged from the team’s real-world experience. The team believes that the only way to develop a meaningful AI assistant is on top of real work—commercial work that customers are willing to pay for because it has value. 

“Real work is messy, with many variables, and it’s a completely different dynamic from what you might expect in a lab environment,” Dharmadhikari told AIM.

The Vayu platform currently integrates deeply with the Salesforce developer platform, giving AI agents access to the same tools that human developers use but through a different interface. This approach allows AI to assist in tasks that traditionally require human intuition and experience.

One of the most innovative aspects of realfast’s approach lies in the way they train the AI agents. Unlike traditional AI models that rely on vast amounts of static data, realfast.ai’s models learn from observing human developers at work. 

“We realised that building an applied AI product is like teaching a child,” Ponnappa explained. “You need to break down your own thinking and create exercises that teach the AI the principles you’ve learned over years of experience.”

This method has led to the development of AI agents capable of handling over 700 specific tasks related to unit testing, all based on how human developers approach these tasks. “It’s a combination of natural language processing, fine-tuning, and generating large quantities of data,” Sule added.

The Moat

realfast.ai’s first major success came when they started working with design partners to implement their AI-driven processes. “We’ve been in production with a hybrid team—human developers assisted by AI agents—on tasks like unit testing,” Dharmadhikari shared. “We’ve seen up to a 3x speed-up in these processes.”

Sidu Ponnappa

As Realfast.ai continues to grow, the founders remain focused on their mission to revolutionise the IT services industry through AI. “The largest challenge we face is that there’s no existing data for this type of work,” Ponnappa noted. “The process and craft by which a finished product is created aren’t tracked or recorded anywhere because there’s been no reason to do so until now.”

With an example, the founders illustrated that the company was able to streamline 3,000 lines of legacy code for a company, which is very dirty and messy. This wouldn’t have been possible with just human engineers as it’s a tedious task. 

Currently, the platform uses models like ChatGPT and Anthropic’s Claude, but does not rely on open-source models as they believe that their reasoning capabilities are nowhere close to the proprietary ones. Moreover, unlike everyone else making open-source AI agents, the realfast.ai platform is SOC 2 and ISO compliant.

“We think of this platform as an Iron Man suit for the IT guys,” Ponnappa joked, noting that this is how they often pitch it to the investors. 

The Pivot They Needed

In the early days, Dharmadhikari and Ponnappa, both with extensive experience in services, especially from their time at Infosys and ThoughtWorks, respectively, decided to start a boutique IT company focusing on complex systems integration, “a space we believed had significant untapped potential”.

Sule had been an advisor for the team long before he came on board as a co-founder. 

This plan, however, took a drastic turn with the release of ChatGPT. The founders, already bullish on IT services, quickly recognised the transformative potential of AI. “Our strategy was always tool-oriented because value to customers comes from reliable delivery,” Ponnappa explained. 

“When ChatGPT dropped, it was unlike any tool we had ever seen. It wasn’t just a 10% improvement here or there; it was driving unprecedented changes across the entire lifecycle, from sales to coding.”

Pivoting to this field, as they continued to integrate ChatGPT into their processes, the founders quickly realised the potential of AI to revolutionise the IT services sector. 

“We started to see that the way IT services will look in five years post-AI adoption will be completely different from today,” Ponnappa noted. “It’s a platform problem. You need a platform where AI tools, assistants, and humans can work together seamlessly.”

This insight became the foundation of realfast.ai, a company dedicated to building an AI transformation platform that could support the unique needs of IT services companies. The goal was not just to enhance existing processes but to fundamentally change how services are delivered.

The post This AI Startup is an Iron Man Suit for the IT Guys appeared first on Analytics India Magazine.

It’s now widely recognised that selling AI models is a zero-margin game. The next wave of AI startups must capitalise on LLMs in the application layer to tackle real-world challenges.

“The next billion dollar startups in AI will play on the application layer and not the infrastructure layer,” said AIM Media House chief Bhasker Gupta in a LinkedIn post. 

Gupta added that there is a plethora of problems to be solved using AI, and these startups will localise their solutions while maintaining a broad-based approach.

Echoing a similar market sentiment was Nayan Goswami, the founder and CEO of Chop. “The next major wave of AI innovation will focus on the application layer, where startups will build specialised vertical AI software-as-a-service (SaaS) companies for global markets,” he said. 

Goswami further elaborated that with robust foundational models like Anthropic, Cohere, and OpenAI, along with infrastructure companies like LangChain and Hugging Face advancing rapidly, we’re poised to witness a surge in application-layer startups targeting specific verticals. 

“Think of foundational models as the roadways, and application layers as the vehicles driving on them,” he explained. 

Finding the Right Application to Build is Key 

Andrew Ng, the founder of DeepLearning.AI believes AI’s usefulness in a wide variety of applications creates many opportunities for entrepreneurship. However, he advised budding entrepreneurs to be extremely specific about their ideas for integrating AI. 

For instance, he explained that building AI for livestock is vague, but if you propose using facial recognition to identify individual cows and monitor their movement on a farm, it’s specific enough.  

A skilled engineer can then quickly decide on the right tools, such as which algorithm to use first or what camera resolution to pick.

In a recent interview, Ng explained that the cost of developing a foundation model could be $100 million or more. However, the applications layer, which receives less media coverage, is likely to be even more valuable in terms of revenue generation than the foundation layer.

He also said that unlike foundation models, the ROI on the application layer is higher. “For the application layer, it’s very clear. I think it’s totally worth it, partly because it’s so capital efficient—it doesn’t cost much to build valuable applications. And I’m seeing revenues pick up. So at the application layer, I’m not worried,” he said.

Perplexity AI serves as a strong example by integrating search with LLMs. Rather than building its own foundational models, the startup leverages state-of-the-art models from across the industry, focusing on delivering optimal performance. The company is planning to run ads as well from the next quarter onwards. 

However, not everyone is going to make the cut; some startups are going to fail. Statistically speaking, around 90% of startups don’t survive long enough to see the light at the end of the tunnel.

Ashish Kacholia, the founder and managing director of Lucky Investment Managers, said, “AI is the future but key is how the applications shape up to capitalise on the technology.”

India is the Use Case Capital of AI 

“India is going to be a use case capital of AI. We’ll be very big users of AI, and we believe that AI can significantly help in the expansion of the ONDC Network,” said Manoj Gupta, the founder of Plotch.ai, in an exclusive interview with AIM. 

Similar thoughts were shared by Nandan Nilekani when he said that India is not in the arms race to build LLMs, and should instead focus on building use cases of AI to reach every citizen. He added that “Adbhut” India will be the AI use case capital of the world. 

“The Indian path in AI is different. We are not in the arms race to build the next LLM, let people with capital, let people who want to pedal chips do all that stuff… We are here to make a difference and our aim is to put this technology in the hands of people,” said Nilekani.

Krutrim founder Bhavish Aggarwal believes that India can build its own AI applications. Agreeing with him, former Tech Mahindra chief CP Gurnani said, “It’s time to stop ‘adopting’ and ‘adapting’ to AI applications created for the Western world.”

Gurnani said that the time is ripe for us to build AI models and apps based on Indian data, for Indian use cases, and store them on India-built hardware, software and cloud systems. “That will make us true leaders in the business of tech,” he added. 

Notably, Gurnani recently launched his own AI startup AIonOS. 

Startups Offering More Than LLMs

Lately, several AI startups in India have been building services using generative AI. For example, Unscript, a Bengaluru-based AI startup, is helping enterprises create videos with generative AI. Another video generation startup, InVideo, is estimated to generate $30 million in revenue in 2024. 

Recently, Sarvam AI launched Sarvam Agents. While the startup, backed by Lightspeed, Peak XV, and Khosla Ventures, is not the only company building AI agents, it stands out for its pricing. The cost of these agents starts at just one rupee per minute. 

According to co-founder Vivek Raghavan, enterprises can integrate these agents into their workflow without much hassle.

These agents can be integrated into contact centres and various applications across multiple industries, including insurance, food and grocery delivery, e-commerce, ride-hailing services, and even banking and payment apps.

Similarly, Krutrim AI is making AI shopping co-pilot for ONDC. Khosla Ventures-backed upliance.ai is building kitchen appliances  integrating generative AI. 

Meanwhile, Ema, an enterprise AI company founded by Meesho board member Surojit Chatterjee, recently raised an additional $36 million in Series A funding. 

The company is building a universal AI agent adaptable to a wide array of industries, including healthcare, retail, travel, hospitality, finance, manufacturing, e-commerce, and technology. 

Enterprises use Ema for customer support, legal, sales, compliance, HR, and IT functions. 

Lately, we have observed that Y Combinator is bullish on Indian AI startups, many of which are focused on building AI applications.

For example, the creator of AI software engineer Devika, Mufeed VH, who founded Stition.AI, is now part of YC S24 batch. His startup works around AI cybersecurity for fixing security vulnerabilities in codebases, and is now renamed to Asterisk. 

On the agentic front, Indian co-founders Sudipta Biswas and Sarthak Shrivastava are building AI employees through their startup FloWorks.

Examples are aplenty, with India poised to boast 100 AI unicorns in the next decade. 

In a conversation with AIM, Prayank Swaroop, partner at Accel India, said that the 27 AI startups his firm has invested in over the past few years are expected to be worth at least ‘five to ten billion dollars’ in the future, including those focused on wrapper-based technologies.

There are a host of categories, such as education, healthcare, manufacturing, entertainment, and finance, to explore with generative AI, and this is just the beginning.

The post The Secret to Creating the Next Billion-Dollar AI Startup appeared first on Analytics India Magazine.

Much like the AI industry, sceptics can say the gaming industry is also alive because of hyped products. And currently, the ‘hype’ is around Black Myth: Wukong. 

The game developed by Game Science, which is backed by the Chinese tech giant Tencent and Hero Interactive, is nothing short of a visual masterpiece, which is powered by NVIDIA GPUs. At the same time, its success story can be compared to OpenAI’s ChatGPT and also teaches a lot about how investments should work in the startup landscape.

To set the record, Black Myth: Wukong sold over 10 million copies in just three days, becoming the fastest selling game, beating Elden Right, Hogwarts Legacy, and even Red Dead Redemption 2. This is highly reminiscent of OpenAI acquiring 1 million users in just five days, compared to Instagram reaching 2.5 months to reach 1 million downloads.

But apart from the users, the game also tells us about what investing in AI startups means. 

The Word is ‘Patient Capital’

Black Myth: Wukong took the company five years to make. Game Science was established in 2014 in Shenzhen by seven former Tencent Games employees. Before shifting their focus to Black Myth: Wukong in 2018, the startup released mobile games and the shift to making the AAA game only happened because of the rise of Steam users in China. 

At that time, the company had 13 employees and in August 2020, when Game Science unveiled the first trailer for the game to attract talent, received over 10,000 resumes, including applications from AAA gaming companies and international candidates willing to apply for Chinese work visas. 

The development team eventually grew to 140 employees.

In March 2021, Tencent acquired a 5% minority stake in Game Science, emphasising that their role would be limited to technical support, without influencing the company’s operations or decisions. Though the financial backing was there, the company received several controversies around the game’s content and technical problems due to the shift from Unreal Engine 4 to Unreal Engine 5.

Before Tencent and Hero Interactive’s investment, Game Science’s financial performance was largely dependent on the success of their mobile games. While these games were commercially successful, the studio’s primary goal was to develop high-quality console games, which required significant financial backing. 

Similarly, AI Takes Time

This is what patient capital stands for. Believing in what the startup is building and giving them years to develop their products. What Game Science did right with their journey was developing smaller revenue generating games along the way to make up for the cost of building Wukong, which is something that AI startups should focus on. 

Or maybe they need investors like Microsoft and YC, who believe in OpenAI.

Arjun Rao, partner at Speciale Invest, also told AIM a similar strategy when investing in R&D startups. He said that it is essential to be patient when investing as these startups are still in the budding stage, and placing the bet on the right founders is paramount. “Founders do not need to worry about the current downturn and keep a long-term mindset,” said Rao.

AI startups, just like games, require extensive patient capital from investors as they require extended periods of R&D before their innovations can be brought to market. That is what happened with Microsoft backing OpenAI, eventually resulting in the release of Chat`GPT.

OpenAI started laying the foundation for ChatGPT in the beginning of 2020 when they released GPT-3. Though a great technological marvel, the company wasn’t generating profits, and still isn’t even after two years of ChatGPT’s release in 2022. 

India is expected to have around 100 AI unicorns in the next decade. This wouldn’t be possible if investors do not trust the founders and pour money into R&D. Prayank Swaroop, partner at Accel, said that VCs are increasingly expecting AI startups to demonstrate rapid revenue growth.

“Even to the pre-seed companies, we say, ‘Hey, you need to show whatever money you have before your next fundraiser. You need to start showing proof that customers are using you.’ Because so many other AI companies exist,” said Swaroop. 

This is what investing in AI startups should look like. The game that took more than five years to make couldn’t have been possible if the investors were just looking for immediate profitability and not betting on the founders. Maybe, Indian investors need to relook at their investment strategy.

The post What Black Myth: Wukong’s Success Tells us About Investing in AI Startups appeared first on Analytics India Magazine.

A lot goes into making the videos we consume online—brainstorming ideas, writing scripts, editing, and recording voice-overs—and all of this consumes a substantial amount of the content creators’ time. 

This is where generative AI can step in to ease the burden. It can be a great tool for the creators to streamline these processes and reduce the time spent on routine tasks.

Imagine an AI tool that lets you accomplish everything at one place with just a few prompts. InVideo AI promises exactly that, which is why content creators around the globe are falling in love with the platform.

According to Sanket Shah, co-founder and CEO of InVideo, the platform gets nearly 3 million new users every month. 

“We don’t track the total number of people using our free product, but if we take 3 million on average, we could have around 35 million users on our platform in just one year. In terms of paid users, we have about 150,000 of them,” he said.

AIM met Shah in early August in the Bengaluru edition of AWS GenAI Loft, a collaborative gathering of developers, startups, and AI enthusiasts to learn, build, and connect.

Shah revealed that the startup has already secured over 50% of its $30 million revenue target for the year.

Avoiding the Uncanny Valley

Interestingly, InVideo has not developed a text-to-video model like OpenAI’s Sora or Kling. Instead, the startup has partnered with several media providers like Getty Images and Shutterstock and pays them a licence fee. 

One primary reason for this approach, according to Shah, is that InVideo wants to provide users with publishable videos. 

Currently, even though models like Sora produce high-quality videos, there is limited clarity about the datasets they are trained on, which complicates the publishability of these videos.

“Moreover, at InVideo, one of our core principles is to avoid anything that falls into the uncanny valley. As of last week, we felt that generative image and video technology still often produced results with issues like extra fingers or multiple eyes—things that are not acceptable for our purposes. 

“We focus on delivering high-quality, publishable videos, so we prioritise ensuring that our users receive content that meets professional standards,” Shah said. 

The platform leverages AI to understand the user’s intent, write script, handle voice-overs—practically cloning the user’s voice. 

It selects and integrates media, performs editing tasks, adds music, and ensures that all elements like transitions and zooms are correctly applied, effectively automating much of the post-production process.

However, the startup does not shy away from leveraging models like Sora. Once available, they could contemplate integrating Sora and Kling into their platform. However, they are not in the business of building models.

“We don’t want to enter into a race with the hyperscalers (model builders) to build the next-big model. Models are also perishable and we have seen that already,” he said.

The AI in InVideo 

While the startup is refraining from entering the territory of model builders like OpenAI, Anthropic, Microsoft, and Google, it is building models that suit its business model.

“We prefer to focus on developing models that are niche and highly specific to our needs. For instance, we are working on a lip-sync model tailored to our requirements,” Shah revealed.

The startup also plans a new AI-powered feature next month, which allows users to create an avatar or a digital clone of themselves. 

“Here’s how it works– you record a short video, speaking for 30 seconds to a minute, and the AI generates an avatar. Once you have your avatar, you can input a prompt or specify what you want it to say,” Shah revealed.

The platform does leverage LLMs from Anthropic, OpenAI and Google, but Shah refrained from revealing much about their use cases. “This is very proprietary and that is where most of the magic happens.”

InVideo also leverages Amazon Bedrock, which gives them access to some of the top LLMs through a single API. 

Moreover, the startup also leverages AWS’ multi-region fleet of Spot GPUs for video rendering and editing on open-source solutions, allowing them to run 90% of their workload on Spot instances, which enables close to 40% cost reduction.

Enabling Content Creators with AI

The startup started with a pre-AI product in 2017 and was initially focussed on enterprises. However, pivoting to AI and to a more B2C business model from B2B proved to be a game changer.

Today, it caters to YouTubers and established and new content creators creating content for Facebook, Instagram, and TikTok. 

“The platform is also leveraged by small businesses, for example, a lady selling horses in Texas, a partially deaf teacher in Palo Alto who is teaching a community college, a bunch of students and teachers, someone selling water bottles, and some restaurants,” Shah revealed. 

“About 5% of our customers are also filmmakers.”

When AIM asked Shah about some of the most fun and interesting videos he has seen users generate using the platform, he revealed that the brand manager of the legendary rock band Aerosmith used InVideo to generate content on how to deal with depression.

“One day, the brand manager received an email from a viewer who was contemplating suicide. After watching the video and following some advice, they decided against it. Stories like these are incredibly powerful,” Shah revealed.

The post Why are Content Creators Falling in Love with This AI Startup? appeared first on Analytics India Magazine.

Enterprises love to RAG, but not everyone is great at it. The much touted solution for hallucinations and bringing new information to LLM systems, is often difficult to maintain, and to evaluate if it is even getting the right answers. This is where Ragas comes into the picture.

With over 6,000 stars on GitHub and an active Discord community over 1,300 members strong, Ragas was co-founded by Shahul ES and his college friend Jithin James. The YC-backed Bengaluru-based AI startup is building an open source stand for evaluation of RAG-based applications. 

Several engineering teams from companies such as Microsoft, IBM, Baidu, Cisco, AWS, Databricks, and Adobe, rely on Ragas offerings to make their pipeline pristine. Ragas already processes 20 million evaluations monthly for companies and is growing at 70% month over month.

The team has partnered with various companies such as Llama Index and Langchain for providing their solutions and have been heavily appreciated by the community. But what makes them special?

Not Everyone Can RAG

The idea started when they were building LLM applications and noticed a glaring gap in the market: there was no effective way to evaluate the performance of these systems. “We realised there were no standardised evaluation methods for these systems. So, we put out a small open-source project, and the response was overwhelming,” Shahul explained while speaking with AIM.

By mid-2023, Ragas had gained significant traction, even catching the attention of OpenAI, which featured their product during a DevDay event. The startup continued to iterate on their product, receiving positive feedback from major players in the tech industry. “We started getting more attention, and we applied to the Y Combinator (YC) Fall 2023 batch and got selected,” Shahul said, reflecting on their rapid growth.

Ragas’s core offering is its open-source engine for automated evaluation of RAG systems, but the startup is also exploring additional features that cater specifically to enterprise needs. “We are focusing on bringing more automation into the evaluation process,” Shahul said. The goal is to save developers’ time by automating the boring parts of the job. That’s why enterprises use Ragas.

As Ragas continues to evolve, Shahul emphasised the importance of their open-source strategy. “We want to build something that becomes the standard for evaluation in LLM applications. Our vision is that when someone thinks about evaluation, they think of Ragas.”

While speaking with AIM in 2022, Kochi-based Shahul, who happens to be a Kaggle Grandmaster, revealed that he used to miss classes and spend his time Kaggeling. 

The Love for Developers

“YC was a game-changer for us,” Shahul noted. “Being in San Francisco allowed us to learn from some of the best in the industry. We understood what it takes to build a successful product and the frameworks needed to scale.”

Despite their global ambitions, Ragas remains deeply rooted in India. “Most of our hires are in Bengaluru,” Shahul said. “We have a strong network here and are committed to providing opportunities to high-quality engineers who may not have access to state-of-the-art projects.”

“We have been working on AI and ML since college,” Shahul said. “After graduating, we worked in remote startups for three years, contributing to open source projects. In 2023, we decided to experiment and see if we could build something of our own. That’s when we quit our jobs and started Ragas.”

Looking ahead, Ragas is planning to expand its product offerings to cover a broader range of LLM applications. “We’re very excited about our upcoming release, v0.2. It’s about expanding beyond RAG systems to include more complex applications, like agent tool-based calls,” Shahul shared.

Shahul and his team are focused on building a solution that not only meets the current needs of developers and enterprises, but also anticipates the challenges of the future. “We are building something that developers love, and that’s our core philosophy,” Shahul concluded.

The post This YC-Backed Bengaluru AI Startup is Powering AWS, Microsoft, Databricks, and Moody’s with 5 Mn Monthly Evaluations appeared first on Analytics India Magazine.

Every startup wants to be big someday, and most successful businesses were startups when they began. But the truth is, somewhere down the line most of them end up either dying, or getting acquired by big companies. This gets a notable exit for most investors, but the story for startups ends there. 

“Most startups are destined to die. Even the funded ones,” said Kunal Shah, the founder of CRED, adding that the success of a startup is mostly a miracle. “And a miracle doesn’t happen with a team that’s looking for stability and dislikes ambiguity,” he said, adding that startups need problem solvers.

The same is the case with the current AI startups, globally. The ones that started their journey a few years ago are now either getting acquired or gradually dying because of lack of funds. Only a few companies, such as OpenAI, Anthropic, Mistral, Hugging Face, and few others, are actively getting funded, which eventually will be known as the prodigies of the AI generation.

The AI Bubble is Here

With Google recently acquiring the founders of CharacterAI, the case of AI startups sustaining for a long term is put into question. The same was the case with Mustafa Suleyman from Inflection AI joining Microsoft AI Research team, Amazon taking over Adept AI’s team, Snowflake’s acquisition of Neeva, or Canva’s acquisition of Lenoardo.ai.

Going by that logic, Reka AI or even Cohere, might end up with the same fate. With Emad Mostaque leaving, Stability AI is also going through unstable times. The same could happen to Midjourney. 

What options do startups really have apart from getting acquired? As the discussion around the AI bubble intensifies, it gets tougher for companies to raise funds from investors as they get increasingly wary. The ones that were successful during the AI boom are now encountering difficulties.

Most of these startups are also not making money. For example, Lensa, the AI photo generating company with a great product and marketing, was not able to differentiate itself even when it was generating revenue. Quickly, other companies started building similar offerings within their own products, making Lensa lack defensibility.

This is the problem with most of the AI startups. “The problem with AI is that just as quickly as you can create a great product, another copycat can emerge and undercut you,” said David Chen, the CEO of Kapsule. Apart from this, another problem he highlighted was the problem of finding use cases and distribution. 

While India is currently running as the AI use capital of the world, running on jugaad and not VC money, the long term strategy for them also seems questionable. Though they know how to run businesses without large investment, most of them have the inherent goal of getting acquired, as competing with big-tech is not what they strive to do.

A few, such as Sarvam AI, Krutrim, TWO.AI, may have received a decent amount of funding, but long-term plans still remain unclear.

Sriharsha Putrevu, the co-founder of Retail Technology Group, said that the current AI startups would fail as they are not focused on value creation, but rather just the valuation. “Startup is a repeatable, scalable, sustainable business model not just a cash burning, user acquiring business models in hope of making profits in decades,” he said. 

Sales Cure All Problems

When it comes to starting a company, raising funds is the relatively easy part (not easy in itself) as we have seen with a lot of current AI funds. The harder part is finding the right market, niche, and perfect fit for profit. 

The problem cannot be solved by building the best version of an AI model as well, since any competitor would learn from it and create an even better one with the frontier of AI constantly moving. Though the cost of building AI models is shrinking which is helping the startups, it is also drawing in several competitors to the field.

Take, for example, OpenAI’s GPT-4 constantly getting dethroned by Meta’s Llama 3 or Anthropic’s Claude, or Google’s Gemini. And these are the ones that are already competing for the top spot; what about the new startups?

If AI is like electricity, it is important for startups to build a niche and solve the problem in a specific field, since competing for ‘best electricity’ does not make sense. That is what the current Indian AI landscape is focused on – to build use cases of AI instead of building the next LLM. Maybe, this would help them sustain, but for how long is the question. 

Moreover, since the investors in India are extra wary of pouring money into startups, these companies have very low tailwinds. This is making them run low on money and get close to the point of acquisition, or maybe extinction. 

All these problems can be solved with sales, for which startups need to move fast. For some, AI is a bubble, for some it is a tree. Regardless, many AI startups would burst or fall off that tree.

The post Most AI Startups are Destined to Fail – Even the Funded Ones appeared first on Analytics India Magazine.

The startup founders of Bangalore are a completely different breed altogether. A few weeks ago, we spoke about the rise of Chief Everything Officers, the founders who assumed the role of every employee in a company. They made coffee, could code the next billion-dollar app, secure funding, find an apartment, and whatnot.

Not to forget, human resources management (HRM), a key organisational function that founders are more than happy to take up. 

In an interview with Tucker Carlson, Pavel Durov, the founder and CEO of Telegram, revealed that he was the only product manager the company had! Notably, the company has a billion users and a 30-member tech team. “I still come up with most of the features and still work with every engineer and designer… Because I enjoy it,” said Durov. 

When asked about the size of his HR department, Durov said, “Zero” because Telegram decentralised it. “We have a separate platform for that and we select the best of the best engineers from competitions,” explained Durov.

“We don’t need the HR department to find super talented engineers,” he added.

Although Telegram is a Russian company, the same trend can be seen catching up in several startups in Bangalore. “No HR, one product manager and 30 devs banging their heads with MacBooks but we call it ‘Third Wave Coffee’,” said Shravan Tickoo, the founder of Rethink Systems.

A blog post by Ria Shroff Desai from Blume Ventures also explained why a lot of early-stage startups do not need an HR as that is the time for “moving fast and breaking things”. Dara Khusrowshahi, the CEO of Uber, believes that at the beginning of a startup’s journey, you need people to be daring like pirates, and become a navy later on.

10x Founders

However, running a company this way is not easy. Shweta Jain, product owner at Fictiv, said that numerous factors affect the work efficiency within such companies and this cannot work for startups that rely on remote work. “Consider the complexity with the same setup that could arise if the employees are from different time zones, cultures, and speak different languages,” she said, adding that it could lead to decreased productivity.

This also questions the need for HR or product managers within a company of a small size. “The product managers, even though I may get bricks for this, hardly add much value and very often I see that they are just acting as glorious postmen and women,” said a user on the LinkedIn post. 

Although, this type of setup can be risky for companies who don’t have a founder with a vision on the product. Such self-reliance is giving rise to a lot of founders or ‘solopreneur’ who are ready to do a lot of work by themselves for a business run by a single person.

When it comes to Bengaluru, the hustle is real with everyone trying to build a startup. “Startup addiction is in the air of Bangalore (sic),” said a user of LinkedIn. And what could be better than your co-founders being your flatmates, or maybe just you tackling multiple roles. This has given rise to founders and CEOs who are willing to multitask to fulfil the demands of their startups.

HR to be Replaced?

With the advent of generative AI tools for marketing and analytics, many companies have integrated them within their teams for improved productivity. With products like Leena AI and Zoho Recruit, which are helping companies assist, or in some cases, completely automate the hiring process is proving to be a game changer for founders who want to keep a lean team. 

Last year, big-tech companies laid off many in HR roles citing reasons of upskilling the department as AI was able to handle a majority of their jobs.

Moreover, the 9-to-5 jobs are also coming closer to an end with the rise of the gig-based-economy, as predicted by Reid Hoffman. There seems to be less importance given to HR as there would be no regular employees in the future, which simultaneously could also be problematic for companies that are trying to retain any type of talent. 

A lot of companies have already started outsourcing the hiring process to recruitment companies. This would also give rise to the one-person billion-dollar companies. 

Another prediction is that by 2034, one in three professionals will operate multiple micro-businesses. The passion economy will give rise to unexpected millionaires. This could also possibly give rise to the first billion-dollar business built by one person with the help of AI. 

Given the culture of Bangalore, it is very likely that the first one-person billion-dollar company would come out of the city, and it would not need the HR department, as there would be no one to hire, or fire. 

Alas! The HR jokes would also come to an end. 

The post Bangalore Startups Don’t Need HR appeared first on Analytics India Magazine.

The world of 3D model creation is extremely capital-intensive, and India barely has any company building this. As indie game development gains prominence, there is a dire need for an Indian company to enter this space.

Recognising this void, Google for Startups Accelerator: AI First Program has decided to support 3DAiLY, a 3D model creation company that makes ultra-realistic production-ready assets using generative AI. The company is also one of the first to make these models accessible on its community platform for AAA and indie games alike.

Speaking with AIM, the CEO and founder, Harsha P Deka, shared his journey, which began in 2010 with a personal loss that fueled his drive to innovate. While studying computer science in Canada, Deka received the devastating news of his best friend’s death. 

“I wanted to create a 3D model of my friend to give to his family as a memento,” Deka recalled. 

However, he soon discovered the limitations of the technology at the time. Despite reaching out to multiple gaming and animation studios, none could produce a 3D model from a photograph.

The Birth of 3DAiLY

Undeterred, Deka delved into the intricacies of 3D modelling, understanding the immense time and effort required to create high-quality models. By 2014, he had founded an animation studio, encountering firsthand the industry’s challenges. 

“Creating a game, Belegon, took us a year and made us realise the massive funding needed for such ambitious projects,” Deka explained. His experiences underscore the complexities of 3D modelling, particularly for animation and gaming.

A pivotal moment came in 2015 when Deka encountered a 3D scanning setup in a US mall, which produced 3D-printed miniatures from in-person scans. “I asked if they could do it from a photograph, and they said it wasn’t feasible,” Deka said. 

This gap in the market inspired him to create 3DAiLY, a company that could generate 3D models from photos. By leveraging AI, Deka set out to make high-quality 3D modelling accessible and efficient.

3DAiLY has since evolved into a leader in the 3D modelling industry, creating a comprehensive library of human models and developing proprietary AI technology. “We built our own foundation model using the data we’ve collected over the years,” Deka stated. 

Its approach combines artists’ intuition with AI, ensuring that the models are production-ready and of the highest quality. 

Deka said that unlike other AI tools such as Ready Player Me or Sloyd that produce low quality meshes, 3DAiLY’s models are fully rigged and animatable, compatible with various gaming engines like Unity, Unreal, and CryEngine. 

“What MetaHuman did for Unreal, we’re doing for multiple engines,” he emphasised, adding that the rest of them are building tools and not a platform which has a built-in marketplace.

Overcoming Industry Challenges

Establishing such a pioneering company in India has not been without its challenges. “The space is not well understood, and getting VC funding is tough,” Deka noted. Despite these hurdles, 3DAiLY has garnered significant traction, with indie game developers and AAA studios alike adopting their technology. 

Offering models at a fraction of the traditional cost, the company is breaking down barriers to high-quality 3D content creation.

Looking ahead, Deka envisions expanding 3DAiLY’s capabilities to include design-to-3D modelling tools, enabling artists to transform their ideas into tangible models. 

“We’re building an ecosystem where artists can create assets and participate in an SDK, benefiting from in-game asset sales,” he explained. Currently, the platform has around 12,500 artists from 150 countries. 

“Artists are critical to the success of games, yet they often earn the least,” Deka pointed out. By offering tools that significantly reduce production time and costs, 3DAiLY aims to empower artists, allowing them to focus on creativity while the AI handles the heavy lifting. 

This approach not only enhances productivity but also ensures that high-quality 3D models are accessible to all. 

The post This Indian AI Startup is Creating 3D Models for AAA and Indie Games appeared first on Analytics India Magazine.

AI music generation is a tricky business. Amidst copyright claims and the need for fairly compensating artists, it becomes an uphill task for AI startups, such as Suno.ai or Udio AI, to gain revenue and popularity. 

However, Beatoven.ai, an Indian AI music startup, has gotten the hang of it in the most ethical and responsible way possible.

One of the most important reasons for that is its co-founder and CEO Mansoor Rahimat Khan is a professional sitar player himself and comes from a family of musicians going back seven generations. “I was very fascinated by this field of music tech,” he said. 

Khan told AIM that he started his journey at IIT Bombay and realised that though there were not many opportunities in India, he wanted to combine his passion for music and technology. 

Beatoven.ai is part of the JioGenNext 2024, Google for Startups Accelerator, and AWS ML Elevate 2023 programs. Khan said that the team applied to many accelerator programs because they realised they needed a lot of compute to fulfil the goal of building an AI music generator. 

The company raised $1.3 million in its pre-series A round led by Entrepreneur First and Capital 2B, with a total funding of $2.42 million.

After switching several jobs, Khan met Siddharth Bhardwaj and building on their shared passions for music and tech founded Beatoven.ai in 2021. “After coming back from Georgia Tech, I got involved in the startup ecosystem, and started working with ToneTag, an audio tech startup funded by Amazon,” said Khan. 

Everyone Needs Background Music in their Life

The co-founders found out that the biggest market was in the generation of sound tracks for Indie game developers, agencies, and production houses. “But when we look at the nitty gritty of the industry, copyrights are a very scary thing. We thought that generative AI could be a solution to this.” Khan said that the idea was to figure out how users could give simple prompts and generate audio.

The initial idea was to create a simple consumer focused UI where users could select a genre, mood, and duration to generate a soundtrack. But that was when the era of LLM hadn’t started and NLP wasn’t good enough for such tasks. “We started in 2021 before the LLM era, and our venture capital came from Entrepreneur First. We raised a million dollars in 2021 and quickly built our technology from scratch.”

The biggest challenge like every other AI company was the collection of data. “You either partnered with the labels that charged huge licensing fees or scraped [data]. That was the only other option. But if you did that, you would be sued,” said Khan.

All of the Tech

This is where Beatoven.ai takes the edge over other products in the market. Khan and his team started contacting small, and slowly bigger artists for creating partnerships and sourcing their own data. The company had a headstart as no one was talking about this field back then. Within a year, it amassed more than 100,000 data samples, which were all proprietary for them.

During the initial days, Beatoven.ai did not use Transformers. Khan said that it is one of the reasons that the quality was not that great. Later, when Diffusion models came into the picture, the team realised that it is the way forward for AI-based music generation. 

The company started by using different models for different purposes, this included the ChatGPT API from OpenAI. The Beatoven.ai platform also uses CLAP (Contrastive Language-Audio Pretraining), which is mostly used for video generation. 

Apart from this, the company uses latent diffusion models like Stability AI’s Stable Audio, VAE models, and AudioLLM, for different tasks such as individual instruments within the generated music. Then the company uses an Ensemble model for mixing all these individual audios together. 

For inference, the company uses CPUs (instead of GPUs), which keeps it fast and optimised, while reducing costs. 

Trained Fairly

Khan admitted that the audio files generated by Suno.ai’s have superior quality right now, but they also use Diffusion models, which makes them a little slow. “The quality is significantly better from where we started, but it’s not quite there yet.” Khan added that currently the speed is high because the company uses different models for different tasks.

To further expand the data, Beatoven.ai started partnering with several outlets such as Rolling Stone and packaged it like a creator fund. In January 2023, it announced a $50,000 fund for Indie music as a part of the Humans of Beatoven.ai program for expanding their catalogue. 

This gave Beatoven.ai a lot of popularity and many artists wanted to partner with the team. Khan said that the company aims to do more licensing deals to expand music libraries. “When it comes to Indian labels though, they are not yet open to licensing deals,” said Khan. 

Beatoven.ai’s model is certified as Fairly Trained and also certified by AI for Music as an ethically trained AI model.

Apart from music generation, Beatoven.ai is launching Augment, similar to ElevenLabs’s voice generation model. This would allow agencies to connect to Beatoven.ai’s API and train on their own data to make remixes of their own music. For the demo, Khan showed how a simple sitar tune could be turned into a hip-hop remix. 

“You can just use your existing content and create new songs. That’s the idea,” he said.

Currently, Beatoven.ai is also testing a video-to-audio model using Google’s Gemini, where users can upload a video and the model would understand the context and generate music based on that. Khan showed a demo to AIM where the model could also be guided using text prompts for better quality audio generation. 

Not Everyone is a Musician

Khan envisions that in the near future, companies such as Spotify or YouTube start open sourcing their data and offer APIs to make the AI music industry a little more open.

Meanwhile, while speaking with AIM, Udio’s co-founder Andrew Sanchez said, “It’s enabling for people who are just up and coming, who don’t yet have big professional careers, the resources, time or money to really invest in making a career. “It’s enabling a whole new set of creators.” This would make everyone a musician. 

When it comes to Beatoven.ai, he said that he aims to head in a more B2B direction as building a direct consumer app does not make sense. “I don’t believe everybody wants to create music,” added Khan, saying that not everyone is learning music in the world. That is why, the company is currently focused only on background music without vocals. 

The post India’s Beatoven.ai Shows the World How AI Music Generation is Done Right appeared first on Analytics India Magazine.

In May, the Department of Science & Technology (DST) announced the launch of a new hub dedicated to creating Indic language models. This new hub, BharatGPT, was created in collaboration with IIT Bombay, IIT Madras, IIT Hyderabad, IIIT Hyderabad, IIM Indore, and IIT Mandi. 

The initiative aims to develop LLMs in Indian languages for India, along with applications for Indian enterprises.

Apart from working on BharatGPT, Ganesh Ramakrishnan, a professor at IIT Bombay, has been dedicated to developing translation engines. To continue this bid, he has co-founded bbsAI with Ganesh Arnaal, which has been a decade in the making. 

“Arnaal approached me in 2013 with the idea of developing a translation engine to translate technical books from English into Hindi and other major Indian languages. Thus, the Udaan Translation Project was born,” Ramakrishnan recalled in an exclusive interaction with AIM. 

At the recent Global INDIAai Summit 2024, Ramakrishnan discussed how AI can produce groundbreaking outcomes for real business applications in data-scarce environments. He underscored the significance of creating small language models and innovating algorithms. 

Emphasising on human centricity and inclusive AI, Ramakrishnan added that the approach of making small language models for Indic languages addresses the challenge of limited data, enabling the delivery of dependable and practical solutions to the industry. This has led to the founding of bbsAI.

Initially funded by Arnaal, bbsAI officially became a commercial entity in February 2023, entering a licence agreement with IIT Bombay for the commercial exploitation of the Udaan Translation Engine.

Flying with Udaan

The journey for bbsAI started with the Udaan, which stands out in the crowded market of translation tools. Ramakrishnan explained, “Our engine is a result of training models that are probabilistic in nature, but we introduced technical dictionaries as constraints to overcome hallucinations and inaccuracies.”

This deterministic approach, powered by their own open-sourced data-efficient machine learning algorithms and grounded in extensive language resource research by Arnaal, ensures accurate and context-appropriate translations in scientific and technical fields. “The Udaan Translation Engine offers a comprehensive ecosystem: an OCR engine preserving the source document’s style and layout, a translation engine, and a user-friendly post-editing tool.”

In 2022, Ramakrishnan and Arnaal met education minister Dharmendra Pradhan, who appreciated their dedication to building Udaan. “They have developed a translation tool— Udaan—that is breaking the language barrier in education by translating learning materials in Indian languages,” the minister tweeted.

Revolutionising the Insurance Industry

Expanding its offerings to leverage its digitalisation and OCR capabilities, bbsAI has introduced a suite of AI-enhanced process automation solutions that has the potential for a variety of use cases across industries. 

“As a natural extension of the machine learning capabilities we have built over the years, we have begun to offer process automation solutions by building small language models that can provide intelligent, accurate and inherently deterministic solutions to automate a variety of business processes,” Ramakrishnan elaborated. 

bbsAI developed an AI solution for ICICI Lombard’s quotation management system (QMS). “Our solution captures data from various file formats and populates it automatically into the templated underwriting formats, delivering productivity gains,” said Ramakrishnan. 

This solution is a global first in the insurance industry, achieving over 90% accuracy while adhering to strict data privacy regulations with limited datasets. “We have delivered a staggering accuracy of over 90% while completely eliminating hallucinations,” he emphasised.

Small Language Models and Explainability

Ramakrishnan explained bbsAI’s unique approach, which is built on small language models and explainability by design. 

“LLMs perform many tasks, but for business use-cases, explainability and reliability are crucial,” Ramakrishnan stressed. This focus on deterministic solutions has enabled bbsAI to create accurate, reliable, and explainable AI solutions, fostering greater industry adoption. 

“We integrate domain knowledge and cross-industry understanding as an integral part of the development process, not as an afterthought,” he added.

Moving Beyond POCs

One of bbsAI’s significant milestones is its transition from proof of concept (PoC) to real-world AI solutions. “The key is shifting from probabilistic to deterministic models, providing explainable and accurate solutions,” noted Ramakrishnan. 

This approach has not only inspired user confidence but has also demonstrated tangible benefits in efficiency and productivity for clients. “With our unique approach, we have successfully converted AI promises into products and solutions,” he asserted.

bbsAI’s journey from a visionary project to a trailblazer in business automation and translation technology is truly remarkable. “We at bbsAI are passionate about making technology available to all Indians,” added Ramakrishnan.

Bharat Bhasha Sanganan

At its core, bbsAI is driven by the vision of Bharat Bhasha Sanganan, meaning Indian language computing. “In India, only those who know English have privileged access to technology. If we look globally, most developed nations have access to technology in their native languages,” Ramakrishnan explained. 

bbsAI (which stands for Bharat Bhasha Sanganan AI) has taken significant steps to bridge this gap, starting by creating a complete Hindi user interface for LibreOffice, bbsहिन्दीoffice and is planning to extend this to other major Indian languages.

bbsAI has a natural synergy with the National Education Policy (NEP), which has catalysed higher learning through Indian languages, aligning perfectly with bbsAI’s mission. 

“From the academic year 2023-24, engineering and medicine are being taught in 11 Indian languages,” Ramakrishnan mentioned. This shift is expected to boost the demand for textbooks in Indian languages, making bbsAI a valuable partner for publishers and academic institutions. 

“We have been working on machine translation for technical domains for over a decade, ensuring the use of domain-specific vocabulary in our translations,” he concluded.

The post [Exclusive] BharatGPT’s Ganesh Ramakrishnan’s AI Startup bbsAI Tackles Limited Indic Data Challenge appeared first on Analytics India Magazine.

Are AI agents the next big thing? Bengaluru and San Francisco-based startup Floworks definitely think so. The startup, funded by Y Combinator in the Winter 23 cohort, is building AI agents that can handle end-to-end sales functions in an organisation.

In an exclusive interaction with AIM, Floworks co-founders Sudipta Biswas and Sarthak Shrivastava said that the startup is building what they call an ‘AI employee’. 

Large language models (LLMs) today can’t do certain things despite their impressive knowledge base, like sending an email. The startup, which raised $1.5 million in seed funding earlier this year, has developed Alisha, which they call the world’s first fully autonomous AI-powered sales development representative (SDR).

Alisha can automate the entire lead generation process, from prospecting and qualification to scheduling meetings and sending emails, allowing human sales representatives to focus on more concrete tasks.

Alisha Achieves 100% Reliability 

The startup claims that with Alisha, users will generate 10x more leads each day. The AI assistant, which is powered by Thorv2, can interact with external tools such as emails, CRMs, Google Search, and Calendars. It can also parse through one’s email for leads and update the CRM accordingly.

“On any given day, we probably use 10 to 15 different software or tools. But a standard language model like ChatGPT cannot use these tools, and this is where we come in. 

“Our model, which we are internally calling ThorV2, is the most accurate and the most reliable model out there in the world when it comes to using external tools right now,” Biswas said.

Alisha is a product that comes out of the box and can be set up in under five minutes. However, the startup helps fine-tune Alisha to the particular business’s use case by training Alisha with that particular company’s data. 

“Alisha, our SDR, trains itself using this data, integrating workflows and processes specific to each customer. Essentially, each instance of Alisha becomes tailored to each customer, equipped to handle all inquiries about their company and products,” Shrivastava said.

The startup claims Alisha is the most reliable tool in the market, with ThorV2 near zero hallucination and reliability for function calling at 99.5%, which is impressive. 

The startup also claims other models like Claude-3 Opus and GPT-4o have a reliability score of 59.7% and 83.9%, respectively. 

“When using ChatGPT, if you provide the same prompt twice, chances are you’ll receive different answers each time. This variability is inherent to how language models operate,” Biswas pointed out.

However, this very nature of LLMs and the fact that they hallucinate limit the use of these large models for AI agentic workflows.

“When it comes to tool usage, for instance, accessing your CRM, you want very reliable and deterministic action because you don’t want wrong information to be put in your CRM or send a wrong email to your customer. 

“Mistakes at the LLM level can propagate widely, impacting multiple areas with significant business repercussions. Our model ensures 100% reliability in these critical functions,” Biswas added.

(From left to right: Sarthak Shrivastava and Sudipta Biswas, co-founders at Floworks)

ThorV2 Powers Alisha

Thor2, which powers Alisha, is a mixture of agents (MoA) based on a proprietary LLM architecture built from scratch.

“ThorV2 comprises eight distinct AI agents or LLMs, incorporating both open-source and proprietary models. While some are pre-trained, others require us to rebuild their architecture entirely to align with our specific requirements,” Biswas said.

He further claims that Thorv2 is not only 36% more accurate than OpenAI’s GPT-4o but also 4x cheaper and almost 30% faster in terms of latency.

When asked about the cost of building ThoV2, Biswas revealed that it was around $1 million lower, which is significantly lower compared to foundational models.

Moreover, ThorV2 costs just $1.60 per 1000 queries, making it 175% cheaper than GPT-4o.

(Source: Floworks)

Voice Capabilities in the Pipeline 

The founders also revealed that voice capabilities for their Alisha is something they are working towards. Earlier this year, we saw both OpenAI and Google fascinate everyone with the human-like voice capabilities of their models.

Though OpenAI made GPT-4o available for free, it is yet to release the voice capabilities. Biswas reveals that voice is something they are working towards, but he does not see a demand for multimodal, which involves videos. 

Moreover, he claims Floworks does not want to make bold promises and then take an eternity to deliver a product. 

“It’s very easy to build a prototype and woo the audience, but then to actually build and release a production version, where everyone can use it scalably, that’s a whole different challenge,” Biswas said.

An alumni of IIT Kharagpur, Biswas points out a competitor of Floworks called Adept AI. “The company has raised around $400 million so far, and despite being in existence for over two years, still hasn’t released a product. In fact, the company is on the verge of breaking up,” he added.

Automating End-to-end Sales 

While Alisha is designed for function calling, the vision of the company is to build an AI system that handles end-to-end sales functions in an enterprise.

“We actually envision that in the near future, using Floworks, companies will not require sales teams. It will just be basically creating a good product and then actually training an AI system to sell the product,” Shrivastava said.

When asked if Alisha can make cold calls, he added that Alisha is already sending emails on behalf of humans. Soon, it will start reaching out to people on LinkedIn, WhatsApp and other mediums.

“Voice call too is just another mode of communication, and once the technology is ready, Alisha, too, will start making calls,” Shrivastava added.

While the co-founders are confident, it remains to be seen how much of these can be achieved, particularly whether AI can grasp the nuances of sales functions.

Expansion Plans 

Biswas revealed that the team size is 17 currently and they plan to hire for additional roles and expand the team to around 30-35 in the coming months. 

The company started active sales only two months back and so far has acquired around 14 customers both in the Indian and the US markets. Some of the customers include Anya, Unscript, and Qodex.

“We are growing 100% every month when it comes to customer acquisition, and we are getting a lot of referral customers who are knocking on our doors and enquiring about our product,” Shrivastava said.

Alongside Alisha, Floworks’ upcoming products include AI RevOps, AI project managers, and AI Executive Assistants. These innovations aim to optimise business operations, enabling “one-person unicorns.”

The post AIM Exclusive: YC-backed Indian Startup Claims its AI Agent is Better than OpenAI’s GPT-4o appeared first on Analytics India Magazine.