Mumbai-based legal tech company LexLegis has set itself apart as “India’s answer to Harvey AI,” having opened for access as of this week.

LexLegis AI has been trained on one crore legal documents aggregated over 25 years. The AI tool is aimed at legal professionals, offering detailed analyses for legal research.

“It is to help simplify and demystify the legal complexities for everyone and to save time on the vast amounts of time that we’re spending on legal research. The tool enables users to efficiently navigate through thousands of pages and extract meaningful, actionable information,” said co-founder and managing director Saakar S Yadav.

The legal research company, which was founded in 1998, has reinvented itself this year with the goal of building an LLM for Indian law. The company was founded by the late S C Yadav, who served as the Chief Commissioner of Income Tax, and his son Saakar S Yadav.

Over the years, the company worked on several legal tech solutions. Shortly after its founding, the company developed and launched a search engine catered specifically towards legal professionals and tax consultants, to help in the understanding of the taxation domain.

Additionally, they also built the largest database of judgments in India in 2004, followed a decade later by the development of the National Judicial Reference System (NJRS), which is the world’s largest repository of appeals for the Income Tax Department.

With LexLegis AI, the company has leveraged its 25 years of experience within the industry to offer an overarching tool to help legal professionals, businesses and researchers cut down on the time used to research and find citations for relevant cases.

Speaking on the tool, Yadav stated that while the tool currently focuses on tax law, it aims to inculcate all fields of law for use.

While previously AIM has covered tools to assist legal professionals, this is one of the first Indian-made LLMs for law, focusing solely on the Indian legal system.

The post This Mumbai-Based Startup Has Released India’s Very Own Harvey AI appeared first on AIM.

Mufeed VH, the creator of the AI software engineer Devika, has launched its AI tool and startup, Asterisk, along with Vivek R and Asjid Kalam.

Asterisk, a Y Combinator S24-backed AI agent, is revolutionising cybersecurity by automatically detecting and patching security vulnerabilities in codebases. Unlike traditional static security tools, which produce nearly 95% false positives and miss critical business logic errors, Asterisk offers a groundbreaking solution.

The AI agent mimics the analysis process of human security experts, identifying vulnerabilities such as unauthorised access, privilege escalation, and cost-inflating bugs. Asterisk operates autonomously, testing vulnerabilities in a sandbox environment and producing reports without any user intervention, ensuring zero false positives.

Asterisk confirms vulnerabilities by launching a sandbox environment, running the scanned software, and actively attempting to exploit the identified bugs. When Asterisk flags a vulnerability, it’s a confirmed threat.

The team has played a key role in securing major companies, including Google, Mastercard, Okta, NVIDIA, and Microsoft.

Asterisk possesses also a deep understanding of a company’s codebase, enabling it to simulate attacks like a malicious hacker would. This allows it to devise attack scenarios, similar to what was seen in the recent CrowdStrike incident.

Mufeed was earlier the founder of Lyminal and Stition.AI, which is now known as Asterisk, where the team was researching on security within AI models. He was also the gold medalist at the IndiaSkills 2021 Nationals in cybersecurity when he was just 19 years old. 

Kalam is Silver medalist at IndiaSkills and former Security Research Engineer at Emirates National Bank (UAE). Vivek is former Distributed Systems/Platforms Engineer at Chorus One, one of the largest Proof-of-Stake (POS) validators.

The post Devika Creator Launches Asterisk, YC-backed AI Agent Startup appeared first on AIM.

SigTuple, a Bengaluru-based medtech startup, has secured $4 million (INR 33 crores) in an extended Series C funding round led by SIDBI Venture Capital. The funding also saw participation from existing investors, including Endiya Partners and strategic leaders from the healthcare sector.

The fresh capital will be used to drive SigTuple’s geographical expansion, broaden its product portfolio, and support regulatory clearances. This latest investment brings the company’s total funding to $50 million since its inception in 2015.

The AI-powered startup was founded by Rohit Kumar Pandey, Tathagato Rai Dastidar and Apurv Anand. In 2015, SigTuple raised $16M in a Series C funding, and previous to that had raised $19M. 

AI in Healthcare

SigTuple has been making significant strides in the digital pathology space. Its flagship product, AI100, which automates manual microscopic reviews using AI and robotics, has gained traction in the Indian market and expanded into Southeast Asia, the Middle East, and North Africa. The company is now poised to enter European and American markets.

In a major milestone, AI100 received 510(k) clearance from the US Food and Drug Administration (FDA) in September 2023, making SigTuple the third company globally and the first in India to achieve this for AI-assisted digital hematology.

Tathagato Rai Dastidar, founder & CEO of SigTuple said, “While we continue to build on the success of AI100 in India and abroad, 2024 will witness two new major product launches addressing a wide segment of the diagnostic industry, which will help make SigTuple a global brand coming out of India. We are truly excited to welcome SIDBI Venture Capital on board as the lead investor in this round. Their support is going to go a long way in making our dream of going global a reality.”

The company is set to launch two new major products in 2024. One is a next-generation device that will automate all manual microscopy in clinical labs, surpassing the capabilities of AI100. Additionally, SigTuple plans to enter the point-of-care market with a device leveraging microfluidic technology and imaging to conduct essential tests within minutes.

This funding round and product expansion plans underscore SigTuple’s commitment to revolutionising the diagnostic industry by making diagnostics decentralised and fully automating microscopic reviews of diseased samples.

Interestingly, in recent times there have been a number of AI developments in healthcare. In addition to big tech companies such as Google, Microsoft, Oracle, and others heavily investing in this segment. The companies are bringing LLM- based diagnostic measures in addition to other features to assist doctors. 

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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 AIM.

Bloq Quantum, an AI quantum software startup, has raised INR 1.3 Crore in a pre-seed round led by Inflection Point Ventures.

The funds will be used for product development and team expansion. This capital infusion will stimulate innovation in quantum algorithms, improve platform functionalities, and expedite growth.

Bloq Quantum simplifies enterprise adoption of quantum computing with its user-friendly low-code interface. It accelerates quantum algorithm development by tenfold, providing valuable business insights. Designed for users at all skill levels, Bloq Quantum streamlines the path to quantum computing.

Bloq Quantum excels as the foremost platform offering a user-friendly interface for developing quantum algorithms. Its key strength lies in the exceptional team, meticulously curated to lead the charge in advancing quantum computing technology. Their collective expertise and commitment drive Bloq Quantum’s continuous innovation, ensuring they deliver state-of-the-art solutions to our users and partners.

As of June 2024, Bloq Quantum operates globally, providing quantum computing solutions to clients worldwide. The widespread operations cater to diverse industries and users, emphasizing dedication to pioneering quantum computing innovations on a global scale. This global presence underscores the startup’s commitment to innovation and capability to address complex challenges.

Sreekuttan L S, co-founder & CEO, brings a strong physics background from IISER Pune and experience as a Product Manager at The Quantum Insider. Jay Patel, Co-Founder and CTO, combines his expertise in Computer Engineering and Quantum Machine Learning, honed at CERN, with three global Quantum Challenge awards and multiple quantum software prototypes. Together, they drive innovation in quantum computing with their complementary skills and deep industry knowledge.

“Enterprises face challenges in adopting quantum computing due to fragmented and complex algorithm development processes. Bloq Quantum aims to streamline this by offering a comprehensive solution that simplifies the creation of quantum solutions, providing a seamless experience for businesses,” Sreekuttan L S, said.

The post Bloq Quantum Raises INR 1.3 Crore To Accelerate Enterprise Adoption of Quantum Computing appeared first on AIM.

India’s public sector broadcaster Doordarshan recently introduced two AI anchors – Krish and Bhoomi – who deliver weather forecasts, commodity prices, farming trends, updates on agricultural research, and information on state welfare programmes to millions of farmers.

Enabling this is a Delhi-based startup called Personate AI. Incorporated in 2021, the startup is helping broadcasters, media houses and even content creators develop virtual AI agents.

Last year, Aaj Tak became the first broadcaster in India to host an AI anchor named Sana. The virtual anchor is multilingual and provides new updates multiple times throughout the day.

“We introduced India’s first AI anchor, Sana, at the India Today Conclave in the presence of Prime Minister Narendra Modi. Following that, we launched several anchors for various brands, including Vendhar TV in South India and ongoing campaigns for Zee. 

“Since then, AI anchors have been developed for multiple news channels, including Russian media,” Rishab Sharma, chief technology officer & co-founder at Personate.ai, told AIM.

The startup has developed eight different AI anchors for Aaj Tak and successfully launched them across its regional channels. Last month, Modi’s interview with the channel was also translated and broadcasted in seven hyperlocal languages using the startup’s technology.

Building on this success, the startup approached Prasar Bharati, which was impressed with its vision and chose to integrate AI anchors for DD Kisan. Sharma also revealed that AI anchors will soon be introduced for other Doordarshan channels, including DD Sports and DD News.

Personate.ai is also co-founded by Akshay Sharma, who serves as the CEO. Completely bootstrapped and profitable, the company has over 25 enterprise customers. 

Personate AI Studio

The startup has created an AI studio that allows users to produce a clone or a digital avatar of themselves. To date, the startup has collaborated with five media houses, achieving an average return on investment (ROI) of approximately 160%, according to Sharma.

“It’s adding more viewers per minute. Indian viewers have become accustomed to viewing AI content, be it a reel or a video shot on social media,” he said.

Personnate’s AI studio replaces the human component with a synthetic one. This synthetic element can be an actual human, or content creator or a synthetic personality.

For Aaj Tak, the startup also created a clone for their managing editor Anjana Om Kashyap. This was done by taking a short video clip, typically five minutes of the individual’s data. With AI, the clone was ready within minutes to read anything on screen with text-to-speech technology.

In contrast, creating a synthetic anchor involves designing from scratch. “This process includes pixel manipulation, designing the body, overlaying textures and clothing, and stitching the face. For a synthetic person, we spend about a week crafting the design,” Sharma revealed.

The personality is created with a 3D model by adding textures and shapes to the body. Once the model is ready, AI steps in to control its movements. 

Explaining in the context of video games, Sharma said that traditionally animators decided how the character moved. “Here, the role of the animator shifts to generative AI, which now directs how the character behaves and interacts,” Sharma said.

Large Vision Models 

Personate has developed a large vision model (LVM), which is a generative AI model similar to LLMs, but generates pixels instead of text. Examples of popular LVM include OpenAI’s Sora and Google’s Imagen.

The AI model translates a 3D anchor to a 2D screen, ensuring that the output is ultra-realistic. On a 2D screen, the challenge is how to rotate the model and adjust its positioning relative to camera angles, even though there is no actual camera. 

“The goal is for the model to behave as if it’s responding to the camera, creating a convincing and lifelike appearance,” Sharma said.

One of the biggest challenges in training an LVM is data. Moreover, models like OpenAI’s Sora are trained on trillions of data points. According to Sharma, Personate’s AI model too is trained with multi-trillion data points.

The startup tapped into the past experiences of the founders to collect data to train the model. Sharma revealed he began his journey with the Indian Space Research Organisation (ISRO) and later worked with Reliance.

Currently, Personate.ai is the only Indian company with such capabilities. Beyond India, Synthesia, a startup based in London, offers similar solutions.

Synthesia’s platform enables users to create videos using pre-generated AI avatars or by generating digital representations of themselves, which they call artificial reality identities. The startup is backed by NVIDIA and is being leveraged by the United Nations.

The post Meet The Brains Behind AI Anchors on Doordarshan and Aaj Tak appeared first on AIM.

Rabbitt.AI raised $2.1 million from TC Group of Companies. The genAI startup enables businesses to create and deploy advanced AI applications with tools for custom LLM development, RAG fine-tuning, and data-centric AI. Their platform features MLOps integration, voice bot AI agents, and prioritises privacy-first strategies in AI deployment.

Rabbitt.AI is founded by IIT-D alum and the recent funding round had participation from big tech executives including NVIDIA, Meta and Microsoft. “Smaller, custom and industry specific fine-tuned models are making more moves than one big model. At Rabbitt.AI we are the advocates of these and helping companies adopt Open Source AI models,” said Harneet S.N., founder and chief AI officer of Rabbitt.AI.

The London-headquartered startup has its majority development team based in India, with an office in Delhi. 

GenAI Services for Enterprises

Rabbitt.AI collaborates with enterprises to customise LLMs for specific use cases and develop AI applications using Generative AI models. One of the products being developed is a genAI-powered autonomous software engineer that can create production-ready software with no human intervention. This system auto-improves using Rabbitt’s proprietary agentic framework and fine-tuned LLMs.

“We are helping organisations own their data and own their AI. In the new world, we are helping companies become the landlords of the AI world rather than just the tenants like the past Internet world,” said Harneet, who is also a mentor and advisor at Delhi University’s Startup Incubation Fund and an official forum member at Confederation of Indian Industry (CII) Industry-Academia Partnership Forum. 

Rabbitt.AI also provides data annotation and curation services across domains including healthcare, education, marketing, and customer relationship management.

Interestingly, Rabbitt.AI claims to outperform services offered by OpenAI and have claimed to achieve 25% more annotations, 30% more success, and 15% more clients than OpenAI. 

Source: Rabbitt.AI

The post GenAI Startup Rabbitt.AI, Founded by IIT-D Alum, Raises $2.1M appeared first on AIM.

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 AIM.

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 AIM.

AWS India Private Limited has unveiled a new initiative in partnership with Tamil Nadu Technology (iTNT) Hub to establish a generative Artificial Intelligence (AI) startup hub program. This program aims to accelerate the development of generative AI solutions for public-centric initiatives through Tamil Nadu’s startup ecosystem.The program will facilitate collaboration between startups and industry to create public sector-focused solutions using generative AI.

It will target startups working in AI, generative AI, and deep-tech domains, with a focus on applications for government, healthcare, education, and non-profit sectors.iTNT Hub, established by the Ministry of Electronics and Information Technology (MeitY) and the Information Technology and Digital Services Department (IT&DS) of Tamil Nadu, is located at Anna University in Chennai.

It aims to create a deep tech innovation network in Tamil Nadu by leveraging the combined strengths of startups, innovators, academia, government, and industry leaders.

Key features of the program include:

1. Support for startups at various stages of development, from pre-incorporated teams to mature startups.
2. Mentorship for founders from over 570 engineering colleges affiliated with Anna University.
3. Access to research opportunities, sectoral guidance, and funding avenues for startups in incubation.

AWS will provide eligible startups with up to $10,000 in AWS credits, allowing them to experiment with over 240 fully featured services, including innovative generative AI solutions like Amazon Bedrock, Amazon Q, and Amazon SageMaker.

The company will also explore onboarding startups to the AWS Partner Network (APN), subject to eligibility criteria. iTNT Hub will offer additional support through:

• Technical expertise and mentorship
• Guidance on business and funding fundamentals
• Industry connections to understand market opportunities

The program will feature webinars, technical workshops, masterclasses, industry connects, roadshows, hackathons, and customer engagements. A steering management committee comprising executives from AWS India and iTNT Hub will govern the program.

This collaboration aims to strengthen the entrepreneurial environment for startups across Tamil Nadu, with a particular focus on empowering those in locations with limited access to resources. By leveraging AWS’s global technology leadership and iTNT Hub’s local expertise, the program seeks to foster innovation and growth in the generative AI space while addressing public sector needs.

The post AWS and iTNT Hub Collaborate to Nurture Generative AI innovation Among Startups in Tamil Nadu appeared first on AIM.

The Indic data problem has been on every Indian AI researcher’s mind. One of the primary challenges in developing AI for Indian languages is the scarcity of high-quality data. Unlike English, which has a vast amount of digital content available, Indian languages lack sufficient natural data to train AI models.

“For Indian languages, or any language other than English, this has not been true [availability of data online], because of which we do not have a large amount of natural data,” Vivekananda Pani, the CTO and co-founder of Reverie Language Technologies, told AIM.

Various approaches are being considered to address this data problem. One involves using artificial methods to augment existing data, such as converting English data into Indian languages using machine translation. 

However, Pani cautioned against relying solely on this approach. “The best way is not, let’s say, if we try to augment and continue to augment forever… because in the English language that’s not likely to happen,” he said.

Building a Rocket for Indic Languages

Pani advocates for a more sustainable solution: enabling people to create a large amount of natural data that can be fed back into AI systems. This includes leveraging content from various media formats, such as audio and video, which can be transcribed and used to train AI models. 

Speaking about the need for building something like ChatGPT in India, Pani gave the perfect analogy. “Scaling the moon is actually a lot of distance. If you walk, it will take 25 lives, versus if you build a rocket today, you will reach it in a month.”

One problem that Pani pointed out is the standardisation of the Indian language in the digital world. Giving the example of the word “sahayogi”, Pani and his team explained that there are several ways in which people type the word on their phones. 

This is also because there is no uniformity for Indian languages across different types of keyboards, while English is uniform across the board. 

“Even if somebody doesn’t know English, but wants to send a message on WhatsApp, they tend to write the message in their native language using English letters,” Pani explained that it is only for the digital world, but when people write in books and letters, it is usually in native scripts. 

Earlier while speaking to AIM, Raj Dabre, a prominent researcher at NICT in Kyoto, discussed the complexities of developing chatbots for Indian languages. If you type something in Hindi but in the Roman script, current AI models like Llama and others are able to process it to some degree. 

Dabre and his team are working on training models in Romanised versions of Indic data, leveraging the knowledge in the English language, and transferring it to Indic languages.

“Technology has actually been a barrier in our case,” said Pani. He explained that this is something that needs to be addressed very quickly. 

Working on the same issue is Pushpak Bhattacharyya, who was recently appointed as the chairman for the committee for standardisation in artificial intelligence, set up by the Bureau of Indian Standards (BIS)

Bhattacharyya argues that while existing LLMs can be adapted for Indian languages, creating specialised foundational models could lead to significant efficiency gains. Similarly, smaller models with less amount of data would also not be feasible in the longer run. 

Native Languages for AI is a Need

At Reverie Language Technologies, Pani explained that the team has been working on this problem for a very long time, and building that ‘AI rocket’. 

“We started in an era when there was absolutely zero Indian language data in the digital media,” he recalled, highlighting the progress made from their first speech model using only 100 hours of data to more recent models utilising at least 10,000 hours.

“In India, we still have less than 7% people who are fluent in English,” Pani explained that there is definitely a need for building an AI model in Indian languages, and not just relying on the models by the West.

Pani also addressed the startup scenario in India for fundamental research in AI. He notes that while historically, there has been a lack of investment in long-term research projects, the landscape is changing. “Now that OpenAI showed the world what is possible, people have a belief that this can be achieved and therefore let’s go and invest,” he observed.

Another issue is building hardware in India, which Pani explained is a lot harder. “On that front, we are far behind right now. We don’t have enough skills. We will have to import skills and it’s a very, very expensive process,” he said. 

Agreeing with Bhavish Aggarwal’s recent comment on comparing OpenAI with East India Company, Pani said that a lot of data that Indian users “donate” to companies such as Google through their phones is kept with the company and is only accessible to Indian researchers if they pay for it. 

“When you look at countries such as China and Japan, they have their computing world in their native language,” Pani added, saying there needs to be a bigger push from the government for putting standards and baseline fundamentals. 

“These fundamentals today are actually governed by a few American companies and formed by them. So they would not really think of what is right for our country,” Pani concluded.

The post Vivekananda Pani on Building ‘AI Rocket’ for India appeared first on AIM.

Index Ventures, a prominent venture capital firm, has successfully raised $2.3 billion to invest in artificial intelligence (AI) and other transformative technologies. This significant capital infusion is divided into two primary funds: an $800 million venture fund and a $1.5 billion growth fund.

The announcement comes at a pivotal moment for the startup ecosystem, as AI continues to drive innovation across various sectors.

The new funds aim to support startups at different stages of their development, from seed funding to initial public offerings (IPOs). Index Ventures’ strategic focus on AI reflects the firm’s belief in the technology’s potential to revolutionise industries ranging from healthcare to finance.

The venture fund will target early-stage companies, providing them with the necessary resources to develop and scale their innovations. Meanwhile, the growth fund will support more mature startups, helping them expand their market presence and achieve sustainable growth.

Strategic Focus on AI and Transformative Technologies

Index Ventures’ decision to raise these substantial funds underscores the growing importance of AI in the global economy. The firm has a history of backing successful tech companies and aims to leverage its expertise to identify and nurture the next generation of AI-driven startups. By investing in AI, Index Ventures hopes to accelerate the development of cutting-edge solutions that can address complex challenges and create new opportunities.

The announcement has been met with optimism within the tech community, as it signals a robust commitment to fostering innovation and supporting entrepreneurial ventures. With AI poised to play a critical role in shaping the future, Index Ventures’ substantial investment is expected to have a far-reaching impact on the startup landscape.

As the firm embarks on this new chapter, it remains dedicated to its mission of empowering visionary entrepreneurs and driving technological progress. The $2.3 billion raised will enable Index Ventures to continue its legacy of supporting groundbreaking startups and contributing to the advancement of AI and other transformative technologies.

The post Index Ventures Raises $2.3 Billion to Fuel AI Innovations appeared first on AIM.

Indian VCs have a secret—it’s called ‘dry powder’ and it’s just waiting to be invested. Dry powder is essentially money that’s not tied up in other investments and can be quickly deployed when the right opportunity comes along.

Rajan Anandan, the managing partner at Peak XV Partners, recently spoke about this. He sees potential for growth and innovation, and foresees India making a significant impact on the global stage. 

At the Global IndiaAI Summit, Anandan said, “Our firm has over INR 16,000 crore of dry powder. We just want more people starting up in AI.” He added that Peak XV has invested in 25 AI startups so far.

Emphasising that capital availability is not a problem, Anandan mentioned that the venture capital ecosystem has $20 billion ready to be invested in Indian startups. He also noted that AI is currently the most significant theme, with investors showing strong enthusiasm for startups in this field.

Recently, Speciale Invest also hosted DevCon, with 50+ technical founders and engineering leaders. Even Stellaris Venture Partners recently hosted an AI Agents Hackathon, in a bid to attract a lot of talent in the space.

It’s only a matter of time before the $20 billion dry powder is ‘snorted’ by the AI companies of India, as every VC is slowly turning into an AI-focused investment firm. 

The Mystery Behind the $20-Billion ‘Dry Powder’ 

This brings up the question: Where is all this money coming from?

Kyle Stanford, a senior VC analyst at PitchBook, noted that a large portion of the capital invested in high-end venture deals comes from asset managers like mutual funds, hedge funds, and private equity funds. 

“A lot of recently closed VC funds have been holding onto their capital and waiting for the market to bottom out, or they have a better sense of what the pricing of these deals should be,” he said.

When it comes to India, according to data from research firm Preqin, PE/VC dry powder in India increased to $15.6 billion by March 2023, up from $12.8 billion in 2022 and $11.1 billion at the end of 2021. 

Seventy India-focused PE/VC firms closed funds in 2022, raising an aggregate of $8.5 billion — the highest-ever annual fundraising value, Preqin said.

Strong fundraising in the first half of 2022 also contributed to these substantial dry powder reserves. Global venture firms amassed a total of $223.6 billion through Q3, with most of the capital coming from the first two quarters. If this pace continues, it will surpass the total capital raised in 2021, according to PitchBook data.

According to a Bain & Co report, the funding from the investors’ side has seen a significant decline since 2021, with annual funds dropping from $40 billion in 2021 to just $4 billion till June 2024. 

Moreover, the share of fundraising by top investors varies significantly each year, peaking at 85% in 2019 and dropping to 4% in 2023. There is a notable increase in total funds raised in 2022 compared to other years, and a significant drop in 2023. 

This means that the fund accumulated in 2022 is still kept by the investors, adding the cumulative money from all the years.

Meanwhile, data from the Q3 2022 PitchBook-NVCA Venture Monitor shows that through Q3 2022, the total value of venture deals involving nontraditional investors reached $145.1 billion, representing about 74% of all US VC deals. 

This group of investors participated in around 92% of mega-sized venture deals in 2021.

A key factor enabling global VCs to maintain and grow their record amounts of dry powder is the significant involvement of nontraditional VC investors. 

This additional capital, which isn’t counted in the VC dry powder figures, has increased the funds available to startups and helped expand the asset class beyond what is accessible to traditional VC firms.

More Funds Needed in India

When speaking with AIM, Arjun Rao, managing partner at Speciale Invest, said that the investors can’t give funds of $10 million or more because that is around 20% of the total fund size for many.

“We probably need more funds that are of larger size. The gap is in the VC market itself and hopefully some of the homegrown funds that have started in the past 10 years are growing by them having done well in the past cohorts. Their AUM is growing and therefore they can write large checks,” said Rao.

Rao also explained that one of the reasons for such a small cycle of funds is the time horizon of their LPs. “We are sort of bound by that. It’s like, ‘Hey! I got to invest, see the company, build, and grow and then make returns and then return the capital to our investors within a meaningful time horizon’,” he added.

Many believe that Indian investors are risk averse. But that is not the case according to Anandan as Peak XV has already invested in two semiconductor companies, a space tech company, and a hydrogen recycling company, proving their risk-taking capabilities.

While the term “funding winter” has been floating around, Anandan’s optimism provides a refreshing contrast. Despite the challenges, the availability of such a substantial amount of dry powder ensures that promising ventures won’t be left out in the cold.

The post Who is Snorting the $20 Billion Dry Powder in India? appeared first on AIM.

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 AIM.

Founded in 2017 by Ganesh Gopalan and Ananth Nagaraj, Gnani.ai is a Bengaluru-based startup that claims to facilitate over 1 million conversations daily with its product line, which is meant for contact centres.

The company has over 100 customers in banking and financial services, insurance, telecom, automotive and healthcare industries.

In May, the startup launched a series of voice-first SLM (small language models), trained meticulously on millions of audio hours of proprietary audio datasets and billions of Indic language conversations.

“What we’re doing is something different, like a fusion of voice and text models. It’s a multimodal model but right now we are focused on voice and text,” Ganesh Gopalan, the CEO of Gnani.ai, told AIM in an exclusive interview.

So far, the company has built a series of five models designed for the banking, finance, security and insurance (BFSI) sector.

Gopalan revealed that the models are multilingual. In the US, it supports both English and Spanish, whereas, back home, the model is designed to support 12 Indian languages.

“We also plan to launch a model designed for the automotive industry because we have a lot of customers in this industry. Healthcare is going to be a sector in the future,” Gopalan said.

Building for the Edge

The size of SLMs developed by Gnani.ai are relatively smaller compared to an LLM like GPT-4 or even smaller LLMs like Llama-3 7 billion parameters. Gopalan believes the size of SLMs will come down even further, enabling the deployment of them on the edge.

“In the future, we will deploy these models on the edge because the size is also coming down drastically. We believe the solution to many enterprise problems isn’t always found in the generalised 100 billion+ parameter models that companies often tout. 

“These models are excellent for generic applications but may not always address specific enterprise needs effectively,” he said.

Moreover, many enterprises hesitate to adopt third-party models due to concerns about their proprietary nature, uncertainty about the training data used, and security apprehensions. Running a model on the edge where the customers’ data are solves a lot of these problems.

“We think running models on the edge will be a lot cheaper. So, at some point, all these models will be on edge. And that’s something that we are working actively towards,” Gopalan said.

What Gnani.ai considers its strength is the ability to quickly fine-tune the model based on enterprise data and make it production ready. 

“It’s one thing to have an SLM for the BFSI sector, but the real value to a company is when you have a model built just for their data. So that’s what we do. We take our model to enterprises and help them build on top of it. We provide them with necessary tools that can quickly launch the model based on their data,” Gopalan said.

AI Agents are Coming 

Today, AI agents are believed to be the next big iteration in the AI cycle. Previously, Kailash Nadh, the CTO of Zerodha, have said the prospects of having AI agents are very high but perhaps not in a nice way.

While Gopalan agrees AI agents will be the next big leap, he adds that Gnani has been building AI agents for over four years. 

“We automate processes that are done by contact centre agents through our voice bots. We built AI agents that helped one of the largest banks in India in collecting over a billion dollars in the last six months,” Gopalan said.

The bot handled payment reminder calls to customers, ensuring timely payments and helping them find the appropriate payment methods. 

“For a US client, our AI agents are assisting the contact centre agents by providing instantaneous answers to customer queries as conversations unfold,” he added.

AI agents will also change how contact centre operations work. The future is multimodal, according to him. “ For instance, if you encounter an issue with your laptop today, calling the contact centre requires providing a tag number and describing the problem, which can be challenging. 

“Instead, you can show a video, and an AI bot can analyse the visuals, identify the problem area, suggest solutions, and present options to the agent. The agent, using human intelligence, can then assess the problem and provide a solution,” he said.

IVR, Biggest Impediment to Customer Experience 

However, there is a lot of apprehension about AI agents making contact centre agents redundant. Gopalan believes there is a long way to go before AI makes call centre agents redundant, if it ever happens. 

“We currently deploy AI bots for numerous use cases. However, systems are still evolving for scenarios that necessitate human intelligence or empathy. In these areas, AI bots are not ready yet,” Gopalan said.

Moreover, he believes interactive voice responses (IVR) could be the biggest impediment to customer experience. 

“No one enjoys navigating through automated menus and pressing buttons when contacting a contact centre with an urgent issue,” he pointed out.

Gopalan believes AI will have to bring an end to IVR before it eliminates jobs of call centre agents. Contact centre business is one segment which is being impacted by generative AI, and discussions around AI making contact centre agents redundant is widespread.

“Another reason contact centres will remain relevant for a long time is that it’s not just about having people to answer calls. Many companies lack the integrations with CRM systems, ticketing tools, and other necessary infrastructure. Additionally, a significant portion of customer service knowledge resides within the minds of contact centre employees,” he concluded.

The post Bengaluru Startup Builds AI Models That Contact Centres Can Run on the Edge appeared first on AIM.

Everyone hopes to build an AI startup. After leaving OpenAI, Ilya Sutskever has started his own venture called Safe Superintelligence. The startup is bound to raise billions of dollars in pursuing its goal of building ASI, but what about Indian startups that are aiming to do fundamental AI research?

Speaking with AIM, Soket AI Labs founder & CEO Abhishek Upperwal revealed the numbers required to build a research-based AI startup. So far, the company has already built Pragna-1B, which is a foundational model specifically for Indic languages.

Upperwal said that, currently, funding is just enough to make-do for AI research within a startup. “Yes, there are fewer funds that are available as compared to any foreign markets, but I also believe that we can maybe make-do with that particular fund and then ultimately grow in scale after the seed stage,” said Upperwal. 

He explained that for the seed stage in India, a funding of $5 million or $10 million is still a decent amount. This roughly translates to around INR 40-50 crores. This is still very little when compared to the 100s of millions raised by companies in the West.

“If VCs can trust these companies in the generative AI space, we can definitely do wonderful stuff for sure,” added Upperwal.

Where Does the Money Go?

The gap in funding is because of the market. India is a smaller market, therefore the ticket sizes are still way smaller compared to the West. But Upperwal said that the ticket sizes being small pose a problem when compared to the work that startups have to do at the foundational layer. 

Upperwal gives the example of building an estimated 7 billion parameter foundation model out of India. He said that the cost for the compute alone would be close to $2 million. For reference, one NVIDIA H100 costs around INR 30 lakhs, or $36k. 

To build a 7 billion parameter model, considering a six month time frame, a startup would require at least a dozen NVIDIA GPUs for the training period, taking into account time for other factors like inefficiencies.

This is while considering that the model is built in one shot. “It takes a lot of experiments, and checkpoints, or the path that you are taking fails, so you need to rebuild from the previous checkpoint,” explained Upperwal. 

Earlier, Upperwal had told AIM that it took the company six months to train the Pragna model, which involved many experiments with different models and a total of 150 billion tokens. It took close to 8000 GPU hours on NVIDIA A100s to train the model.

Accounting for all of these, an ideal amount to do a lot of foundational work in AI at the seed stage is anywhere around USD $7-15 million, which is close to around INR 125 crore. 

All of this is including the cost of running the business such as hiring talent and paying bills, and does not include the cost of making the models ready for production or inference. That would increase the funding requirement to, at least, more than double.

In the same conversation, Speciale Invest founder and partner Arjun Rao said that Indian VCs are interested in investing in the development phase, more than the research phase of AI. It would take a lot of time to research, build a model, compare it with others, and then figure out how it can be commoditised, which is something VCs are still figuring out. 

Assuming they’re working with a team of eight people and a funding of approximately $10 million (or INR 82 crore), an AI startup in India can survive for about 2.33 years at the current estimated monthly expenditure of INR 2.92 crore, which does not include inference costs.

Assuming that the $2 million as Upperwal mentioned above goes towards compute just for training a 7 billion model for around six months, it still would account for 80.2% of the expenditure for 2 years, with the rest going towards salaries and other expenditure. 

This calculation assumes that the monthly costs remain constant and does not account for potential increases in expenses due to scaling, inflation, or other operational changes.

How Much Are Indian Startups Getting?

While these numbers seem reasonable, they are comparatively lower when looking at the global standard set by OpenAI, Anthropic, or Mistral, who have raised billions of dollars. 

For reference, Sarvam AI, which has announced its intention of building foundational AI models, has raised a total of $41 million. Pranav Mistry-led and Reliance-backed TWO.AI has raised $20 million for building their SUTRA line of models, and Krutrim raised $50 million becoming India’s first generative AI unicorn.

This is just for initial research. SML CEO and founder Vishnu Vardhan emphasised the huge investment required to build and scale complex AI models. In an exclusive interview with AIM, Vardhan disclosed his plans of raising $200-300 million for the same. The company only recently launched Hanooman, its own foundational model. 

“That’s the kind of money we need to launch this kind of a product. We’ve already spent tens of millions of dollars, but that won’t work,” he said, about building a GPT-5 level model in India. 

Even if you add the total funds raised by companies in India, it’s still nothing when compared to OpenAI raising billions single-handedly. It definitely would cost a lot more to build an AI startup in India to compete with the West.

The post How Much Does It Cost To Build an AI Research Startup in India? appeared first on AIM.

AWS has announced the launch of the new AWS Generative AI Spotlight program in Asia Pacific and Japan (APJ). This is a four-week accelerator program to support early-stage startups in the region that are building generative AI applications.

In India, the company is partnering with the venture capital firm Accel for this program. Last year, the company and Accel launched ML Elevate 2023, a six-week accelerator that supported 35 generative AI startups in India. 

The program will select up to 120 early-stage startups across the region, including 40 from India. 

For example, with help from AWS, fintech start-up Fibe has improved customer support efficiency by 30%. 

The participants will also have access to the company’s Activate program for startups. They can receive up to $100,000 in AWS credits.

The company’s Generative AI Spotlight program in APJ is collaborating with venture capital firms and organisations in key cities across the region. 

 Generative AI Accelerator

Additionally, the company has announced a $230 million commitment for generative AI startups to accelerate the creation of generative AI applications worldwide. This funding will provide early-stage companies with AWS credits, mentorship, and education to further their use of AI  and ML technologies.

A major portion of the commitment will fund the second cohort of the AWS Generative AI Accelerator program. This 10-week program will provide hands-on expertise and up to one million dollars in AWS credits to each of the top 80 early-stage startups using generative AI to solve complex challenges. 

The program will identify top startups in areas such as financial services, healthcare, media, entertainment, business, and climate change. Participants will receive sessions on ML performance enhancement, stack optimisation, and go-to-market strategies, along with business and technical mentorship based on their industry vertical.

AWS’s Support for Startups

Matt Wood, VP of AI products at the company, stated, “With this new effort, we will help startups launch and scale world-class businesses, providing the building blocks they need to unleash new AI applications that will impact all facets of how the world learns, connects, and does business.”

AWS has a long history of supporting startups, with 96% of all AI/ML unicorns running on its platform. The new commitment aims to further accelerate the growth of generative AI startups by providing them with the necessary resources and mentorship.

This is not the first time AWS has committed to helping startups, last month AWS collaborated with Shellkode, a cloud company, to train one lakh women developers in generative AI.

The post AWS Teams Up with Accel to Support GenAI Startups in APJ appeared first on AIM.

Text-to-speech (TTS) models are comparatively easier to make in English than in other languages. To fill this gap, IIT Guwahati alumni Sudarshan Kamath and Akshat Mandloi started smallest.ai, and decided to create one for Hindi as well. They call it AWAAZ.

With state-of-the-art Mean Opinion Scores (MOS) in Hindi and Indian English, AWAAZ can fluently converse in over ten accents, reflecting the diverse linguistic landscape of India.

The inception of AWAAZ was driven by the founders’ recognition of a gap in the market for high-quality, affordable TTS models for Indian languages. “When we started building, we realised that the models required for a voice bot were not mature for Indian languages. Existing models for non-English languages were nowhere close to production,” explained Kamath in an exclusive interaction with AIM.

Citing OpenAI’s GPT-4o, which is a generalised model, Kamath said that the company aims to build specialised models that can be tailored for customer support, even for small business. It is also cheaper than other Indian language TTS models, such as Veed.io and Murf.ai.

Janta ki AWAAZ 

AWAAZ stands out for its single-shot voice cloning capability, which can replicate a voice from a mere five-second audio clip. The model also boasts a low streaming latency of just 200 milliseconds. 

To make this technology accessible, smallest.ai has set an introductory price of INR 999 for 500,000 characters, positioning AWAAZ as a cost-effective solution, claiming to be ten times cheaper than its competitors, such as ElevenLabs. 

Kamath said that the language model is about 750 million parameters in size, leveraged using existing open source models.

Kamath attributes the affordability of AWAAZ to their focus on data quality and model efficiency. “Our model is much smaller than those of competitors like ElevenLabs. Despite this, we achieve high-quality speech because our data is highly refined,” he explained. 

smallest.ai uses AWS for cloud services, although they remain flexible about potential future partnerships.

The Dataset of AWAAZ was the Critical Part

Kamath and Mandloi launched smallest.ai in October 2023. The initial goal was to create a voice bot for India capable of qualifying leads and handling customer support. This led to the development of SAPIEN, a voice bot for sales, marketing, and customer support. 

However, the lack of robust TTS models for Indian languages led them to focus on core model development, resulting in the creation of AWAAZ. “The data quality for TTS models reduced drastically when we moved away from English to other languages. It is worse for South Asian languages,” said Kamath.

The Indic data problem has been highlighted several times by researchers when speaking with AIM, be it for text or voice models. 

“We spent a lot of time perfecting the dataset, using over thousands of hours of audio from various people from different states in India. We focused on data quality to ensure a diverse representation, making our model suitable for production-level deployment,” Kamath said. 

The team invested significant resources into this endeavour, with over six months dedicated purely to the development and iterations of data quality.

AWAAZ is currently limited to Hindi and Indian English, but Kamath emphasises the importance of understanding the quality of the output. “The most difficult part is the data. If you tried our model in Tamil, it might respond a little, but we don’t advertise that capability because it’s not up to our standards yet,” he said. 

Way Forward

The company’s ambitious roadmap includes expanding the model’s capabilities. “Our next step is moving closer to GPT-4o-like abilities for Indian languages, where the model can generate answers with a voice, enhancing the interactive experience,” Kamath revealed. 

Additionally, smallest.ai is exploring the development of voice-to-voice models, aiming to offer custom solutions for specific business needs such as lead qualification and customer support.

The founders are committed to AI’s understanding of multimodal data. 

“We’ve been fascinated by AI’s potential to understand more than just text. Speech is one of those areas where AI can truly start to seem human, much like in the movie ‘Her’,” Kamath said, reflecting on the broader vision that drives their work.

The post Meet the Creators of India’s AWAAZ appeared first on AIM.

Cred founder and CEO Kunal Shah wasn’t exaggerating when he said India was now ready to build products for the world. 

“Earlier, I used to think we were not really ready for that, but I feel now there is enough evidence that we might be able to create something interesting for the world,” said Shah in a recent interview. 

Recently, Jivi’s purpose-built medical LLM Jivi MedX ranked number 1 on the Open Medical-LLM Leaderboard, outperforming OpenAI GPT-4 and Google’s Med-PaLM 2. 

Meanwhile, Indian founders started QX Lab AI, which recently launched the Hybrid GenAI Multimodal Platform ‘Ask QX PRO’. Similarly, Quizizz supports millions of students in over 150 countries.

The list goes on. 

Most recently, two Indian engineering students, Rudransh Agnihotri and Manasvi Kapoor, launched Mayakriti, an image generation platform that uses advanced GenAI to create lifelike visuals — from photorealistic portraits and personalised creations as well as in a variety of art styles such as cartoons, anime, and abstract art. 

Rudransh, a third-year mechatronics engineering student from Delhi Skill and Entrepreneurship University, and Manasvi, currently in his second year, pursuing electronics and communication engineering, with a specialisation in AI and ML, from Netaji Subhas University of Technology founded FuturixAI and Quantum Works, a startup focusing on AI research and innovative solutions. 

Mayakriti is a ‘Made in India’ product that utilises research, including papers such as Git Re-Basin and Arcee’s MergeKit, and concepts from mathematics and physics to create high-quality images without requiring the same amount of resources and computing power as needed by other popular models. 

“To train something like GPT-4 in India is not possible in the foreseeable future due to the limitations of compute and training parameters. Even models like DALL·E 3, Midjourney, and Imagen require enormous training parameters. Since we are a research lab, we worked on the math and used techniques like SLERP in diffusion modelling to develop Mayakriti,” said the founders in an exclusive interview with AIM.

SLERP, or Spherical Linear Interpolation, is used to interpolate between the parameters of two models in a spherical space, ensuring smooth transitions and effective merging of models. 

They highlighted that this technique has been used in video games and graphic rendering for some time now, but recent research showed its use in blending a model’s parameters into another so that the properties of both models remain.

“This is how we are able to give image generation qualities that are better than existing models and are not limited by the compute resources in India,” said Agnihotri, who believes that Indians have a higher than average math intellect and should focus on research to build models that can perform the same as the bigger models in fewer parameters.

AdapterFusion, which integrates multiple task-specific adapters into a base model, retaining its general capabilities while improving performance on specific tasks through lightweight modifications, is another technique that was used. 

Anuvadini CEO Chandrasekhar Buddha said that the company, which is a Section 8 non-profit company under the Ministry of Education, recently provided FuturixAI and Quantum Works with NVIDIA A100 GPUs on Microsoft Azure Cloud network. Currently, Mayakriti is using 8 NVIDIA A100 GPUs (80 GB each) for deployment.

“I want to let people know through Mayakriti that if we are making that possible in just a few A100 GPUs by research and utilising core concepts of physics and math, then other Indians can also develop models as good as the big players by just focusing on the fundamentals,” said Agnihotri.

Image Generation Using Mayakriti

Apart from using existing open-source datasets, the founders also developed their own datasets. “In our colleges, our friends are graphic designers, so we asked them to go over the internet, collect some images and label them according to the prompt. This way, we collected up to image data from 3,000 images, and then multiplied that to the base variant,” they explained.

Mayakriti sets itself apart with its focus on hyper-realistic outputs and a wide range of customisable art styles. It employs several separate models and each model has its own speciality, from generating anime to creative arts. 

Apart from letting users write their prompts, the platform also lets them choose resolution, environment, lighting, camera settings, composition, and style for the images.

It also comes with an in-built image editor, which the founders plan to improve by adding features, including the option to add text, which can allow users to create instant posters. 

Agnihotri was once a JEE aspirant who couldn’t make it to an IIT, but that setback didn’t reduce his love for math. Now, with FuturixAI and Quantum Works, the young founder aims to push forward research in the AI field in India making use of research in math and physics. 

“Google has the capability, dataset and compute, but at the same time, we have our own methods that are evolving with time,” he said. 

In an attempt to make Mayakriti better than the image generation offered by Google and OpenAI, the founders plan to make the product free in the coming months for people to try globally and provide feedback. 

Further, in the near future, the startup has another interesting product in the pipeline. They aim to release an AI platform that will let users train their ML models by uploading just a single dataset. 

This is just the beginning and, soon in the future, we’ll see more such startups building from India, for the world! 

The post Meet the Young Indian Founders Building AI Products for the World appeared first on AIM.

Looking at the dire need to build AI in India, by India, and for India, GreyOrange AI research scientist Guneet Singh Kohli went on a unique journey. He began working on Hugging Face’s Data is Better Together initiative in partnership with Daniel van Strien from Hugging Face and as the first step introduced Sanskriti Bench.

The aim of Sanskriti Bench is to develop an Indian cultural benchmark to test the increase of Indic AI models. By crafting a benchmark with the help of native speakers from different regions across India, the initiative aims to take into account the country’s cultural diversity. 

The initiative is also being built with the help of Silo AI’s Dr Shantipriya Parida, who also created Odia Llama, Anindyadeep Sannigrahi from Prem AI, and Dr Kalyanamalini S, who is the language expert from Odia Generative AI.

Talking about the project with AIM, Kohli said that the most important and unique part about this project is that all of the data is novel, which means that it’s created by Indians from across the country to ensure diversity, accuracy, and quality of data. He said that this is not available in other datasets in Indic languages, which are essentially translations taken from English.

Apart from this, Kohli recently also partnered with GitHub and Save the Children to build AI tools for child safety and is preparing an AI system that can catch people who attempt to groom children online. “I write research papers, but eventually, there is no use if you can’t implement it for the people,” said Kohli.

The eventual goal Kohli has with this is to set up a global AI for Child Safety Lab, ahead of which he hopes to collaborate with several psychologists. “In India, children are using a lot of social media, and it becomes important for the country to also start talking about these,” he said, highlighting the importance of more conversations in the country as these are lacking when compared to the US and Europe. 

The First Phase is Going Strong

According to the roadmap that Kohli laid out, the project is in its first phase. Currently, he is creating questions to build a dataset for benchmarking LLMs, which will then be hosted on the Hugging Face leaderboard.

In order to create the perfect questions, Kohli has taken the help of friends from different parts of the country. He gave an example of one friend from Bihar who provided questions in his native language, Maithili, along with the answers. However, the problem he highlighted was that these LLMs had a very big problem understanding context.

“We asked a question to an AI model about a festival from Bihar for which it was able to answer correctly with all the historical accuracy and the reasons for celebrating it. But when asked about more context on the festival, the model related the whole festival to Odisha,” said Kohli. 

He explained that even though we can correct this by using our own knowledge of the culture, what about researchers who are using LLMs for research of Indian culture? “They would get it completely wrong,” said Kohli. Similarly, he highlighted how different states contribute to the country, like how Punjab is famous for agriculture and Gujarat is famous for driving the economy. All of this needs to be represented in the AI models as well with proper attribution.

To ensure these LLMs have geographical, cultural, historical, proverbial, and demographical knowledge about each part of the country in its native language, Kohli has started preparing the dataset.

He is working with several volunteers from Kashmir, Punjab, Kerala, and Assam to integrate the knowledge of each region into the dataset. “I am pushing for the idea that it needs to be completely human-driven,” he added, saying that he does not want to use synthetic data for creating questions as the foundation.

Currently, he is aiming for 500 questions per language and per region of the country, starting with 10 languages, which can be augmented using language models in later versions. “The beautiful part of India is each region has a unique language, which would make it diverse in itself,” said Kohli.

He is also working on incorporating figurative language, like the language used in proverbs, poems, similes, and other expressions, which are unique in each part of the country.

Indian Researchers Need to Get Together

With a BTech from Thapar Institute of Technology and working with a lot of non-profit organisations, Kohli’s motivation has always been to make technology work for the people. That is why he started working on the idea in 2020 with Cord.ai.

“I don’t want to call it my project. I eventually want to call it ‘by the people for the people’,” said Kohli, emphasising the fact that initiatives like these would be able to set up a benchmark created by Indians for anyone building AI models in the country, even if they are coming from outside the country.

“If anyone is creating an Indian model, it should be able to handle Indian culture,” said Kohli, highlighting that all the models coming up in the Indic AI space claiming to be the best need to be evaluated.

Talking about OpenAI coming to India and other Indian-based AI offerings such as Krutrim, Kohli said that it is important for researchers who are building different AI models in different languages to come together. He said that as the phases are completed, the initiative will be part of the community and everyone contributes to it.

Also, speaking about the recent launch of the Cohere Aya model in multiple languages, including Hindi, for which Kohli was one of the reviewers of the paper, he said, “If people from outside India can also do it for Indian languages, we being in India, why can’t we do it?”

The post Meet the Creator of Sanskriti Bench, Building Cultural AI for India with Hugging Face and GitHub appeared first on AIM.

Infosys co-founder Narayana Murthy recently said that Indians are good at applying ideas generated elsewhere for the betterment of the nation. He also added that it would take time for the country to invent new things.

“There are going to be APIs and people are going to use them. That’s the way things will get built. It’s not bad to be a wrapper, it’s just that you shouldn’t be a shallow wrapper. You have to think about the value you’re adding on top of the model,” said Google CEO Sundar Pichai.

India might have arrived late to the AI party, but the future of AI in India is not that bleak. With an increase in investments, more initiatives like AI4Bharat, and industry and academia partnerships to bolster research in the country, India can definitely up its AI game!

Top GenAI Indian Startups in 2024

Here are 12 Indian startups that are leading the GenAI wave in India. 

1. KOGO AI

Bengaluru-based deep tech startup KOGO AI has developed a platform that helps companies build AI agents that can converse in Indic languages. Using the platform, companies can build an AI agent from scratch within minutes.

Initially, these agents will be able to support conversations in Urdu, Hindi, and English, with plans to include another 73 languages, both Indian and global, soon.

For this, the Bengaluru-based startup has partnered with Bhashini, the Indian government’s initiative aimed at breaking language barriers in India, and Microsoft to make the agents multilingual.

2. Sarvam AI

Established in July 2023, Sarvam AI was co-founded by Vivek Raghavan and Pratyush Kumar to make generative AI accessible to everyone in India at scale. 

“We think this is a foundational technology, and we don’t want India to become solely a prompt engineering nation,” said Raghavan in an exclusive interview with AIM. 

The company has raised $41 million in its Series A funding round led by Lightspeed Ventures with participation from Peak XV Partners and Khosla Ventures.

Last year, Sarvam AI also open sourced OpenHathi, an Indic Hindi LLM built on top of Llama 2. On Hugging Face, the model has been downloaded more than 18,000 times last month.

It recently also open-sourced ‘Samvaad’, a curated dataset with 100,000 high-quality conversations in English, Hindi, and Hinglish, totalling over 700,000 turns.

Further, Sarvam AI is collaborating with Meta to develop vernacular LLMs and has partnered with Microsoft to create an Indic voice based LLM.

3. PAiGPT

PAiGPT, India’s first AI chatbot for UPSC aspirants, recently released its app for Android and iOS. 

The app’s USP is its ability to fetch real-time information on various topics and current affairs, similar to Perplexity AI and Google Gemini. However, what sets it apart is its feature that provides trending topics and the option to create multiple-choice questions based on the available information.

Founded in September 2022 by Eshank Agarwal, Addya Rai, Siddharth Singh, and Deepanshu Singh, the app also allows aspirants to upload images of editorials from popular newspapers and then generate summaries. 

4. Soket AI Labs

India now has a company building solutions to achieve AGI and beyond. Soket Labs is an AI research firm with a vision to further the advancement in AI towards ethical AGI.

Founded in 2019 by Abhishek Upperwal, the company is part of NVIDIA’s Inception Programme and AWS Activate for training compute access.

Soket AI Labs recently introduced Pragna-1B, India’s first open-source multilingual model designed to cater to the linguistic diversity of the country. Available in Hindi, Gujarati, Bangla, and English, the model comes with 1.25 billion parameters and a context length of 2048 tokens.

5. KissanAI

In a major step forward for AI in agriculture, agri-tech startup KissanAI recently launched Dhenu Vision LLMs for crop disease detection.

Last year, KissanAI also released Dhenu 1.0, an agricultural LLM tailored for Indian farmers. Recently,  it released Dhenu Llama 3, fine-tuned on Llama3 8B. 

The agriculture generative AI startup also teamed up with UNDP to develop the pioneering voice-based vernacular generative AI CoPilot for Climate Resilient Agriculture (CRA) practices. This initiative aims to deliver crucial advice to thousands of Indian farmers, especially smallholders who have been hit hard by climate change.

6. Subtl.ai

Subtl.ai, is addressing the challenges of generative AI in enterprise environments. It focuses on creating solutions that enable enterprises to handle sensitive data securely without exposing it to the internet.

Vishnu Ramesh, founder of Subtl.ai, calls it a ‘private Perplexity built on light models for enterprise’. 

Subtl.ai has developed a proprietary product that leverages the Llama 3 8B model, allowing businesses like the State Bank of India to access and respond to inquiries quickly and securely, directly citing provided sources of information. 

7. dot.agent 

dot.agent is the world’s first AMS (AI/Agent Management System) that acts as a central hub that directs requests to the most suitable AI agent or model for the task. This “smart dispatcher” continuously learns from your data & adapts to your specific use case.

It allows Dot to outperform AI models like GPT-4 and Devin in real-world use cases, potentially reducing your AI costs by up to 60%! Dot for Code Generation is also purportedly 8x better than GPT-4. 

8. Stition.ai 

Stition.ai focuses on building security products for LLMs. Stition’s security product that can automatically find safety flaws without human intervention and patch vulnerabilities has been in public beta since December. A full release is expected soon.

Mufeed VH, the founder of Stition.AI, recently released an open-source passion project called Devika. This Indian version of Devin can understand human instructions, break them down into tasks, conduct research, and autonomously write code to achieve set objectives. 

9. CognitiveLab

Founded by Adithya S Kolavi, CognitiveLab recently released an Indic LLM leaderboard for the growing number of Indic language models entering the scene without a uniform evaluation framework.

The Indic LLM leaderboard offers support for seven Indic languages – Hindi, Kannada, Tamil, Telugu, Malayalam, Marathi, and Gujarati – providing a comprehensive assessment platform. Hosted on Hugging Face, it currently supports four Indic benchmarks, with plans for additional benchmarks in the future.

Click here to check it out.

10. MachineHack

MachineHack Generative AI, one of the few pure-play generative AI startups in India, has launched DataLyze, a generative AI data analysis tool, making data analytics accessible to everyone. 

Launched in 2018, MachineHack is an all-in-one platform designed for data engineers, data scientists, machine learners, and developers at all levels. Users can enhance their skills, compare their expertise with peers, write articles, learn coding, apply for jobs, and build impressive portfolios. 

11. TWO

TWO is a tech company that aims to redefine human-AI interactions through its proprietary multilingual and cost-efficient language models called SUTRA. These are ultrafast, multilingual, online generative AI models that can operate in 50+ languages with conversational, search, and visual capabilities.

SUTRA-Online are internet-connected and hallucination-free models that understand queries, browse the web and summarise information to provide current answers. It can answer queries like “Who won the game last night?” or “What’s the current stock price?” accurately.

12. Tensoic

Mumbai-based software development company Tensoic released a Kannada Llama aka Kan-LLaMA — a 7B Llama-2 model, LoRA PreTrained and FineTuned on Kannada tokens.

Just a few days after releasing Kan-Llama, the researchers also released a playground to test the model. Hooked to NVIDIA A100 GPUs, Tensoic released the playground in partnership with E2E Networks, one of the biggest providers of cloud GPUs in India.

The post Top 12 Indian Generative AI Startups in 2024 Building GenAI Products appeared first on AIM.

With the advancements brought in by GPT models, GPT-4o being the latest, creating sustainable AI startups that leverage artificial intelligence and can last and grow over time has become increasingly important.

In a recent podcast, OpenAI chief Sam Altman spoke about how to either create a business that thrives even if the next AI model isn’t significantly better, or develop a system that gets more useful as AI models improve or advance. 

Additionally, he favoured not building an “AI business” in most cases, but rather a business that uses AI as a technology. Giving an example, he drew parallels to the early days of the App Store, where many people built simple apps like Flashlight which became obsolete with an iOS upgrade.

Meanwhile companies like Uber established sustainable businesses as smartphones improved, leveraging phones as the key technology that significantly enabled their operations.

How OpenAI Plans to Monetise

Recently, OpenAI made GPT-4o available to everyone for free (with usage limits), offering features like browsing, data analysis, and memory. 

Additionally, Plus users will receive up to 5x higher limits and earliest access to features like the new macOS desktop app and next-generation voice and video capabilities. The move highlights OpenAI’s efforts to encourage upgrades to their monetisation plans, as discussed in the podcast.

Altman said that they are yet to figure out ways to make an expensive technology like GPT-4 available to users for free. He emphasised that while they aim to provide advanced AI tools for free or at a minimal cost as part of their mission, the high expenses currently pose a significant barrier.

Meanwhile, OpenAI recently became a Reddit advertising partner, which likely indicates that the company can leverage Reddit’s large user base to advertise its own products and services, potentially driving more customers and revenue.

OpenAI’s revenue for this year has surpassed the $2-billion mark, according to reports from the Financial Times. Therefore, like OpenAI showcasing its continuous revenue generation, startups must also ensure they can sustain their business models in the long run.

Do startups need to follow big companies 

A few days ago, Cred founder Kunal Shah cast a wide net asking people on X this direct question: “Who is building an AI application in India”, receiving nearly 300-400 responses. 

Dharmesh BA, who is working on a stealth startup, noted that many products were simply wrappers around existing models in various modalities. He categorised these apps as CRUD (Create, Read, Update, Delete) and warned that building apps based on the assumption that OpenAI or current LLMs can’t perform specific tasks could lead to a disaster.

Each time OpenAI updates or releases a new version, many startups find themselves rendered obsolete because the enhanced capabilities of OpenAI often solve the problems these startups were aiming to address.

When OpenAI introduced ChatGPT Enterprise, it sent shockwaves across several SaaS startups that had developed products around ChatGPT or offered wrappers based on ChatGPT APIs for business clients. 

Additionally, Dharmesh’s post highlighted a perspective that attempts to confine an extremely powerful technology, like LLMs—which can be compared to a genie capable of doing anything—into a limited space such as mobile apps or websites. 

These technologies are capable of much more complicated and valuable work, and by limiting their potential, we are not utilising them in the medium they are meant to reside in. 

What about Indian Startups

In yet another post on X, the Cred founder said that early-stage startups should be easy to iterate and late-stage startups should be hard to distract. This highlights the mentality of Indian startups that are not iterating and not innovating enough.

In India, researchers and enterprises should prioritise building large models, technical benchmarking, and AI industrial standardisation over developing specific use case apps, which are easily replicated and improved upon. 

Most envision LLMs as operating systems where users choose their own apps, but these apps’ longevity depends on the base provider, like OpenAI’s architecture. 

But as AIM wrote, the question remains as to why such research isn’t being conducted domestically, especially as tech giants like OpenAI and Google focus more on Indic languages, posing a threat to those developing for the Indian ecosystem.

The post How to Build Sustainable AI Startups appeared first on AIM.

E2E Cloud, a leader in GPU cloud solutions, has entered a strategic partnership with Atal Incubation Centre – Nalanda Institute of Technology Foundation (AIC-NITF), backed by the Atal Innovation Mission under NITI Aayog. 

This collaboration aims to enhance innovation and entrepreneurship in India, particularly in AI.

Through a newly signed MoU, E2E Cloud will provide its state-of-the-art GPU infrastructure and expertise to startups incubated at AIC-NITF. This includes access to 64 H100 GPU super pods, expandable to 2048 GPUs, which will support startups in their innovation and scaling efforts.

Tarun Dua, CEO of E2E Cloud, highlighted the importance of this partnership, stating, “Our collaboration with Atal Incubation Centre – Nalanda embodies our shared commitment to nurturing technological innovation. By providing startups at Nalanda with advanced GPU resources at affordable rates, we aim to reduce the barriers for startups to build and scale.”

AIC-NITF, dedicated to promoting innovation and entrepreneurship, will leverage E2E Cloud’s resources to help startups tackle complex challenges and introduce groundbreaking solutions. Durga Prasad Gouda, CEO of AIC-NITF, remarked, “This collaboration with E2E Cloud aligns perfectly with our mission to nurture technology-led startups. Access to cutting-edge GPU technology will enable our incubatees to accelerate their research and development efforts, paving the way for impactful solutions”

Furthermore, E2E Cloud announced a special initiative for incubators: the first 25 incubators will receive GPU credits worth up to 2 lakh rupees each, enhancing their ability to leverage advanced technology and expand their operations.

The post E2E Cloud and AIC-NITF Partner to Boost Indian AI Startup Ecosystem appeared first on AIM.

At the OpenAI Spring Update, OpenAI CTO Mira Murati unveiled GPT-4o, a new flagship model that enriches its suite with ‘omni’ capabilities across text, vision, and audio. Murati promised iterative rollouts to enhance both developer and consumer products in the coming weeks.

During the demonstration of GPT-4o’s real-time translation capabilities, the model seamlessly translated from English to Italian, showcasing its sophisticated linguistic adaptability. Many believe that this new feature of OpenAI is likely to replace Google Translate.

Now that OpenAI is also releasing search capabilities within ChatGPT, it might be time for startups like Perplexity to start counting their days.

While this is about the competition between Big Tech, everyone who has been building models around OpenAI’s GPT models or building on top of open-source models such as Llama or Mistral, is at the threat of being replaced by another update to OpenAI’s update.

Don’t be Just a Wrapper

A few days ago, Cred founder Kunal Shah posted on X asking every Indian startup to post their AI products. The post garnered nearly 400 responses. Dharmesh BA, who is working on a stealth startup in India, took some time to figure out that many of the products were essentially just wrappers of existing models on different modalities. 

He called and categorised these apps as CRUD (Create, Read, Update, Delete). “Most of these apps are built on top of various mediums such as text, image, video, or voice. Building an app with the premise that OpenAI or current LLMs don’t perform a specific task, and therefore you will do it, can lead to disaster,” he said.

To explain, he said that it’s similar to judging a growing child – a 3-year-old who will gain much more knowledge by age 5. You cannot assess a 3-year-old’s intelligence as static. 

AIM has always been at the forefront of talks on this. When OpenAI introduced ChatGPT Enterprise, it sent shockwaves across several SaaS startups that had developed products around ChatGPT or offered wrappers based on ChatGPT APIs catering to business clients. 

Now, the same is happening with several startups that were offering services utilising different models on top of GPT APIs to offer text, image, video, or voice, such as translation apps, language learning apps, coding assistants, or for use in customer service.

Each time OpenAI updates or releases new versions, many startups find themselves becoming irrelevant because of how the new capabilities of OpenAI can now handle the tasks they were attempting to address.

Moreover, attempting to limit the potential of powerful technologies like LLMs within mobile apps or websites is akin to hiring someone like Raghuram Rajan for basic arithmetic – while capable, his true potential lies in solving more significant problems, as Dharmesh explained in his post.

Therefore, it’s important to think carefully before investing in LLMs through traditional apps and websites and building wrappers on top of them. While this isn’t meant to discourage innovation, it’s crucial to remain adaptable and open to change, recognising that the medium you use is evolving. 

Diminishing Returns

This is similar to what Gary Marcus points out repeatedly in his blogs. Most of the advancements in Big Tech are now limited to comparing small changes and updates to their models rather than offering anything revolutionary. All of it is just to get an edge over its competitors.

When it comes to India, researchers and enterprises need to focus on building large models, prioritising technical benchmarking and an industrial standardisation of AI rather than obsessing over specific use case apps. These apps, built on LLMs, are features that can be easily replicated and improved upon by others. Many fail to grasp this point.

Most believe that in the future, there could be several options, and the LLM would be like an operating system where users can decide and choose their own apps. While that is true, how many of those apps would last if the base provider, such as OpenAI, changes its architecture?

The ultimate question that remains is why such research is not being done in the country. OpenAI and Google, both tech giants, have redirected their focus towards Indic languages as well, which will eventually become a threat to those building for the Indian ecosystem. 

Recently, Shah also posted on X saying that early-stage startups should be easy to iterate and late-stage startups should be hard to distract. This also highlights the mentality of Indian startups that are not iterating and not innovating as well.

“Startups should be easy to unlearn,” replied Vivian.

The post We Live in an Era Where it’s Easy to Build but Difficult to Figure Out What to Build appeared first on AIM.

Infosys co-founder Narayana Murthy, who has been quite vocal about the need for the youth of the country to work 70-hours a week, recently said that Indians are good at applying ideas generated elsewhere for the betterment of the nation. He also added that it would take time for the country to invent new things. 

He may be right. The most recent Ola’s Krutrim model is just a replica of OpenAI’s model, simply moulded for Indian use cases. This is similar to Flipkart copying Amazon, Ola copying Uber, and Zomato and Swiggy copying DoorDash. 

Meanwhile, there are other quick commerce companies such as Zepto and Dunzo, which are completely new concepts and are thriving in India. 

Recently, Kunal Shah, the founder of CRED, posted on X saying that early stage startups should be easy to iterate and late stage startups should be hard to distract. This also highlights the mentality of Indian startups that are not iterating and not innovative as well. “Startups should be easy to unlearn,” replied Vivian.

The sentiment that India has been lagging when it comes to building technology and leveraging AI has grown stronger. “The reality of India is that at this point of time, by and large, we have upgraded ourselves to the orbit of applying ideas and concepts that are invented outside India and do some innovation and become experts,” Murthy said, while also adding that it is the first step towards catching up with the West.

Murthy is positive that India will be able to invent new things, but it would take time, and the youth has to be enthusiastic about it. “Because my own belief is that a youngster of today is at least 10-20 times smarter than what I was at their age.”

A lot practical though

Though this may sound like a negative take on India’s innovation, applying global ideas locally is a smart and practical approach. “It’s a strategy that accelerates innovation and progress, leveraging collective human knowledge to solve problems faster,” said a user on X.

One of the reasons that India’s AI is a little bleak is that there is a lack of investment when compared to the West, along with a lack of industry and academia partnership. If this happens, it would create a pathway for more research and development within the country, which still seems to be lacking. 

A similar conversation recently occurred when experts from the Indian tech community said that there was nothing foundational being built out of India. Most of the research was just copy and paste from the West. 

“Who has challenged the original algorithm? While Transformers are a great piece, they have flaws in terms of compute and carbon,” said Nikhil Malhotra, global head-Makers Lab, Tech Mahindra, reiterating that most of the research in India is done on fine-tuned models. 

“Training something from scratch and turning it into the 10th best foundation model that no one will use in production is the wealth only a few companies with deep pockets can afford… even spending millions on failed training runs,” said Pratik Desai, adding that India has so many unique use cases that don’t need foundational model research and using models such as Phi, Orca, or Llama is enough.

“India has never led any fundamental research, but we have a golden opportunity as AI can be a levelling field,” added Desai. “However, this requires a fundamental shift from coaching and academia to a change in mindset from parents, and founders to investors.” 

On the other hand, “If we don’t work on our own AI infrastructure, in the next 5-10 years, like we import oil, we will have to import AI,” said Gaurav Aggarwal, who is currently leading an AI initiative at Jio. He added that it pains him to see that India is not producing AI experts, just “slightly glorified engineers” who have no clue about what they are building.

Similar thoughts were recently shared by Dharmesh BA, founder of a stealth startup. He said that though a lot of people are building AI, it is most likely a wrapper of GPT. “We live in an era where it’s easy to build but difficult to figure out what to build,” he added.

What is the problem?

The possible reasons behind India not inventing new things, as pointed out by several users on X, is the lack of risk-taking attitude, and our over-reliance on the traditional system of education. Meanwhile, some say that there are merits in being a second-mover, but India definitely needs to up its game. 

There have been several theories floating around the internet claiming that India has arrived late to the AI party. Even with the alleged late arrival, most of the AI development in India is focused around building AI  use cases by adopting AI models developed by the western countries, rather than the core technology.

Sourav Das, researcher at IIIT Kalyani, had said the same thing earlier. “How many of them have made an algorithm, theory, or model from scratch,” he questioned, saying that everything is available on the internet and the researchers are just exploiting the resources. 

“There is no invention in India, just reusing the things that are already there,” he said, adding that all the fine-tuning is just getting “honourable mentions”.

A lot of the current AI development is being driven by young developers building AI models on top of existing ones such as LLaMA and Mistral, but nothing concrete has come up yet. 

Though there are initiatives such as Ola’s Krutrim, Sarvam AI, Tech Mahindra’s Project Indus, and BharatGPT that are focused on building models from scratch, a lot of work still needs to be done.

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India’s legal system is neck-deep in crisis, with nearly 5 crore pending cases across the country’s courts. The sheer volume of cases has led to delays, inefficiencies, and a lack of access to justice for many citizens.

However, a new wave of legal tech startups is here, aiming to tackle these challenges head-on by leveraging the power of generative artificial intelligence (GenAI). And with 762 legal tech startups in India, the potential for transformation is immense.

One such startup is CourtEasy, founded by a team of engineers and lawyers who experienced legal problems firsthand. “We started CourtEasy to address the problems faced in the legal sector,” Mrunmayee Shende, one of the founders of CourtEasy, told AIM. 

“The system is overloaded with pending cases, and we believe AI will help solve it at a faster pace,” she added.

What legal tech aims to solve 

The team at CourtEasy recognised the potential of GenAI to bridge the gap between technology and the legal system, particularly in lower courts where the adoption has been slow. 

“In lower courts, people are not aware of the potential of technology yet,” Shende explained. “Higher courts and law firms are more open to adopting newer technologies as they evolve, but this is not the case in lower courts.” 

With the aim to fix this gap, she said, “If we plot the adoption of technology in the legal system, we can see a significant gap between lower and higher courts. We want to bridge this gap by making sure our services are available to all.” 

The company has developed a range of tools powered by GenAI. “We have our Marathi language model called Marathi LLM, which is a foundational model using LLM for the Marathi language,” Shende explained. 

“Similarly, we are in the stage of building a separate legal model that can provide answers and solve the problems faced by legal professionals,” she added. The team has built language models for regional courts, legal research aids, and case drafting assistants. 

Another startup working towards making justice faster and fairer is Jhana AI. “At Jhana, we build intelligent and fundamental tools for legal services, a one-stop ecosystem, and you interact with this humanlike chatbot that is reflective and iterative,” said Em McGlone, co-founder of Jan.

McGlone, explaining the versatility of the chatbot, said, “It can do anything from browsing and reading the web like a human to referencing legal books, journals, statutes, court orders from various courts, and various legal reportage, news blogs, and so on.”

SpotDraft, another legal tech startup, uses GenAI to help law firms and legal teams draft, store, analyse, execute, and automate contracts and contract processes. The company has already processed around 4 million contracts and has clinched clients both locally and globally.

Shashank Bijapur, the CEO and co-founder of Spotdraft, describes himself as a ‘recovering lawyer’ who has experienced the drudgery of lengthy documents firsthand. 

“At some point, I stopped using my brain altogether. I was just using my eyes and fingers to copy and paste [legal clauses] from one sheet to another,” he said, stressing on the need for automation in legal processes.

Global success and future prospects

On one side there’s the Supreme Court launching AI-powered tools like SUPACE (Supreme Court Portal for Assistance in Court’s Efficiency) and SUVAS (Supreme Court Vidhik Anuvaad Software). 

While on the other, we have the Madras High Court’s impressive case clearance rates through tech interventions. These demonstrate how AI can enhance efficiency and access to justice across all levels of the judiciary.

The successful implementation of GenAI in legal systems around the world has already shown promising results. 

For example, in the United States, AI-powered tools have been used to predict the outcomes of cases with high accuracy, while in China, AI judges have been deployed to handle minor legal disputes. 

However, the adoption of GenAI in the legal system is not without challenges. Privacy and data security are major concerns, as legal cases often involve sensitive personal information. 

Shende acknowledged the importance of data safety, “We are a startup, and we have received funds and infrastructure credits from companies like Nvidia and Microsoft to ensure data security.”

Despite the challenges, legal tech startups remain optimistic about the future of GenAI in the industry. CourtEasy has partnered in the Microsoft for Startups initiative and is now in the process of raising additional funds to expand its operations. 

Jhana is also looking for enterprise solutions for their product and is excited to meet people from the community who are building various tools for big corporations.

The future of tech integration into India’s legal system looks promising. As McLone puts it, “We think we can make justice faster and fair by creating better retrieval systems for lawyers to use.”

The post Can GenAI Solve Legal Troubles in India? appeared first on AIM.

As the UPSC Civil Services Exam (CSE) date of June 16th draws closer, over 11 lakh aspirants are diligently preparing to tackle one of the world’s most challenging examinations. SuperKalam, an AI platform is empowering students with personalised learning experiences, paving the way for their success in cracking the UPSC CSE.

Vimal Singh Rathore along with Aseem Gupta, both of whom previously founded qoohoo, started SuperKalam in July last year. 

Vimal who cleared his UPSC in Central Armed Police Forces Exam (CAPF) exam but went on to work at Unacademy and then start his own company, Coursavy, which was acquired by Unacademy.

The duo recognised the need for a more adaptable and student-centric approach to UPSC preparation. With his extensive background in education and entrepreneurship, Rathore set out to create a platform that could bridge the gap between traditional coaching methods and the unique requirements of each aspirant.

In an exclusive interview with AIM, Rathore shared his insights on the platform’s genesis and its mission to revolutionise UPSC preparation. 

“The inspiration is pretty simple,” he explained. “There are three examinations in India which kind of change the trajectory of any student or any family’s future or life, which are UPSC, that is for the civil services, NEET to become a doctor, and JEE to become an engineer. These examinations cumulatively are given by 5 million aspirants every year.”

Rathore further emphasised the need for a more personalised approach to learning for these exams. He stated, “The entire responsibility of understanding, asking questions, whether they are grasping or not, is completely on the student. The platform somehow, because of this kind of process, was not accountable. They were unable to do a lot about how students can improve their learning outcomes.”

Leveraging OpenAI’s GPTs 

SuperKalam leverages multiple models, including Llama, OpenAI’s GPT-4, and GPT-3.5, to deliver personalised learning experiences. For generating MCQs, Super Kalam relies on OpenAI GPT-4, while reasoning-based problems are tackled using OpenAI GPT-4 Turbo.

Super Kalam assists students in creating personalised timetables, sending daily targets, and tracking progress throughout their learning journey. It helps resolve doubts, aids in mastering concepts, and can evaluate a UPSC student’s handwritten answer in less than 60 seconds—a process that typically takes 2-4 weeks in the current education market. 

The platform also conducts mock tests, identifies students’ strengths and weaknesses, and provides progress reports that foster self-awareness, unlocking their full potential.

Rathore revealed, “There are at least 14 Agents that we are using to understand what you are asking Super Kalam.” The platform also uses fine-tuning and prompt engineering techniques to enhance the accuracy and relevance of the generated content.

SuperKalam has invested in NVIDIA GPUs and cloud infrastructure to ensure scalability and cost-efficiency, although they declined to mention how many. 

“We are also leveraging NVIDIA GPUs for certain models to handle the performance” Rathore mentioned. As the platform’s user base grows, the team is exploring partnerships with local cloud service providers to optimise costs and performance.

Aspiring to become SuperKalam

Last year the founders were a part of the Y Combinator W23 batch and the company is backed by Gustaf Alstromer, a partner at Y Combinator. Rathore brings his experience as a founding team member and growth leader at Unacademy, while Gupta, the CTO, previously worked in the early engineering team at Razorpay.

Since its inception, Super Kalam has witnessed remarkable growth. The platform currently boasts a user base of over 46,000 students, with an impressive week 12 retention rate of 78%. 

Rathore proudly shared, “We launched this latest version of the product on 15th of March. So 355 students who attempted any number of questions that day, the total number of questions that you are seeing, is different for every student.”

The impact of Super Kalam’s AI-driven approach is evident in the success stories of its users. Rathore highlighted the journey of Navya, a UPSC aspirant who struggled with consistency and self-doubt. 

“Navya’s accuracy rate was 56% at 45 questions. And here, Navya is number one ranker, with 234 questions and 84% accuracy,” he beamed.

SuperKalam has set its sights beyond UPSC and aims to scale to cater to students attempting other competitive exams. “At the same time, our mission is to make quality education accessible and affordable,” Vimal concluded. 

The post How SuperKalam Uses OpenAI GPTs to Fuel UPSC Aspirants  appeared first on AIM.

There are essentially two challenges for generative AI in enterprise space. The first is that every company wants a solution based on their private data, and the second is that the data is too sensitive for companies to publish on the web. Subtl.ai started building a solution for this back in 2020.

“Microsoft has a lot of security features on top of its OpenAI offerings. But at the end of the day, it’s still a common endpoint, making it difficult for companies in the banking or sensitive data sector to rely on it,” said Vishnu Ramesh, founder of Subtl.ai, in an interview with AIM.

Ramesh calls Subtl.ai a ‘private Perplexity built on light models for enterprise’. The company started as a for-profit research company out of IIIT Hyderabad and has built a light stack, which can sit on top of the existing cloud of the enterprise customers. It remains disconnected from the internet to protect the privacy of the data. 

“The main reason we started this was to check if we can actually build something like Google from India,” said Ramesh, adding that many companies such as FreshWorks have built amazing products, but can something like Google come out of India. “In 2020, the vision was to make Google private, which slowly shifted to giving people a way to talk to their documents,” he added. 

“Even with Google, there is a team of 1000 people sitting on the backend and verifying all the information that goes to the users, which is a very hard thing to do for 1000 enterprise customers.” With this in mind, Ramesh went on to acquire several defence contracts to build the product. 

Perplexity recently released its Perplexity Pro for Enterprise, which offers similar solutions as Subtl.ai, but customers are worried about using it. Even though it gives real-time information with access to the internet, it is yet to crack the private use case for enterprise with regards to security. 

Similarly, Atlassian has released Rovo, which offers similar capabilities for enterprise customers to allow workers to access data and information from external sources along with the data held within the enterprise.

Then, what is the moat?

OpenAI also offers retrieval capabilities through its API, and the same is the case with Google and Anthropic. But the recent Amazon Q case where the information was leaked to the internet has been on the minds of enterprise AI adopters. This has made executives seek solutions that stay off the internet, but still access the private data. 

This is where Subtl.ai comes in. 

In a demo presentation of the product set to be launched this week, Ramesh showed an example of how the State Bank of India is leveraging this solution. Built on top of Llama 3 8B and Subtl.ai’s proprietary retrieval solutions, the product can access all the information of catalogues and databases that the bank trained on and can reply to simple questions citing the exact source along with a paragraph.

“What would take 3-5 business days for a simple keyword query, our offering does in less than a minute,” quipped Ramesh, about the SBI offering. He also added that most of the data for SBI were the RBI regulations, which were publicly available on the internet. It was around 40% of the solutions that SBI operators required. 

“There is value in getting focus paragraphs. It’s a great user experience for the customer.”

“This is what gives us the moat,” said Ramesh, adding that other offerings which are also connected to the internet provide a link and a PDF for private data as the cited source. It makes the task to search for the exact information another added task for the users. Subtl.ai gives a paragraph from a 600-page long PDF, citing the exact source of information for quick verification. 

The future and issues

The only thing that falls short is that currently the offerings are only text based. When it comes to multimodal capabilities, Ramesh said that the data is converted to text from audios or videos and fed into the engine. In an example, he showed a video of all Kunal Shah podcasts, which were transcribed through YouTube and fed into the machine. If a user asks any question based on it, the chatbot generates the response giving the exact timestamp from the video where Shah mentioned the sentence. 

Moreover, when it comes to multilingual capabilities, Ramesh said that they worked with Agastya Foundation, an NGO in Karnataka, where underprivileged kids would ask doubts in Kannada. These would be converted to English manually and fed into the system for answers. 

“We are a small company and we do not have vast resources like OpenAI. But we are confident that our model can understand text and provide answers based on that. Everything else will just come around,” said Ramesh. 

Ramesh narrated the story of a law firm that reached out to him for the offering. It made him curious about why someone would want the product when others are offering it. The reason is the same – giving exact information while also giving extracts from the documents, instead of PDFs and page numbers. 

This also helps in reducing the hallucinations within the model as the information is being retrieved from small amounts of data sources instead of whole documents. Ramesh claims that Subtl.ai offers 75% lesser hallucination rate when compared to its competitors. 

The company started with using OpenAI solutions, moved to Mistral, and now uses Llama 3 and has five models under the hood for seamless experience for its customers, with the second biggest model being only of 110 million parameters, making it very lightweight and easy for customers to integrate.

In the coming weeks, Subtl.ai plans to release the model for enterprise for free for a month and also give a private product on the internet for people to test out.

The post Meet the Indian AI Startup Building a Private Perplexity for Enterprise appeared first on AIM.

Healthtech AI startup Endimension Technology has recently raised INR 6 Crore in Pre-Series A round led by Inflection Point Ventures.

The funds will be used to fuel AI research and development, team expansion, and software enhancement. These strategic investments aim to bolster Endimension’s market position, accelerate growth, and establish Endimension as an industry leader.

Other investors in this round include Sucseed Indovation, SINE IIT Bombay and individual angel investors. Endimension Technology, incubated at IIT Bombay, is driven by the vision to harness AI technology in radiology, ensuring early and precise diagnosis for patients globally.

“The Indian radio-diagnosis market, growing at a CAGR of 15%+ over the last decade, has got a lot of focus on equipment & infrastructure. The under-stated need is that of qualified professionals, i.e. radiologists, to manage this burgeoning demand. There has been growth across tier 1, 2 & 3 for equipment, but the availability and prohibitive costs of trained radiologists exacerbate the problem of demand outstripping supply situation.

“Endimension focuses on leveraging AI to facilitate faster assessment and diagnosis, employing generative AI to streamline report generation and reduce the time required by radiologists. IPV is confident that this investment will contribute towards the betterment of the industry,” Ivy Chin, Partner, Inflection Point Ventures said.

So far, Endimension’s platform has processed over 1 million scans to date and is currently deployed in 400 hospitals and diagnostic centres across multiple regions, enhancing their accessibility.

Endimension has added several feathers to its cap over the years. The startup was one of the 20 startups selected by Google for Startup Accelerator Class 8 and one of the top 10 startups at the WhatsApp Incubator Programme.

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New Jersey-based prominent cloud software and data analytics firm Axtria has announced its ninth Global Innovation and Capability Centre in Hyderabad, India. 

This facility, located at DLF Cyber City, Gachibowli, is the company’s largest office in India. Spanning 76,000 square feet, it’s certified LEED Platinum for its sustainable practices. It’s designed to be differently abled-friendly and has garnered praise for its zero-water waste initiatives.

Jaswinder Chadha, Axtria’s president and CEO, expressed the company’s commitment to India’s talent pool and its mission to drive innovation in the life sciences field. “This expansion reinforces our relentless pursuit of growth and innovation as we strive to serve our clients better and drive positive impact in the life sciences industry,” added Chadha.

Focusing on generative AI strategies, the company aims to recruit nearly 800 professionals globally in the coming months.

Manish Mittal, head of global delivery at Axtria and India country head, highlighted the role of Hyderabad’s tech sector in fostering innovation. “In the past few years, Hyderabad has emerged as a potential tech sector backed by economic and infrastructural transformation, and we are happy to open doors of opportunities to the tech community residing in the city.” added Mittal. 

Axtria’s workforce, including engineers and data scientists, is dedicated to developing cutting-edge solutions for the industry. The company’s recent expansions in Noida, Pune, and Hyderabad are expected to create numerous job opportunities.

Axtria collaborates with 16 of the top 20 pharmaceutical companies, offering a range of cloud-based solutions. From brand launches to retirement, Axtria guides its clients through the digital transformation journey. Its suite of products, including Axtria InsightsMAx, SalesIQ, CustomerIQ, MarketingIQ, and DataMAx, empowers stakeholders at all levels to make informed decisions and optimise operations.

As a participant in the United Nations Global Compact, Axtria aligns its operations with principles of human rights, labour, environment, and anti-corruption, demonstrating its commitment to societal goals.

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