Год: 2025

India’s healthcare market, estimated anywhere between $180 and $400 billion, is undergoing a significant shift, and digital health is now one of its fastest-growing engines. Startups are playing a pivotal role in this transformation.

Over the past few years, India’s health-tech segment has expanded from around $3 billion in 2020 to $7 billion in FY 2023, with projections placing it at a massive $60 billion by FY 2028. The ecosystem itself is booming: India now hosts over 10,000 health and life sciences startups recognised by promotion of industry and internal trade department (DPIIT), recording a remarkable 127% CAGR since 2016.

Against this backdrop of scale and innovation, here are the top 10 Indian healthtech startups leading the race.

Niramai
Niramai offers Thermalytix, a non-invasive, radiation-free breast cancer screening system using thermal sensors. Its AI analyses ~4,00,000 temperature points per scan to detect subtle heat signatures associated with tumours, producing a quantitative ‘breast health score’. Their models are trained on large datasets linked to mammograms, ultrasound and histopathology, increasing clinical reliability. Thermalytix is now commercially available in over 22 countries and holds a CE mark.

Qure.ai
Qure.ai builds AI-powered diagnostic tools for medical imaging, including chest X-rays (qXR) and head CT scans (qER), to detect conditions like tuberculosis, lung nodules and haemorrhages. Their deep learning models speed up diagnosis by triaging critical cases in real time, reducing radiologists’ workload. Recently, Qure.ai announced plans to expand to 10,000 hospitals in the coming years.

SigTuple
SigTuple combines robotics and AI via its AI100 platform to digitise microscope slides, including blood smears and urine, and analyse them. Their AI models classify different cell types, detect morphological anomalies and flag abnormalities. The AI100 product is FDA 510(k) approved, and SigTuple holds multiple patents on its AI-based screening tools.

Tricog Health
Tricog Health offers remote cardiac diagnostics. ECGs captured in clinics are uploaded to the cloud, where Tricog’s AI analyses them for over 140 cardiac conditions, like arrhythmias and ST elevation. Its AI-generated report is reviewed by in-house cardiologists to ensure accuracy and speed. As of late 2024, Tricog has enabled diagnostic care for 20 million lives, partnering with organisations like AstraZeneca to scale in underserved regions.

HealthifyMe
HealthifyMe is a digital wellness platform that combines tracked nutrition, fitness and lifestyle with human coaches. Its AI coach ‘Ria’ provides personalised diet and advice on habits, and the company is now embedding generative AI to personalise nutrition plans further. In its latest funding round, it raised $30 million to expand AI capabilities and push into global markets.

Practo
Practo provides a comprehensive healthcare platform for patients and clinics: booking appointments, managing medical records and running clinic software. Their AI supports smart triage, structured patient summaries, and optimised scheduling. Practo is now aiming to double its international revenue (currently ~20%) by expanding into markets like Canada and Australia.

PharmEasy
PharmEasy is a leading Indian e-pharmacy that also offers diagnostic test bookings. While specific details on AI deployment are not widely publicised, it leverages machine learning for demand forecasting, inventory optimisation and personalised test-and-medicine bundling. The operational efficiency gains from AI help reduce costs, improve delivery times and ensure better availability.

Innovaccer
Innovaccer provides a data platform that aggregates fragmented health records into a unified, AI-enabled system. Their AI predicts care gaps, stratifies patients by risk, automates documentation via NLP, and supports decision-making for care teams. In January, they raised $ 275 million (Series F) to build new AI co-pilots and scale cloud capabilities.

MedGenome
MedGenome is a genomics company offering genetic testing, sequencing, and bioinformatics. Their AI-driven pipelines interpret genetic variants predict disease risk, and support personalised medicine. Recently, they expanded in India by acquiring a majority stake in Gujarat-based diagnostic chain Green Cross Genetics to strengthen their integrated genomics and diagnostics footprint.

Tata 1 mg
Tata 1mg is a full-stack digital healthcare platform offering e-pharmacy, diagnostics, teleconsultations, and wellness content. Since being acquired by Tata Digital, it has built a tighter supply chain stocking medicines in its own warehouses, which has boosted margins and improved quality control. On the tech side, it uses data and AI in its ODIN (Order Delivery Intelligent Network) to manage orders, inventory, quality checks and logistics. ata 1mg is now aggressively expanding offline, aiming for omnichannel reach.

Disclaimer: This list is not a ranking and is not based on any specific metric. The startups featured here are highlighted for their contributions to India’s healthtech ecosystem and their use of AI, not for comparative evaluation.

The post Top 10 Indian Startups Powering Healthcare with AI appeared first on Analytics India Magazine.

The United States Court of Appeals for the Fifth Circuit has upheld the damages imposed on Tata Consultancy Services (TCS) in the trade secrets lawsuit filed by DXC Technology Company, earlier known as Computer Sciences Corporation (CSC).

In a disclosure, TCS said it is evaluating various options, including further review and appeal before the appropriate courts, and intends to “vigorously defend its position.”

The Appeals Court has also set aside an earlier injunction issued by the United States District Court for the Northern District of Texas.

That injunction had restricted TCS from using certain CSC software and confidential materials. With the order now vacated, these restrictions are no longer in force while the District Court reassesses the matter.

A major part of the dispute concerns TCS’s work for Transamerica.

Under Transamerica’s agreements with CSC, TCS was given limited permission to access CSC’s Vantage and CyberLife systems to support the insurer’s technology transformation.

CSC later argued in court that TCS went beyond what was allowed and used CSC’s software and confidential information in ways not permitted under those agreements.

The District Court said CSC had raised genuine issues of fact on this point, allowing the claim to proceed.

The original judgement had found TCS liable under the Defend Trade Secrets Act of 2016 and ordered the company to pay CSC about $56.15 million in compensatory damages, $112.30 million in exemplary damages, and $25.77 million in prejudgment interest through June 13, 2024.

TCS disclosed this ruling last year, saying it believed it had “strong arguments against the Judgement” and intended to pursue review or appeal.

The company also said the ruling would not have a major adverse impact on its financials or operations.

The District Court’s detailed memorandum opinion from 2023, had denied TCS’s motion for summary judgment and its attempt to exclude CSC’s damages expert.

It also struck several of TCS’s defenses, including failure to state a claim, lack of trade secrets and consent, and ruled in CSC’s favour on defenses such as laches, failure to mitigate and unclean hands.

Other defenses, such as equitable estoppel, waiver and acquiescence, were left to be decided at trial.

TCS had also argued that the exemplary damages were “legally excessive” and should be reduced or set aside.

The Appeals Court rejected this argument and upheld the entire damages awarded by the District Court.

The post US Court Upholds $194 Million Penalty Against TCS appeared first on Analytics India Magazine.

Salesforce CEO Marc Benioff has sparked a fresh wave of debate in the AI world after declaring that Google’s newly launched Gemini 3 has decisively overtaken ChatGPT.

In a post on X, Benioff wrote that he has used ChatGPT “every day for 3 years,” but after spending two hours with Gemini 3, he’s “not going back,” calling the leap in reasoning, speed, images and video “insane.”

Benioff’s emphatic endorsement comes just as Google unveiled Gemini 3, its most powerful multimodal AI model to date and Nano Banana Pro, an advanced image-generation system built on top of it.

His comments immediately intensified comparisons between Google and OpenAI, the two companies locked in the most closely watched AI rivalry.

According to Google, Gemini 3 brings major improvements in complex reasoning, multimodal understanding, and tool-use capabilities. It integrates text, images, video, and code processing, positioning it as Google’s first truly general-purpose, agentic AI system across consumer and enterprise products.

Alongside it, Google introduced Nano Banana Pro, a new image-generation and editing model that promises studio-grade visuals, reliable text rendering, multilingual support, consistent brand styling, and high-resolution (including 4K) output.

The model is already rolling out across Google Workspace and the Gemini app, signalling Google’s push to tie creative workflows directly into its AI ecosystem.

The launches represent one of Google’s most aggressive bids yet to reclaim AI leadership, especially as OpenAI continues rapid advancements with its GPT-5 series. Benioff’s praise adds fuel to that momentum, offering rare public validation from a long-time power user of competing AI systems.

The post ‘Holy S***… I’m Not Going Back to ChatGPT,’ Says Marc Benioff After Using Gemini 3 appeared first on Analytics India Magazine.

Andrej Karpathy, the AI researcher and founder of Eureka Labs, recently shared an experiment called “LLM-Council”, which sends a user query to multiple language models, lets them anonymously judge each other’s answers, and then produces a final response based on their rankings.

The results of this experiment revealed that the AI model that consistently ranked highest was OpenAI’s GPT-5.1. This is significant given how recent benchmarks suggested Google’s Gemini 3.0 had overtaken OpenAI in overall capability and reasoning tests.

“Quite often, the models are surprisingly willing to select another LLM’s response as superior to their own, making this an interesting model evaluation strategy more generally,” said Karpathy.

“For example, reading book chapters together with my LLM Council today, the models consistently praise GPT 5.1 as the best and most insightful model, and consistently select Claude as the worst model, with the other models floating in between.”

Karpathy’s experiment setup is a three-step loop.

First, the user’s query is sent to all models separately, and their answers are shown side-by-side without revealing who wrote what.

Next, each model sees the others’ responses, still anonymised, and ranks them based on accuracy and insight. Finally, a “chairman model” produces the answer by combining the councils’ outputs and their critiques, turning the response into a consensus built through competition.

However, Karpathy also noted that these rankings are subjective and don’t necessarily match his own judgment.

As he put it, “I’m not 100% convinced this aligns with my own qualitative assessment. For example, qualitatively, I find GPT 5.1 a little too wordy and sprawled and Gemini 3 a bit more condensed and processed. Claude is too terse in this domain.”

He revealed that he built this project over the weekend using a ‘vibe coding’ tool and shared the repository on GitHub.

Reacting to Karpathy’s post on X, Vasuman M, founder and CEO at Varick AI Agents, claimed on the social media platform that he built something similar months ago, and observed similar performance from OpenAI’s models.

“Even after plugging in Gemini 3.0, the winner was GPT 5.1, every single time,” he said. “Even funnier, if you tell other models (Claude, Gemini, Grok) that the answer they are reading came from GPT (un-anonymise), they fold immediately and start correcting themselves based on GPT’s output.”

The post GPT 5.1 Is The Best, As Declared By Gemini 3.0, Claude & Grok On Andrej Karpathy’s ‘LLM Council’ appeared first on Analytics India Magazine.

India’s edtech sector is entering its second act, one marked by an aggressive shift offline. After years of digital disruption, leading players are investing in physical centres to rebuild trust, improve outcomes, and achieve sustainable unit economics.

PhysicsWallah (PW), once a purely digital success story, is preparing a ₹3,480 crore IPO to fund offline expansion and acquisitions such as Utkarsh Classes, strengthening its reach in tier-II and tier-III cities. Meanwhile, UpGrad and Unacademy are reportedly in talks for a $300–400 million merger — a move that signals large-scale consolidation as both seek deeper offline footprints.

UpGrad has been expanding into physical learning centres and enterprise programs, while Unacademy, after restructuring for profitability, expects 70% of its centres to turn profitable in 2025.

Even Vedantu and SpeakX are following suit, using recent funding rounds from Accel and WestBridge Capital to grow hybrid and classroom-based models.

The message is clear: despite their digital DNA and efforts towards AI, India’s edtech leaders are betting big on brick-and-mortar. AI now plays a supporting role — bridging digital precision with classroom credibility, as the sector learns that the future of learning still has a physical address.

AI and Offline Evolution Through the Pandemic

The pivot to offline and hybrid learning began soon after the pandemic. During 2020–2021, India’s edtech sector boomed as platforms like Byju’s, Unacademy, Vedantu, and PhysicsWallah scaled rapidly with AI-driven systems and virtual classrooms. But, as schools reopened in late 2021, engagement fell. Parents sought credibility, students wanted peer interaction, and teachers struggled to sustain attention online. The shift forced edtech firms to transition from their growth-at-all-costs model to sustainable, outcome-focused models.

Even a physical AI-based tutor won’t be able to replace a classroom, because offline teaching provides students emotional and mental support, they need to interact with the teacher, said Suraj Biswas, founder and CEO of Assessli.

Assessli is building, it claims, “the world’s only LBM (Large Behavioral Model) — an AI foundation model trained on genomic, neuropsychological, and behavioural data.”

Biswas added that edtech firms are routing to offline models because they need to sustain themselves and keep the revenue rolling.

By 2022–2023, the offline pivot gained pace. Byju’s launched Tuition Centres, Unacademy opened hubs in Kota and Bengaluru, Vedantu rolled out hybrid learning pods, and PW expanded into tier-II and tier-III cities through Utkarsh Classes.

AI shifted from automation to classroom support. Unacademy’s Airlearn offers AI-driven language practice in English and regional languages, PW uses AI dashboards to personalise guidance, and Vedantu’s WAVE 2.0 monitors engagement to adapt teaching in real time. Byju’s added computer-vision tools to track participation. These examples show how edtechs are refitting their AI for the classroom.

This model can work because offline teaching and AI are complementing each other to shape the learning experience, said Shantanu Rooj, founder and CEO at TeamLease Edtech. “AI gives scale, personalisation, and 24×7 academic support. While offline centres offer trust, community, and structure, especially for high-stakes learning,” he added.

Rooj said that recent industry studies show that hybrid models improve completion and engagement versus purely online or purely physical formats. Over 70–80% of institutions are experimenting with some form of blended delivery.

“For a company like ours that is deeply committed to online and work-linked learning, offline touchpoints make sense as they strengthen outcomes, not replace digital-first efficiency,” he added.

The Profitability Game

Edtech companies are moving towards offline learning as many students still prefer face-to-face classes, especially for competitive exams and certification courses. Even though fewer people take these offline courses compared to digital ones, companies focus on them because students stay longer (lifetime value of customer) and the cost to get each customer is lower (customer acquisition cost).

Biswas elaborated to explain: These firms struggle for profitability as online the CAC is very high compared to offline. A lot of marketing and retention goes into online as the CAC to LTV ratio is small, which should ideally be 1:7—if CAC is 10%, LTV should be around 70%.

“Otherwise, if I spend ₹3,000 or even ₹500 to get one user and only earn ₹3,000–₹5,000, the ratio isn’t that good. That’s why offline is always better. Of course, it depends — user to user, organisation to organisation, course to course — but this is the ideal case.”

He added that retention is much lower in online learning because students have many options. “In online, the cost is around ₹4,000–₹5,000, so students don’t mind subscribing to multiple platforms — Allen, Unacademy, Vedantu — or even using free YouTube classes,” he said. Yet, having digital operations helps in building brand presence in tier 2 and tier 3 cities where students lack infrastructure.

AI and the Blended Future

Acquisitions of AI-led edtech platforms by offline centres, or vice versa, are only one way forward, said Rooj. He said that offline players can bring AI into their systems through partnerships, in-house solutions, or platform integrations that support adaptive practice, doubt-solving, vernacular learning, and performance analytics.

According to him, the real advantage will come from combining three core elements: human context through mentors and counselling, AI infrastructure that enables personalisation and real-time feedback, and visible outcomes linked to grades, exams, or jobs. Centres that act as high-trust spaces powered by intelligent digital systems, he said, will hold a stronger edge than those treating AI as a superficial add-on.

Rooj claimed that traditional “content plus testing” models are losing relevance as exam preparation shifts toward conceptual mastery, analytics, and clear career pathways.

Biswas added to this by dividing learners into two groups—offline and online—each using AI differently. Offline learners rely on mentoring and structure, but increasingly benefit from digital augmentation. In classrooms, AI is automating question papers, evaluations, and performance tracking.

Companies like Extramarks are building B2B SaaS tools to help teachers optimise classes and analyse learning outcomes. He noted that while teachers use AI to create questions and students use it to draft answers, there is now a need for balanced tools that assist both sides without bias.

Looking ahead, Biswas said he expects “physical AI companions” to enter classrooms—interactive tutors capable of explaining concepts, drawing visuals, and generating content dynamically across formats such as text, diagrams, and videos.

For online learners, the biggest challenge is retention in an environment overloaded with content. While AI enables personalised learning and content generation, gamified experiences are emerging as the real differentiator because they keep a fickle and easily distracted audience engaged and motivated to stay longer on a platform. These operations might use AI and increase operational costs.

Biswas believes the next major opportunity lies in hybrid learning models across tier-II and tier-III cities, where demand is high but access to quality education remains low. Institutions that blend offline trust with AI-driven personalisation, he said, are best placed to lead this next phase of growth.

The post Edtech’s Offline Shift reshapes AI Usage appeared first on Analytics India Magazine.

StoneX Group Inc’s CTO Boris Levine does not sound like someone waiting for the future to arrive. He sounds like someone who has already lived through five different versions of it and is now watching the rest of the world catch up.

Levine holds the number one spot on HackerEarth, with a focus on mathematics and algorithms, and that sets the tone. He codes. He thinks in systems. He sees patterns long before others feel the tremor.

He has been at StoneX for years, but his job description keeps expanding. He is trying to turn a decades-old financial firm into a full-scale tech company. He is pushing teams across continents to rethink everything from testing to customer support to the code they ship.

And he is doing all of this while flying between conferences, reviewing hackathon pitches, helping shape Gartner as the member of the CIO Research Board, and sitting in front of developers who keep asking him whether the world is shifting beneath their feet.

He believes it is. And he thinks we are early. “Impossible just takes longer,” he said. It is the closest thing he has to a motto, and much of his work is built on that line.

When he landed in Bengaluru at 3 am on November 14, the StoneX hackathon was in full swing with 30 teams across the city, as well as in Pune, and more than 130 participants. The problems ranged from HR to data engineering to operations.

But what really excites him is not the hackathon itself. It’s what sits underneath it—a shift in how developers think about creating things in a world full of AI tools. “Everyone is very excited about possibilities and opportunities,” he said, coming straight from the Gartner summit in Barcelona. As part of the CIO Research Board, he’s spent four consecutive summits in conversations dominated entirely by AI, and its effects on engineering, business processes and customers.

What Would Levine Do?

Levine sits right at the centre of two worlds. One is the traditional financial stack that demands caution, stability and regulatory clarity. The other is the speed at which new models are breaking every expectation about what is possible in software development. That tension shapes almost every decision he makes.

StoneX uses Copilot and other tools across teams. “We can see very good signs of development process improvement in terms of efficiency,” he said. The company has built proofs of concept for automated testing, automated script generation and faster transition work. But he refuses to hand over full product creation to AI.

“We need to be very careful about what we can put in front of the customers, how secure, how stable the solution is,” he said. The company is regulated by dozens of bodies around the world. Every line of code carries weight and liabilities. And the same question keeps returning: how do you push the edge of technical possibility while living inside a system that cannot afford mistakes?

Levine believes the industry is still stuck in the wrong place.

Enterprises are chasing productivity when they should be chasing process redesign. Studies back him up. “Only 5% of the projects deliver return on investment,” he said, pointing to an MIT figure. McKinsey & Company’s recent report shows most pilots remain stuck in the POC bucket. Everyone is chasing faster development, faster operations, faster monitoring.

But the real return, he argues, will come only when companies change how the process itself works, not when they plug AI into the old version of it.

He is blunt about where the bottleneck is. “We’re not yet there,” he said. Not just because of technology, but also because of regulations. Financial firms cannot let models make decisions that touch clients without rigour, which does not yet exist. That is the gap, and it slows everything else down.

He is equally blunt about the limitations of current models. “You cannot fit your entire code base in the context window,” he said. Increasing the context window only creates new problems: cost, loss of focus, and error rates that spike with scale. If the window gets to a million tokens, even a small margin of error compounds. The model loses coherence. It drifts.

The path forward, he says, is clear—composite models, knowledge graphs, better architecture. Systems that work more like people do. “Highly skilled developers don’t remember every single line of code,” he said. They remember ideas, concepts and patterns. Models need the same structure. Context windows cannot get us there. Knowledge systems can.

What About the Future?

Levine has been following Meta chief AI scientist Yann LeCun’s work and also believes in world models. He does not believe anything today can truly reason across steps. Current systems try to process entire problems at once instead of breaking them down. “Very complex problems usually need to be split in different stages of proof,” he said.

That, he argues, is the skill missing from all large models today.

He pointed to another shift he is watching closely: edge AI—models that run on phones. He explained it through the lens of search. Today, StoneX and other firms rely on Google ranking. They know what matters: mobile speed, layout stability, relevance and token match. These rules determine how clients discover trading products. But if users move from Google to local models on their phones, everything changes.

“Users will stop searching Google. They will start asking questions to the local LLM model,” he said. When that happens, the entire onboarding funnel changes.

This leads him into a deeper fear. Advertising and sponsored answers. A future where on-device models suggest trades, products or even medicines based on whoever paid for influence. “How can you trust your helper on your phone that it actually tells you something?” he asked.

This kind of thinking is what makes him hard to categorise. He moves from pure code to system behaviour to sociology with the same clarity. He has done this before. His years at Intel shaped how he sees the hardware problem. The gap between the human brain running on “about 40 watts” and current models consuming kilowatts or more is, to him, absurd.

He has seen early attempts at new chips, and he believes hardware will unlock the next leap. He believes these chips will make AI so cheap that it will sit inside kettles, irons, refrigerators and everything else. That is the future he sees.

What Happens Inside StoneX?

Inside StoneX, the work is more grounded. The company is using AI to simplify customer service, unify operational data and remove the need for employees to jump between systems. “They can just ask our internal systems,” he said. The system converts questions into API calls, collects data and retrieves answers.

It changes speed and quality. On the development side, AI tools reduce PR review time, security checks and migrations. They accelerate the boring parts. They free teams to think better.

But he knows this comes with risk. “Three months down the line, I need to change something. I have a bunch of code that nobody understands,” he said. Models evolve. Vendors change. Context changes. Maintenance becomes a nightmare. Technical debt balloons. This keeps him cautious.

When it comes to clients, he refuses to move fast. He knows regulators will not accept AI-driven advice without a foundation that cannot be tricked by prompts.

“We need AI that is guarded and controlled, not by an extra system prompt,” he said. It must be built into the model. Something that cannot be overridden. Something that refuses to act outside its role. That, he says, will open the door to serious enterprise adoption. He does not believe synthetic data solves this. Only responsible AI will.

He is equally firm about where India fits into the future. StoneX has long-standing operations in India, with offices in Pune and Bengaluru.

India has scale, talent and deep pools of engineering talent. Pune brings payments and banking strength. Bengaluru brings universities and a flood of young developers. StoneX has invested in both for a reason. The offices are growing fast. The company sees them as long-term investments, not cost centres.

He paints India as one of the new bright centres of technical innovation. While the hackathon is one signal, the pace of hiring is another. But the shift in developer mindset, he says, is the biggest one.

When he talks about art, he becomes almost reflective. He loves painting. His favourite artist is Rubens Santoro. He slows down. He describes light, mood and influence. He talks about creativity as a way to detach from day-to-day technical intensity.

He sees programming as creative work. In places like Poland, developers are even given tax benefits for working in creative professions. He says some of the best engineers have no artistic hobbies. The connection, he says, is personal, not universal.

He does not fear AI taking over art. “Are we going to see the next Gustav Klimt?” he asks. He does not think the current mathematical models can produce anything that breaks out of the mainstream. He says the same about code. He does not expect AI to produce a new way of organising computation. It can remix, but it cannot invent the next architecture. Not yet.

But what about AGI? Levine says motivation, purpose and the ability to verify intelligence sit at the centre of the question. He has opinions but no definitive answer. “I would need to think about it,” he says when asked how we would even test real intelligence. Current models can easily pass Turing-style checks, he noted. They still have no purpose. No internal drive. No reason to choose one action over another.

He believes that true intelligence needs motivation built into its core.

The post ‘Impossible Just Takes Longer,’ Says StoneX Group Inc’s Relentless Super Coder CTO appeared first on Analytics India Magazine.

Chennai-based Agnikul Cosmos has raised about ₹150 crore (~$17 million) in fresh funding on November 22. The company secured the round at a valuation of $500 million. The funds will support its upcoming launches, stage-recovery programme, and its integrated space campus on 350 acres allocated by the Tamil Nadu government.

The round included participation from family offices and institutional investors, including Advenza Global Limited, Atharva Green Ecotech LLP, HDFC Bank, Artha Select Fund, Prathithi Ventures, and 100X.VC. Agnikul plans to scale production of rocket and aerospace components and advance work on reusable systems.

CEO and co-founder Srinath Ravichandran said the funding will help the team work on stage recovery and upper-stage extension. “This fund raise allows us to work on such missions while also focusing on scaling launch frequency and building for the world, from India,” he added.

Agnikul will also strengthen its reusable launch architecture. The company recently secured a patent that extends the operational life of upper stages. Co-founder and COO Moin SPM said, “With growing demand and more than a dozen customers eager to launch with us, scaling our operational depth was the natural next step.”

Investors expressed confidence in the company’s global potential. Arun Kumar, managing partner at Celesta Capital, called Agnikul “a standout example of the cutting-edge deep tech innovation we see in India today.”

The company also recently announced a new large-format metal additive manufacturing unit to expand its 3D-printing capability beyond engines. Agnikul’s customer base spans India, the Middle East, and Australia.

Anirudh A. Damani of Artha Select Fund said the company’s work shows that “India’s private space industry has arrived and the world is now watching closely.”

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OpenAI has entered into a collaboration with Hon Hai Technology Group (Foxconn) to advance work on US manufacturing readiness for next-generation AI infrastructure hardware, the companies announced on November 20.

While the agreement does not include purchase commitments, OpenAI will have early access to evaluate new systems and the option to buy them. The partnership aims to address the growing demand for physical infrastructure that supports increasingly advanced AI models.

Under the initiative, the two companies will co-design and develop multiple generations of AI data centre racks in parallel, aligning OpenAI’s infrastructure roadmap with Foxconn’s engineering and manufacturing capabilities.

Sam Altman, CEO of OpenAI, said the effort represents a generational opportunity to reindustrialise America. “This partnership is a step toward ensuring the core technologies of the AI era are built here. We believe this work will strengthen US leadership and help ensure the benefits of AI are widely shared,” he added.

As part of the collaboration, both sides will work to broaden domestic sourcing, improve rack architecture for US manufacturing, and expand local testing and assembly. The companies said these steps are intended to strengthen the US AI supply chain, speed deployment, and improve reliability.

Foxconn will also manufacture key components for AI data centres in the US, including cabling, networking, cooling, and power systems, to support the buildout of high-performance compute infrastructure.

“We at Foxconn are thrilled to partner with OpenAI… As the world’s largest manufacturer of AI data servers, Foxconn is uniquely positioned to support OpenAI’s mission with trusted, scalable infrastructure,” said Foxconn chairman Young Liu.

Previously, OpenAI announced a multi-year strategic collaboration with Broadcom to co-develop and deploy 10 gigawatts of OpenAI-designed AI accelerators and networking systems, marking a major expansion in OpenAI’s infrastructure capabilities.

Under the partnership, OpenAI will design the accelerators and systems, while Broadcom will provide Ethernet and other connectivity solutions for large-scale deployment across OpenAI facilities and partner data centres. Deployment of the racks is expected to begin in the second half of 2026 and conclude by the end of 2029.

The post OpenAI, Foxconn Partner to Co-Design AI Data Centre Hardware appeared first on Analytics India Magazine.

Chennai-based Agnikul Cosmos has raised about ₹150 crore (~$17 million) in fresh funding on November 22. The company secured the round at a valuation of $500 million. The funds will support its upcoming launches, stage-recovery programme, and its integrated space campus on 350 acres allocated by the Tamil Nadu government.

The round included participation from family offices and institutional investors, including Advenza Global Limited, Atharva Green Ecotech LLP, HDFC Bank, Artha Select Fund, Prathithi Ventures, and 100X.VC. Agnikul plans to scale production of rocket and aerospace components and advance work on reusable systems.

CEO and co-founder Srinath Ravichandran said the funding will help the team work on stage recovery and upper-stage extension. “This fund raise allows us to work on such missions while also focusing on scaling launch frequency and building for the world, from India,” he added.

Agnikul will also strengthen its reusable launch architecture. The company recently secured a patent that extends the operational life of upper stages. Co-founder and COO Moin SPM said, “With growing demand and more than a dozen customers eager to launch with us, scaling our operational depth was the natural next step.”

Investors expressed confidence in the company’s global potential. Arun Kumar, managing partner at Celesta Capital, called Agnikul “a standout example of the cutting-edge deep tech innovation we see in India today.”

The company also recently announced a new large-format metal additive manufacturing unit to expand its 3D-printing capability beyond engines. Agnikul’s customer base spans India, the Middle East, and Australia.

Anirudh A. Damani of Artha Select Fund said the company’s work shows that “India’s private space industry has arrived and the world is now watching closely.”

The post Indian Spacetech Startup Agnikul Cosmos Raises $17 Mn to Build Reusable Rocket appeared first on Analytics India Magazine.

TCS has secured a $1 billion investment from global alternative asset manager TPG to accelerate the expansion of HyperVault, its AI data centre business that aims to build gigawatt-scale, AI-ready digital infrastructure across India.

The deal marks one of the largest private investments into India’s data centre ecosystem and is a critical step in TCS’ plan to become what it calls the world’s largest AI-led technology services company. The company aims to set up AI data centre capacity exceeding one gigawatt over the next few years.

HyperVault will be funded through a mix of equity from TCS and TPG, and debt. Both partners together will commit up to ₹18,000 crore, of which TPG will invest up to ₹8,820 crore.

Depending on the final structure at closing, TPG is expected to hold between 27.5% and 49% in the venture. TPG’s investment is being made through its climate-focused platform TPG Rise Climate and the Global South Initiative, along with its Asia real estate business.

TCS chairman N Chandrasekaran said the partnership will help the company quickly build large-scale AI data centres to serve the rising demand from hyperscalers and AI companies.

He said the capability positions TCS to deliver “complete AI solutions” for global customers and strengthens its ambition to lead in AI-led services.

Jim Coulter, executive chairman of TPG, said data centres sit at the intersection of green energy infrastructure, technology, and real estate, and described the partnership as an opportunity to help build India’s next wave of digital infrastructure “in a climate-positive manner.”

India’s data centre market, currently around 1.5 GW of installed capacity, is projected to cross 10 GW by 2030. Industry estimates show nearly $94 billion has flowed into the segment since 2019, driven by cloud adoption, AI workloads, and hyperscaler expansion.

TCS said HyperVault will offer secure, liquid-cooled, high-density AI data centres with energy-efficient designs and connectivity across major cloud regions. It will work closely with hyperscalers and AI companies to design, deploy, and optimise AI infrastructure for large-scale, real-time applications.

The company said its broader AI strategy spans AI data centres, cloud platforms, AI-led IT services and industry-specific solutions. AZB & Partners and Deloitte advised TCS on the transaction, while TPG was represented by Cyril Amarchand Mangaldas, Latham & Watkins, and Price Waterhouse & Co LLP. The deal is subject to customary approvals.

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