Category: Startup Funding

Origin Lab is building a platform that turns licensed video game worlds into training data for AI systems that need to understand movement, physics, and 3D space. The startup has raised an $8 million seed round as demand grows for better data for world models and physical AI. That matters because labs working beyond text don’t have the neat, internet-scale data firehose that helped large language models take off. Origin Lab launched in 2026. It was started by Anne-Margot Rodde, Antoine Gargot, and Colin Carrier — a founding team pulling from AI, gaming, creator platforms, and data engineering.

What is Origin Lab and how does it work?

At a basic level, Origin Lab sits between game publishers and frontier AI labs. It licenses game-world content directly from rights holders. It captures that content through its own software pipelines, enriches it with structured metadata, and delivers custom datasets to model builders. So the buyer isn’t getting random gameplay clips off the internet. It’s getting a researcher-ready package built to spec.

That capture layer is the interesting part. Origin Lab records game worlds with engine-level access, pulling in synchronized video, keyboard and mouse input, camera telemetry, and depth information from the render pipeline. In practice, that means a lab can train on more than pixels. It can also learn from how a scene changes, where the camera moved, what inputs caused that movement, and what the environment state looked like at the time.

The platform also goes beyond raw capture. Origin Lab is building software for enrichment, QA, search, annotation, packaging, and delivery. Its datasets can include gameplay footage, 3D worlds, motion capture, and digital assets, all structured for multimodal training instead of dumped into a folder and left for the buyer to clean up later. That cuts out a lot of ugly manual work — licensing, formatting, and validation.

For customers, the before-and-after is pretty stark. Before, a lab might scrape public footage, negotiate one-off deals, or try to build synthetic environments from scratch. With Origin Lab, the pitch is simple: ask for a rights-cleared dataset with the signals you need, and get something source-controlled and usable for training from day 1. Rodde summed up the company’s thesis in one blunt line: “That data essentially lives in video games.”

Who founded Origin Lab and what makes them credible?

The founding story

Origin Lab came together around a pretty specific bet: AI is moving from language into environments, and the next bottleneck is no longer model architecture alone — it’s access to better world data. Gargot has said the company grew out of a shared ambition with Rodde to build a rights-cleared content platform for the AI era, with Carrier joining after that early thesis was already taking shape. The team’s view is that AI shouldn’t keep feeding on scraped content when richer, licensed environments already exist.

The company is based in California, with San Francisco listed as its headquarters. It’s still tiny — LinkedIn lists it in the 2-10 employee range. That makes the early commercial progress more notable than the org chart.

Founder fit

Rodde is the commercial and partnerships operator in the trio. She serves as co-CEO and chief commercial officer at Origin Lab, and she also comes from the gaming creator economy, where she built Creators Corp. That background fits the job here because Origin Lab isn’t just selling software. It has to convince rights holders that their assets can become a business, not a legal headache.

Gargot is the technical builder on the AI side. Origin Lab lists him as CTO, and his profile points to 10+ years in data, machine learning, and AI engineering. That matters because the company’s product isn’t a simple marketplace listing. It needs capture systems, multimodal structuring, and data pipelines that can work across different titles and hardware setups.

Carrier brings product and platform experience from the creator internet. He now serves as co-founder, co-CEO, and CPO at Origin Lab. Before this, he built Oooh, and his earlier career included years at Twitch — enough that former colleagues have described him as one of the people behind TwitchCon. He also holds patents around remixable video content and per-frame metadata capture. That feels unusually relevant for a company built around structured audiovisual data.

Early traction and fundraising details

For a company that only surfaced publicly in 2026, Origin Lab already has some real signals. It has exclusive partnerships with more than 20 game publishers covering more than 50 titles, and it’s already under contract with a leading frontier AI lab. That’s the kind of traction investors care about because it shows both sides of the marketplace moving at once.

Lightspeed Venture Partners led the $8 million seed round. SV Angel, Eniac, Seven Stars, and FPV joined in, along with angel checks from Twitch co-founder Kevin Lin and Cruise founder Kyle Vogt. The money is earmarked for capture and enrichment tech, publisher partnerships, and the engineering and research teams building dataset creation, QA, search, annotation, packaging, and delivery systems.

How Origin Lab compares with synthetic data rivals

Origin Lab’s closest competition doesn’t fit into one clean bucket. On one side, there are synthetic-data companies like Parallel Domain, Datagen, and Synthesis AI, which generate or simulate training data for computer vision and autonomy use cases. On the other, there’s the old-fashioned alternative: scrape footage from the web, hire people to clean it up, and hope the legal and quality issues don’t explode later.

What makes Origin Lab different is that it’s not promising to invent worlds from scratch. It’s packaging licensed worlds that already contain physics, player behavior, scene structure, and controllable interactions. That gives it a cleaner rights story than scraped footage and a different data profile than purely synthetic pipelines. Investors are backing that supply advantage — exclusive publisher relationships plus source-level capture — more than a vague “AI for gaming” pitch.

Why are investors backing Origin Lab now?

This round matters because it validates a pretty sharp thesis: the supplier layer in AI can become a big business when the biggest labs all hit the same bottleneck at once. Faraz Fatemi at Lightspeed pointed to companies like Scale AI as proof that data vendors can scale revenue fast when they become essential infrastructure. His read was even simpler than that — “the bottleneck for all of them is data.”

For Origin Lab, the cash should help turn a clever thesis into actual operating infrastructure. More capture tech means broader support across titles, more enrichment and QA means less bespoke wrangling for each buyer. More publisher relationships mean the company can become a real supply hub instead of a one-off broker doing custom projects behind the scenes.

There’s also a timing edge here. Labs working on world models, robotics, and multimodal systems need data that shows cause and effect, not just nice-looking frames. If Origin Lab becomes the place where they source that data legally and reliably, it could matter a lot more than its current size suggests. If it doesn’t, it risks ending up as a services business with fancy branding. That’s the tension to watch.

How big is the market around Origin Lab?

The synthetic data generation market is still small by big-tech standards, but it’s growing fast. Grand View Research puts the market at $218.4 million in 2023 and projects it to reach $1.79 billion by 2030, a 35.3% compound annual growth rate. And the fastest-growing segment in that report is image and video data — the exact area where Origin Lab is operating, even if its datasets are licensed and structured rather than fully synthetic.

The other trend is broader than synthetic data. AI labs are shifting from text-heavy systems to models that need to reason about environments, actions, and state changes. That’s why the conversation has moved toward world models, robotics, simulation, and multimodal training. Once that shift happens, flat internet content starts looking a lot less useful. Highly structured interactive data starts looking expensive — and valuable.

And there’s a legal undertone here too. Scraped training data has already created messy public blowback, including the December 2024 noise around early Sora outputs that appeared to echo video game and streamer footage. Origin Lab is basically selling the opposite of that mess: consent, provenance, and cleaner inputs. That won’t solve every training-data problem. But it’s a much more serious answer than “just scrape more.”

What should you watch next at Origin Lab?

Origin Lab has a smart pitch and a credible founding team. It also has the right enemy: bad, flat, legally murky training data. That’s a real problem, and the company is attacking it with something more concrete than most AI infrastructure startups manage in their first year.

The next test for Origin Lab is scale. Can it keep signing publishers, standardize messy game data across lots of titles, and become a repeat supplier to major labs instead of a niche broker for special projects? If it can, this seed round will look cheap in hindsight. If not, the idea will still be good — just smaller than the hype around physical AI makes it sound today.

Read how Mind Robotics raised over $1B to build AI-powered industrial robots designed for complex factory tasks that traditional automation still struggles to handle.

FAQ

– What funding did Origin Lab raise? 

 Origin Lab raised an $8 million seed round in May 2026. Lightspeed led the round, with participation from SV Angel, Eniac, Seven Stars, FPV, and angels including Kevin Lin and Kyle Vogt. That gives the company both venture backing and operators who know gaming and autonomy firsthand.

– How does Origin Lab turn video games into AI training data? 

 It licenses content directly from game publishers and then captures structured signals from the games themselves. That includes video, player inputs, camera telemetry, depth data, and metadata around scene composition and environment state. That gives AI labs more useful material than ordinary gameplay footage.

– Who are the founders of Origin Lab? 

 Origin Lab was started by Anne-Margot Rodde, Antoine Gargot, and Colin Carrier in 2026. Rodde brings partnerships and gaming-creator experience, Gargot comes from ML and AI engineering, and Carrier has a background in Twitch and creator-video products. The team spans both data infrastructure and content distribution.

– Is Origin Lab a synthetic data company? 

 Not exactly. It overlaps with the synthetic data market because it serves AI labs that need structured training data, but its core product is licensed, source-captured data from real game worlds rather than entirely generated scenes. That puts it in a different category from companies like Parallel Domain, Datagen, or Synthesis AI.

Mind Robotics builds AI-powered industrial robots for factory floors. Its latest funding news is big even by 2026 standards: the Rivian spinout has raised another $400 million just 2 months after landing $500 million, pushing total funding past $1 billion. The bet here is simple enough — factories still rely on people for a lot of messy, variable work that classic automation hasn’t handled well. RJ Scaringe, Rivian’s founder and CEO, created Mind Robotics in 2025 to go after that problem directly after deciding existing startups weren’t built for industrial work at real scale.

What is Mind Robotics and how does it work?

Mind Robotics is building intelligent robotics for industrial deployment, starting on the factory floor rather than in homes or research labs. Its system is full-stack: AI models and purpose-built robotic hardware. It also includes the deployment infrastructure needed to put those machines into real manufacturing environments. That matters because the jobs it’s targeting aren’t simple pick-and-place routines — they’re dexterous, variable, reasoning-heavy tasks that still break a lot of conventional automation.

The workflow is pretty clear. It starts with production-scale data from active Rivian manufacturing lines and uses that data to train and refine the models. Then it validates the robotic systems in the same kind of live industrial setting where they’re meant to operate. That’s the “data flywheel” pitch: build with a customer, not for a hypothetical customer.

Mind also isn’t pitching a one-trick machine. The company wants a platform that can generalize across core factory tasks and work safely alongside humans. That puts it somewhere between old-school fixed automation and the current wave of humanoid robot startups chasing general-purpose labor.

Scaringe has also been unusually blunt about the hardware philosophy. He has said Mind is leaning toward more traditional factory robot designs, not flashy humanoids, and joked that “doing cartwheels does not create value in manufacturing.” He’s also hinted Rivian’s custom robotics processor could eventually be useful to Mind. That suggests the startup may inherit more than just factory access from its parent.

Who founded Mind Robotics and what makes it credible?

The company started as an internal idea, then became a standalone bet

Mind Robotics began in 2025 under the internal name “Project Synapse.” Scaringe said he started it because he didn’t think the existing robotics field had the right mix of industrial know-how and supply-chain realism. It also lacked the model-training advantages needed to automate factory work at scale. In March, he described the effort as an attempt to build “robotics with human-like skills.” Ambitious wording, sure. But it explains why the company is chasing dexterity and adaptation instead of narrow repetitive automation.

Scaringe has actual manufacturing scar tissue

This isn’t a founder wandering in from consumer software. Rivian’s 2026 proxy says Scaringe founded Rivian in June 2009 and led the company through its major technical and manufacturing milestones, including vertically integrated technology platforms, multi-program manufacturing capabilities, and major partnerships. He holds a B.S. from Rensselaer Polytechnic Institute and both an M.S. and Ph.D. in mechanical engineering from MIT’s Sloan Automotive Laboratory.

That background is the whole case for founder-market fit. Mind Robotics is trying to automate work inside demanding production systems, and Scaringe has spent more than a decade building one. In his own words, he didn’t want Rivian’s future manufacturing dependency tied to robotics startups that hadn’t industrialized a product or built real supply chains.

There’s already a track record of spinning projects out

Mind isn’t the first time Scaringe has carved a new company out of Rivian’s internal work. He also helped create Also, the micromobility startup that spun out in 2025 and has raised more than $300 million to date. That doesn’t make every spinout a winner, obviously. But it does show he’s trying to turn internal technology programs into separate venture-backed businesses instead of burying them inside one corporate structure.

Early signals are promising, but still early

Mind Robotics is not a mature commercial robotics company yet. Its materials frame Rivian as the initial deployment partner, and Scaringe told The Wall Street Journal in March that Mind expects a large number of robots to be deployed by the end of 2026. So the company has real-world validation conditions and a live factory environment. The next proof point is execution.

Getting systems onto the floor, keeping them reliable, and showing they can earn their keep.

The fundraising is massive, and the cap table tells a story

Kleiner Perkins led the new $400 million round, with participation from the venture arms of Volkswagen and Salesforce. It came just 2 months after a $500 million Series A, and after a $115 million seed round led by Eclipse in late 2025. That sequence puts Mind above $1 billion in total funding and at a valuation north of $3 billion in barely half a year.

Competition is crowded — but Mind isn’t copying the pack

Mind enters a robotics market that already has heavyweight names. Apptronik closed a $403 million Series A to push its Apollo humanoid into industries including automotive, electronics manufacturing, and logistics. TechCrunch has also grouped Apptronik with Boston Dynamics, Agility Robotics, and Figure as prominent names in the humanoid race. Mind’s positioning is different: fewer sci-fi demos, more task-specific industrial systems trained in active factories.

The legacy alternative is even less glamorous. A lot of factories still buy fixed-function robotics from established vendors and then bolt on integrators to make the systems usable. In warehouse robotics alone, major incumbents include ABB, Honeywell, KUKA, OMRON, and Yaskawa. Mind’s argument is that the next step isn’t just another robot arm. It’s a system that can handle variability without requiring a factory to be redesigned around the machine.

Why does Mind Robotics funding matter now?

This round matters because it turns Mind from an intriguing spinout into a company that can afford industrial speed. Building robots for real factories is brutally expensive — hardware, safety, deployment, model training, and long validation cycles all eat cash. A startup that wants to move from prototype to plant floor needs more than a flashy demo and a small seed check.

The investor mix matters too. Volkswagen’s venture arm isn’t just a financial name on the cap table; Volkswagen is already tied to Rivian through a software joint venture launched in November 2024, with plans to use Rivian’s architecture in future vehicles and up to $5.8 billion committed by 2027. Salesforce Ventures, meanwhile, gives the round a software-and-enterprise stamp that’s different from a pure robotics wager.

For Rivian, there’s a deeper implication. If Mind works, Scaringe may have found a way to turn one automaker’s manufacturing pain into a standalone robotics business — using real factory data as the moat instead of treating operations as a cost center. That’s a much more serious thesis than “humanoids are cool.”

What does Mind Robotics funding say about factory automation?

The timing isn’t random. The International Federation of Robotics says 4.664 million industrial robots were operating worldwide in 2024, up 9% year over year, and annual installations in 2024 were the second-highest ever recorded. In the Americas alone, installations stayed above 50,000 for a fourth straight year, with the U.S. accounting for 34,200 units. This is already a huge operating market, not a science experiment.

There’s also room above the classic automotive-robot model. Warehouse robotics was a $4.31 billion market in 2022 and is projected to reach $17.29 billion by 2030, with a 19.6% CAGR. Growth is being pushed by labor shortages and e-commerce pressure. Safety needs also matter, as does the need to raise throughput without endless hiring. That’s the kind of environment where investors start paying up for more adaptable automation.

So the macro read is this: factories don’t just want more robots. They want robots that can deal with variation and work near people. They also need to fit into existing operations without months of custom engineering every time something changes. That’s the opening Mind is trying to exploit.

Conclusion

Mind Robotics funding is starting to look less like a side project and more like a serious industrial platform bet. The money is there. The founder credibility is there. The missing piece is the hard one — proving that these robots can move from promise to uptime inside Rivian plants by the end of 2026, and then beyond them.

Read how upGrad raised ₹360 Cr to expand AI-led learning, strengthen workforce training, and support its proposed Unacademy acquisition.

FAQ

– What funding did Mind Robotics just raise?  

 Mind Robotics just raised $400 million in a new round led by Kleiner Perkins. That came only 2 months after a $500 million Series A and follows a $115 million seed round from late 2025, pushing total funding above $1 billion and valuing the company at more than $3 billion.

– How does Mind Robotics work?  

 Mind Robotics combines AI models and purpose-built factory robotics hardware. It also includes deployment infrastructure in one industrial automation stack. The key twist is that it trains and refines those systems using production-scale data from Rivian’s active manufacturing lines, then aims to deploy the robots in those same environments.

– Who founded Mind Robotics?  

 RJ Scaringe created Mind Robotics in 2025 and serves as its chairman. He’s the founder and CEO of Rivian, which he started in June 2009, and he has degrees in mechanical engineering from RPI and MIT — a background that makes a lot more sense for industrial robotics than a generic software résumé would.

– Is Mind Robotics a humanoid robot company?  

 Not really — at least not by the way Scaringe has described it so far. Mind is focusing on industrial robots for factories and has emphasized more traditional or purpose-built designs over humanoid showpieces, putting it in the broader factory automation and industrial robotics category rather than the pure humanoid race.

upGrad is an online higher education and workforce training company, and its latest upGrad funding round brings in ₹360 crore as it lines up the proposed Unacademy acquisition and a bigger push into AI-led learning. The problem it’s chasing is pretty simple: Indian learners and employers don’t want one-off courses anymore — they want outcomes, credentials, mobility, and often a job link at the end. Founded in 2015 by Ronnie Screwvala, Mayank Kumar, Phalgun Kompalli, and Ravijot Chugh, upGrad is now trying to turn itself into a broader learning-and-work platform instead of staying just another course marketplace. That’s why this internal round matters more than the headline number suggests.

What is upGrad and how does it work?

upGrad sells structured learning, not random video libraries. A learner typically comes in through a goal — promotion, certification, AI training, a degree, study abroad, or career transition — and then moves through a guided program with coursework, mentorship, projects, support, and in many cases career services. Its homepage now spans online programs, offline learning options, study abroad, and newer AI-focused courses. That tells you how wide the company wants the funnel to be.

One of the clearest examples is upGrad Abroad. The model lets students begin online in India and then move on-campus later, with credits accepted at 18 global universities on selected pathways. The offering also bundles counselling, application support, visa help, and course selection. It’s the messy admin work that usually scares off first-time international applicants. And yes, the pitch is cost and flexibility: start at home, spend less, then finish overseas.

There’s a separate enterprise layer too. upGrad for Business works with employers on workforce skilling programs, especially around digital, leadership, and tech capabilities. That’s different from selling a single course to a consumer. It turns the company into a training vendor for firms that need custom learning tracks for teams. A steadier business, if it works.

What upGrad removes, basically, is fragmentation. Before platforms like this, a learner might juggle one provider for exam prep, another for certifications, a consultant for overseas admissions, and a separate route for placement help. upGrad’s bet is that people will pay for one guided system if it cuts confusion and gets them from “I need a better career move” to “here’s the credential, pathway, and support.”

Who founded upGrad and why are investors still backing it?

The founding story

upGrad started in 2015 after the founding team zeroed in on a blunt gap: professionals tend to stop structured learning once they enter the workforce, even as industries keep changing under them. The company was built around that tension from day one — not school tutoring, but working-professional upskilling tied to career progression. Ronnie Screwvala is co-founder and chairman, Mayank Kumar is co-founder and managing director, and Phalgun Kompalli is co-founder. Ravijot Chugh is also a co-founder and product leader.

Why the founders had market fit

Screwvala wasn’t coming in as a first-time operator. Before upGrad, he built UTV into a major media company and exited it to Disney in 2012 at an enterprise value of about $1.4 billion. That matters because edtech at scale isn’t just about course content — it’s a consumer business and a brand business. It’s also an execution business. upGrad’s original setup paired that scale-building experience with a team focused on product, curriculum, and learner outcomes for professionals.

Traction, product spread, and where the company sits now

This isn’t a pre-launch story. upGrad is already live across consumer courses, offline centres, study abroad, and enterprise skilling. The company has crossed 10 million enrolments across 100+ nations. That gives some sense of the reach it built before this latest round.

Fundraising details and the Unacademy angle

The fresh capital is internal, which stands out on its own. Ronnie Screwvala put in ₹300 crore, while existing investors Temasek, IFC, and 360 One joined on a pro-rata basis; Temasek contributed ₹45 crore, and IFC plus 360 One added around ₹16 crore together. The round totals ₹360 crore and comes just as upGrad prepares to close its proposed Unacademy acquisition. Days before this financing surfaced, upGrad filed a merger application with the Competition Commission of India on May 7, 2026.

The use of funds is broad, not narrow. upGrad plans to spend across test prep and study abroad. Enterprise skilling, workforce development, and AI-led learning products are also on the list. Moneycontrol first reported in November that the two companies were discussing a deal valued at $300 million to $400 million, and those talks were on-and-off for months before resuming and moving toward a term sheet this year. In its CCI filing, upGrad said the Unacademy buy would give it entry into online test preparation, where it doesn’t currently operate.

Who upGrad is competing with

upGrad’s real competition depends on the vertical. In professional upskilling, it runs into players like Simplilearn, Great Learning, Coursera, Udemy, and Emeritus. In study abroad, it competes with counsellors, pathway providers, and the old-school education-consultancy model. If the Unacademy deal closes, the battleground gets tougher — UPSC, JEE, NEET, and GATE are already crowded with established brands and offline coaching muscle. upGrad’s edge is that it’s trying to bundle degrees and skilling. Mobility and now potentially test prep sit in the same company. That’s ambitious. It’s also messy to execute.

Why does this upGrad funding round matter?

Because this isn’t just growth capital. It’s control capital.

An internal round led so heavily by the founder usually signals speed and conviction more than optics. upGrad didn’t bring in a flashy new outside lead; it leaned on insiders who already know the company and still wanted to write checks. Ronnie Screwvala alone putting in ₹300 crore tells you this wasn’t treated like a small bridge or a token top-up. It was meant to give the company room to make a strategic move.

That move is Unacademy. And if the deal closes, upGrad expects to have nearly ₹900 crore in cash tied to that transaction, taking its overall capital pool to roughly ₹1,260 crore. That gives it ammunition to go after adjacent categories instead of staying boxed into working-professional education. It also lets the company spend on offline and online formats at the same time. Expensive, but probably necessary now that pure digital edtech has lost some shine.

There’s another reason this matters. upGrad is trying to be an integrated learning-and-work platform at a moment when standalone edtech models look thin. Test prep, study abroad, enterprise skilling, certifications, placements, and AI products don’t naturally fit together unless the customer journey is designed really well. If upGrad can stitch those pieces together, the round looks smart. If it can’t, this becomes a holding pattern dressed up as expansion.

How big is India’s edtech market right now?

The market is still large, even after the post-pandemic reset. Grand View Research estimates India’s education technology market generated $6.6 billion in revenue in 2024 and could reach about $17.0 billion by 2030, growing at a 16.9% CAGR from 2025 to 2030. It also calls India the fastest-growing regional market in Asia Pacific in this category.

The shape of demand has changed too. Investors and customers now care a lot more about job-linked learning, blended delivery, and business models that don’t depend on endless discounting. That’s part of why Indian edtech companies have been broadening into enterprise skilling, overseas education, and hybrid formats instead of betting everything on one audience. The sector is still in consolidation mode, but it’s a more disciplined version than the 2021 boom years.

Final take on the upGrad funding round

The upGrad funding round is really a bet on consolidation.

₹360 crore by itself is useful, sure. But the bigger story is that upGrad wants to own more of the learner lifecycle — from test prep and degrees to study abroad and workforce training — while layering AI into the product mix. The next thing to watch is whether the Unacademy deal closes on schedule, and whether upGrad can turn a collection of adjacencies into one business that actually feels coherent.

Read how Exaforce raised $125M to build an AI-native security operations platform that helps enterprises detect, investigate, and respond to cyber threats with autonomous AI agents.

FAQ

– What is the latest upGrad funding round? 

 The latest upGrad funding round is an internal financing of ₹360 crore. Ronnie Screwvala led it with ₹300 crore, while Temasek, IFC, and 360 One also participated, as the company prepares to close its proposed Unacademy acquisition.

– How does upGrad actually work for learners? 

 upGrad works like a guided learning platform rather than a loose course catalog. It offers structured programs in higher education, skilling, study abroad, and enterprise learning, and for some study-abroad pathways it lets students start online in India before moving to partner universities overseas.

– Who founded upGrad? 

 upGrad was founded in 2015 by Ronnie Screwvala, Mayank Kumar, Phalgun Kompalli, and Ravijot Chugh. Screwvala brought major company-building experience from UTV, while the broader founding team built the platform around working professionals who needed to upskill without leaving their jobs.

– Is upGrad a test-prep company or an upskilling company? 

 Right now, it’s primarily an upskilling, higher education, study-abroad, and workforce-training company. But if the Unacademy acquisition goes through, upGrad will gain a direct route into online test prep categories like UPSC, JEE, NEET, and GATE, which would make it a much broader edtech player than it is today.

Exaforce builds software that helps security teams detect, investigate, and respond to cyber threats with AI agents instead of bouncing between a stack of separate tools. The Bay Area startup has now raised $125 million in fresh funding to push deeper into agentic security operations, a category getting crowded fast as enterprises look for faster answers to AI-assisted attacks. The pitch is simple: defenders can’t keep up when alerts pile up and zero-days spread quickly. Analysts still spend too much time reconstructing context by hand. Exaforce was founded in 2023 by Ankur Singla, Ariful Huq, Jakub Pavlik, and Devesh Mittal.

What does Exaforce’s agentic security operations platform do?

Exaforce is building an AI-native SOC platform that collects cloud and SaaS telemetry into a real-time security knowledge graph. The platform uses AI agents for threat detection, triage, investigation, and response through tools like Exabot Detect, Exabot Triage, Exabot Investigate, and Exabot Respond. It’s meant to handle detection and triage. Investigation and response happen inside one operating layer.

What makes that interesting is the order of operations. Many AI security tools still rebuild context during investigations by checking logs, APIs, and tickets one step at a time. Exaforce does the harder work upfront by connecting identities, permissions, configurations, files, code, and cloud activity as data arrives. That lets its agents retrieve context instead of rebuilding it on demand. In the company’s words, that cuts typical investigations from many minutes to under a minute.

The tooling looks a lot more like an analyst workspace than a chatbot wrapper. Exaforce’s platform supports natural-language queries and a conversational, visual data explorer. The platform also includes built-in workflows and a data pipeline that organizes and connects large volumes of telemetry data efficiently. This helps security teams avoid switching between SQL queries, scripts, APIs, and multiple dashboards during investigations.

There’s also a technical bet underneath all of this. Exaforce says its multi-model AI engine combines semantic, behavioral, and knowledge models rather than leaning on an LLM alone. The idea is to reduce hallucinations and improve consistency. It also makes the output more auditable for security teams that can’t afford fuzzy answers. The startup has also introduced “vibe hunting,” a natural-language approach to threat hunting that replaces rigid query-based searches.

Who founded Exaforce and how is it positioned in agentic security operations?

How Exaforce started

Exaforce came out of a pretty specific frustration. The founding team had experience securing large cloud environments and saw SOC teams struggling to stay ahead of threats. Other team members had worked on large language models and believed security teams needed specialized AI systems instead of generic models. They started Exaforce to speed up complex security tasks with specialized AI agents and better data infrastructure.

Why this team fits the problem

Ankur Singla gives the startup real founder-market fit. Before Exaforce, he founded Contrail Systems, which Juniper Networks acquired, and later founded Volterra, which F5 acquired. That history matters because Exaforce isn’t selling a lightweight AI add-on. Exaforce is building a deep infrastructure platform for enterprise security teams, and Singla has experience building and selling enterprise software companies.

The broader team adds credibility too. Some early Exaforce team members came from F5 and Palo Alto Networks, while others had worked on LLMs at Google. Co-founder Jakub Pavlik previously co-founded tcp cloud, later acquired by Mirantis, and held engineering leadership roles at Volterra and F5. The team brings experience across cloud infrastructure, security operations, and AI engineering.

Traction, fundraising, and competition

The startup is no longer in pure demo mode. Exaforce brought its product to market in the fourth quarter of 2025 after roughly 2 years of work with design partners. Since launch, it has added 20 customers, including Replit and Guardant Health, and expects to reach 40 to 50 customers by the end of 2026. Mayfield also said the company has grown to more than 130 employees and processed millions of investigations.

On the money side, Exaforce has raised a $125 million Series B after a $75 million Series A last year, bringing total funding to $200 million. The new round included HarbourVest, Peak XV, Mayfield, Khosla Ventures, Seligman Ventures, and AICONIC. TechCrunch reported the Series B valued the company at $725 million. Exaforce says the money will go into its core platform, especially the multi-model AI stack and the real-time knowledge graph. It also plans expansion in Japan and Europe, along with more spending on customer success, research, MDR oversight, and support.

Competition is already intense. Startups such as 7AI, Dropzone AI, and Prophet Security are chasing the same AI-for-SOC budget, while bigger platforms from Palo Alto Networks and CrowdStrike still own a lot of buyer attention. Exaforce’s clearest point of separation is architectural: it argues that legacy SIEMs and many AI SOC tools still reconstruct context mid-investigation, while its system keeps live context ready at ingest. That’s the bet investors are backing — not just more automation, but a faster and cheaper way to reason about security events at enterprise scale.

Ankur Singla put that positioning plainly: “We built Exaforce to be the platform defenders actually work in, not just an AI layer on top of existing tools.” Vinod Khosla framed the investor case just as directly: “When the cost of defence drops by an order of magnitude, the entire calculus of security changes.”

Why did Exaforce raise $125M now?

This round matters because Exaforce is trying to cross a hard boundary: from impressive early product to global security platform. That jump gets expensive fast. Selling into enterprise security teams means long deployments, high support expectations, constant product tuning, and a real services layer around the software. Exaforce isn’t just hiring researchers. It’s putting money into MDR oversight, customer success, and support.

The timing also says something about investor appetite. A year after the Series A, the company came back with a much larger round and one of the bigger financings in the emerging AI SOC segment. That usually means two things: the product is landing with customers, and investors think the window to establish category leadership is open right now, not 3 years from now. Mayfield’s note about production deployments, millions of investigations, and a tripled valuation backs that up.

For customers, the practical implication is simple. Exaforce now has the cash to expand product breadth and geographic reach without slowing down. If the company executes, buyers should expect a more mature platform and tighter workflows across detection and response. They should also expect stronger local coverage in markets beyond the US.

How big is the market for agentic security operations?

The market Exaforce is chasing is large enough to attract a lot of capital and messy enough to create room for new platforms. Frost & Sullivan estimates the global modern SIEM market will grow from $7.13 billion in 2024 to $13.55 billion by 2029, with cloud SIEM revenue growing at a 17.5% CAGR over that period. That’s a useful proxy because modern SIEM platforms are steadily absorbing analytics, automation, and AI-assisted investigation features that used to sit in separate tools.

The services side is moving too. Grand View Research estimated the managed detection and response market at $3.5 billion in 2023 and projects it will reach $15.31 billion by 2030. That lines up with what buyers are actually asking for now: fewer point tools and faster triage. They also want a mix of software plus expert oversight when in-house teams are stretched thin.

And that’s why Exaforce’s timing doesn’t feel random. AI-driven attacks are pushing enterprises to modernize security operations, but the real pressure is operational. Security teams need automation that’s explainable, fast, and cheap enough to run continuously across cloud-heavy environments. The winners probably won’t be the noisiest AI vendors. They’ll be the ones that can fit into day-to-day SOC work without making analysts trust magic.

What should customers watch next from Exaforce?

Exaforce has enough money now to stop being judged like an intriguing startup and start being judged like a platform company.

That changes the standard.

The next thing to watch is whether its agentic security operations story holds up under broader deployment. Can it expand beyond early believers, keep false positives low, and make the human-plus-agent workflow feel reliable enough for real incident response? If it can, this round will look smart. If it can’t, it’ll be another reminder that AI security products sound a lot better in pitch decks than they do at 3 a.m. in a live SOC.

Read how Nivasa Finance raised ₹25 Cr in seed funding from Prime Venture Partners, Blume Ventures, and Whiteboard Capital to simplify affordable home-loan access for rural and semi-urban borrowers across India.

FAQ

– What is Exaforce’s latest funding round?  

 Exaforce raised a $125 million Series B in May 2026. The round followed a $75 million Series A a year earlier and pushed total funding to $200 million, with investors including HarbourVest, Peak XV, Mayfield, Khosla Ventures, Seligman Ventures, and AICONIC.

– How does Exaforce work for security teams?  

 Exaforce gives SOC teams a single platform that unifies telemetry, builds a real-time knowledge graph, and then uses AI agents to detect, triage, investigate, and respond to threats. The product includes Exabot Detect, Triage, Investigate, and Respond, plus natural-language search and visual investigation tools that reduce the need for manual queries and scripting.

– Who founded Exaforce?  

 Exaforce was founded in 2023 by Ankur Singla, Ariful Huq, Jakub Pavlik, and Devesh Mittal. Singla previously built Contrail Systems and Volterra, while Pavlik had already co-founded tcp cloud and later led engineering work at Volterra and F5, which gives the team a strong mix of cloud, infrastructure, and security depth.

– Is Exaforce a SIEM company or an MDR company?  

 It sits somewhere in between, which is part of the appeal. Exaforce is pitching an AI-native security operations platform that overlaps with modern SIEM, SOC automation, and MDR by combining data ingestion, investigation, response workflows, and optional managed oversight in one system.

Nivasa Finance is a Bengaluru startup that connects underserved home-loan borrowers with banks and NBFCs for affordable housing finance. The company has raised ₹25 crore in seed funding from Prime Venture Partners, Blume Ventures, Whiteboard Capital, and several angel investors. Nivasa Finance wants to help rural and semi-urban borrowers access formal home loans more easily. Founded in 2025 by Samit Shetty and Hitesh Saraf, the startup is building a faster and clearer housing loan process. Easier to execute on the ground, too.

What is Nivasa Finance and how does it work?

Nivasa Finance isn’t a bank. It works as a distribution and fulfilment layer for secured housing credit, helping borrowers with onboarding and documentation. It also matches them with lenders and handles loan disbursal, while the actual loan sits with partner banks, NBFCs, small finance banks, or housing finance companies. That matters because the company is trying to remove the part of the journey where customers bounce between branches, agents, and paperwork without knowing which lender will actually say yes.

The product flow is pretty direct. A borrower or local advisor can start a lead through WhatsApp or Nivasa’s online portal, submit personal and financial details digitally, and move into remote assessment before the case is routed to the most suitable lending partner. Nivasa has also built customer-facing digital interfaces and app-based journeys. It still keeps doorstep service in the loop for people who need hand-holding offline.

That hybrid model is the point. Nivasa doesn’t pretend affordable housing borrowers in non-metro India will all self-serve through an app. Instead, it combines field advisors and telecalling with screening and branch coordination through digital workflows, so the lender gets cleaner files and the borrower doesn’t have to decode the mortgage process alone. Its Mysuru branch, opened in June 2025, was built to support walk-ins and advisor engagement. It also handles lead screening, telecalls, and disbursal coordination.

The service is pitched around small-ticket home loans — the website advertises loans from ₹5 lakh to ₹35 lakh — and it doesn’t charge customers commission or service fees. On the partner side, Nivasa lists registered relationships with lenders such as Slice Small Finance Bank, Muthoot Housing Finance, Jana Small Finance Bank, and Veritas Finance. That gives a clearer picture of the kind of institutions it’s plugging into.

Who founded Nivasa Finance and what makes them credible?

The founding story

Nivasa Finance was founded in 2025 by Samit Shetty and Hitesh Saraf in Bengaluru. The company’s pitch is aimed at borrowers building homes in rural and semi-urban markets — people who often have fragmented income proof, thin documentation, or simply no easy path into formal mortgage underwriting. That’s why Nivasa talks about itself less like a pure lender and more like infrastructure that helps lenders enter a hard-to-serve segment with less friction.

Founder market fit

Shetty is the more obvious operator for the distribution and lending side. Before Nivasa, he was vice president of strategy and M&A at Navi Technologies and CEO of Navi Finserv, where he worked on digital personal and housing loans. Earlier, he founded and ran Chaitanya India Micro Finance, later acquired by Navi, and he studied at IIM Ahmedabad after completing an engineering degree from Bangalore University.

Saraf brings the credit brain. He leads credit policy and Nivasa’s lender allocation engine. Before this, he built and ran credit-risk functions at SmartCoin and ZestMoney, where he worked on AI- and ML-based risk engines and automated decisioning systems. He has 18 years of experience across fintechs, multinational banks, and credit bureaus, and studied mathematics at IIT Kanpur. A strong fit.

Early traction and fundraising

Nivasa is already live, not just testing slides. It has partnered with more than 10 lending institutions across banks, small finance banks, housing finance companies, and NBFCs, and the source article says it has disbursed more than ₹20 crore while piloting the model in Mysore and Mandya. Its own properties also show operating branches in Mysuru, Mandya, and Ramanagara. That suggests the company is building an actual field footprint instead of staying purely digital.

The new round is a seed round of ₹25 crore. Prime Venture Partners led it, with Blume Ventures, Whiteboard Capital, and angel investors also participating. Nivasa will use the money over the next 12 months to expand geographically, strengthen its distribution network, deepen partnerships with banks, NBFCs, and HFCs, and invest harder in field execution. It’s also exploring an NBFC licence, which would move it closer to the lending stack instead of remaining only a distribution intermediary.

Competition and market positioning

Here’s where Nivasa gets interesting. It doesn’t sit neatly beside large affordable housing finance companies such as Aadhar Housing Finance, Aavas Financiers, Home First, Aptus Value Housing Finance, or India Shelter, because those firms actually lend off their own books. It’s also different from Easy Home Finance, which is a mortgage-tech lender with its own lending operations and broader pan-India expansion plan.

Nivasa’s closer comparison is the old patchwork it’s trying to replace: local DSAs and branch-level sourcing. Manual screening, too. And lender shopping done through personal networks. Its edge, if it works, is speed plus control — a digital front end, a credit-aware matching engine, and a field network that can still show up at a customer’s doorstep. Investors are backing that distribution-first approach in secured lending, especially in markets where formal mortgage supply exists but origination and fulfilment are still broken. That’s a smart bet. But it’s still a bet.

Why does Nivasa Finance’s ₹25 Cr seed round matter?

Seed rounds in lending-adjacent businesses usually go one of two ways. Either the money disappears into customer acquisition with no moat, or it funds the messy operational layer others don’t want to build. Nivasa looks more like the second case.

The company isn’t using this round to chase vanity scale. It’s putting capital into geography and lender relationships. Field execution is in there too. Those are the exact pieces that determine whether a home-loan fulfilment model can work outside top metros. For borrowers, that means more local access points and faster case movement. For lenders, it means better reach into rural and semi-urban demand without building every last distribution pipe themselves.

And the NBFC angle matters too. If Nivasa eventually secures that licence, it gets the option to move from being a facilitator into owning more of the lending economics. That doesn’t happen overnight, and it adds regulatory weight. But it does show ambition beyond being “just another DSA” with a nicer app.

How big is India’s affordable housing finance market?

The timing isn’t random. India’s individual housing finance market is currently valued at about ₹33 trillion and is projected to reach ₹77 trillion to ₹81 trillion by FY30, implying a 15% to 16% CAGR over FY25 to FY30. CareEdge also noted that residential property sales were up 74% from CY19 to 4.6 lakh units in CY24. That helps explain why investors keep coming back to housing credit.

Inside that, affordable housing finance is still one of the more active pockets. Sector estimates point to affordable housing finance companies growing assets under management by 20% to 21% in FY26 and FY27, while another sector report pegs the AHFC market at ₹14.1 trillion by FY27. That’s big enough to matter. Still fragmented enough for newer models to carve out room.

There’s a real structural shift underneath this. More lenders are willing to underwrite borrowers with informal or semi-formal income if the sourcing, documentation, and early risk filters are tighter. Government support for affordable housing and the push from PMAY 2.0 also help demand hold up in lower-income segments. That doesn’t remove credit risk — some analysts have already flagged mild stress in smaller-ticket loans — but it does explain why investors are still willing to back distribution, underwriting, and fulfilment plays in this category.

Will Nivasa Finance become more than a home loan distributor?

Nivasa Finance has a clean story: fix the part of housing credit that breaks before the loan ever gets booked. That’s a real problem, and the company already has enough founder depth and early traction to make the story credible.

What to watch next is execution, not branding. Can it expand beyond its Karnataka pilot markets without losing quality? Can it keep lenders happy while building a much larger field network? And if the NBFC plan becomes real, can Nivasa Finance make the jump from being a strong fulfilment layer to becoming a bigger secured-lending business in its own right?

Read how Dessn raised $6M led by Connect Ventures to help product teams prototype directly inside real codebases instead of relying on traditional design-to-engineering handoffs.

FAQ

– What funding did Nivasa Finance raise?  

 Nivasa Finance raised ₹25 crore in a seed round announced on May 13, 2026. Prime Venture Partners led the round, and Blume Ventures, Whiteboard Capital, and angel investors also joined in.

– How does Nivasa Finance work for a home-loan customer?  

 Nivasa Finance acts as an intermediary between borrowers and formal lenders rather than lending directly from its own balance sheet. A customer can begin through WhatsApp or the company’s portal, go through digital onboarding and assessment, and then get matched to a suitable bank, NBFC, small finance bank, or housing finance company.

– Who are the founders of Nivasa Finance?  

 Nivasa Finance was founded in 2025 by Samit Shetty and Hitesh Saraf. Shetty previously held senior roles at Navi and built Chaitanya India Micro Finance, while Saraf spent years building credit-risk systems at SmartCoin and ZestMoney.

– Is Nivasa Finance an NBFC or a housing finance company?  

 Right now, no — Nivasa Finance operates as an intermediary and direct selling agent for RBI-authorized lenders. The company is exploring an NBFC licence, which could let it play a deeper role in the secured lending stack later on.

Dessn is an AI-native product design platform that lets teams prototype directly inside an existing codebase instead of mocking things up somewhere else first. The Dessn design tool has now raised $6 million, led by Connect Ventures, as more startups look for faster ways to close the gap between design and shipped software. Founded in 2024 by Gabriella Hachem and Nim Cheema, the company is betting that the old handoff between Figma files and engineering tickets is starting to look slow — and a little outdated.

That’s the pitch.

And it’s a sharp one. Dessn isn’t trying to replace brainstorming tools or one-shot prompt apps. It wants to own the messy middle — where real product teams already have code, real constraints, and too many rounds of “can engineering make this actually work?”

What is the Dessn design tool and how does it work?

The cleanest way to explain Dessn is this: a company gives the platform read access to its repository, and Dessn spins up a cloud design environment around that codebase so designers and product managers can prototype in the real product context without opening an IDE. Every prototype runs as a live branch of the codebase. That means the team is working against actual components and system behavior instead of an approximation. Setup is basically one click, with environments targeted to be ready within 24 hours.

That matters because the product isn’t just a text box that spits out mockups. Dessn supports React web apps, and teams don’t need to clean up their stack first — it works across existing CSS choices and component libraries. Each project runs in an isolated microVM. The repo stays read-only, and Dessn never writes or pushes code back automatically. That’s a direct answer to the trust problem that kills a lot of “just connect your codebase” products.

There are also some more interesting workflow touches. Nim Cheema has described Dessn as combining visual component rendering with AI-driven code help. Earlier posts from the company point to features like visual search — navigating a codebase from a screenshot of the live app, a Figma mockup, or even a rough sketch. That gives a clearer picture of what the product is trying to be: less “AI design toy,” more shared interface between design intent and production software.

Before Dessn, a designer might make static mocks, wait for engineering bandwidth, then discover the real app behaves differently. After Dessn, the prototype lives where the product already lives. The company offers a free tier that lets teams compile 1 repository and use 5 prompts a week. Paid plans start at $39 per user per month. Higher tiers unlock more prompts, public links, and the option to opt out of AI training.

Who founded Dessn and why are they building it?

The founding story

Dessn was founded in 2024 by Gabriella Hachem and Nim Cheema, who had already worked together at 2 previous startups. Their core idea was simple and pretty provocative: if code keeps getting cheaper to generate, design becomes more valuable as the differentiator. Cheema summed up that thesis bluntly when he said they started from the belief that “code is going to get commoditized.”

That idea helps explain why Dessn is so focused on teams with existing products. This isn’t a blank-canvas ideation app like Lovable or Vercel’s v0. It’s built for companies that already have software in market and want to iterate on the real thing faster.

Why the founders fit this problem

Hachem comes from product, UX, and marketing roles across SaaS, enterprise, e-commerce, and B2B/B2C software. Before Dessn, she worked as a product manager at Planned and Automat.ai, and also held a conversational AI UX design role at Automat.ai. She studied marketing and entrepreneurship at McGill.

Cheema brings the more technical half. He started as a software engineer, spent time in data visualization, and moved toward product engineering with a user-experience focus. Before founding Dessn, he was involved in AI work and later held the Head of AI title at Planned. The pairing is coherent: one founder from product and UX, the other from engineering and AI, both circling the same handoff problem from different sides.

Early traction, pricing, and team size

Dessn is already live with real customers. Named users include teams at Color, Wispr, and Mercury. On Dessn’s own customer example for Color, 104 team members have used the platform over 10 months, creating 421 prototypes, with the largest prototype involving 570 prompts. In separate funding materials, Dessn said some users spend more than 5 hours a day in the product. The company itself is still tiny, with 4 people, and plans to add only a few more.

The $6M round and what investors are buying

Connect Ventures led the startup’s new $6 million round, with participation from Betaworks and N49P. Other reporting says the amount combines a seed round with a small, previously unannounced pre-seed. The money is expected to go toward hires and growth rather than a giant headcount sprint.

Investors aren’t just buying revenue hopes here. They’re buying a different product theory. Betaworks partner Jordan Crook argued Dessn is the kind of tool Figma would build if it were starting from scratch today — one centered on production fidelity instead of translating designs into code after the fact. It gets at why this round stands out. Dessn is trying to shift the design surface itself.

How the competition looks from here

The obvious comparison set is the current wave of AI design tools — Visual Electric, Weavy, Flora, and Krea. But those products mostly focus on concept generation, media creation, or AI-assisted canvas workflows. Visual Electric was acquired by Perplexity in October 2025 after raising $2.5 million. Weavy was acquired by Figma and turned into Figma Weave, which now emphasizes browser-based image, video, and motion workflows. Flora raised $42 million in January 2026 to expand its AI-native creative workflow product. Krea has grown into a heavily funded image-and-video creation company with $83 million in backing.

Dessn’s angle is narrower and, frankly, more opinionated. It doesn’t want to be the place where you dream up anything from nothing. It wants to be the place where a team with an existing app changes real flows and real components. Real interfaces too. Hachem has also made a point of saying Dessn doesn’t create switching costs — teams can use it alongside Figma — but it deliberately doesn’t want a Figma integration because that would pull work away from production instead of into it.

Why this Dessn design tool funding round matters

This round matters because Dessn has already chosen a hard path.

A lot of AI product tools stay vague on infrastructure. Dessn didn’t. It built around the ugly part first — running different customer codebases in the cloud without needing a developer to do setup. If that works reliably, the company can become more than another prompt interface. It becomes workflow plumbing for product teams.

The money should also help Dessn push into the next layer of collaboration. Right now the product has no integrations. The roadmap points toward tools like Slack and meeting notetakers such as Granola, where a discussion could turn straight into a prototype. That sounds ambitious, and maybe a little dangerous — lots of AI tools get bloated fast — but it fits the founders’ worldview that product decisions should turn into working artifacts much faster than they do now.

There’s also a more philosophical piece here. Hachem said she and Cheema are “token maximalists,” meaning they’d rather spend more compute to reach the right result than preserve a static UI just to look familiar. So if you’re expecting a classic toolbar-heavy design app, Dessn probably won’t go there. This funding gives the company room to keep that view intact instead of sanding it down too early.

What market shift is creating demand for design-in-production tools?

The market backdrop is favorable for a product like this. Grand View Research estimates the global CAD software segment was worth about $11.2 billion in 2024 and projects it to reach roughly $33.0 billion by 2030, with a 20.5% CAGR. That’s not a direct proxy for AI product design tools, but it does show how quickly software for design, modeling, and digital product work is expanding.

The workflow trend lines are moving in Dessn’s direction. An ASME report on CAD in 2030 said 80% of end users in the U.S. prefer cloud-hosted and SaaS applications for communication and organization. Separate 2026 research on generative AI in software engineering found 79% of developers use GenAI daily, and more than 70% reported at least halving time on tasks like boilerplate and documentation. When teams are already comfortable with cloud tools and AI-assisted software work, the leap from “design file first” to “production context first” starts to look a lot less weird.

That doesn’t mean Dessn automatically wins.

It does mean the company is launching into a moment when code is becoming easier to generate, browser-based creative tools are normal, and more product teams are willing to let non-engineers get closer to the software itself.

The takeaway on the Dessn design tool

The interesting thing about Dessn isn’t just the $6 million. It’s that the company is making a pretty direct attack on the old idea that design has to happen somewhere separate from the product.

If the Dessn design tool can keep setup light, expand beyond early customers, and add collaboration layers without losing its production-first discipline, it could end up mattering a lot more than most AI design startups. What to watch next is simple: integrations, broader technical support, and whether teams decide they really want design to happen inside the codebase instead of next to it.

Read how Exaforce raised a $125M Series B to build an AI-agent-powered SOC platform that helps security teams automate threat detection, triage, investigation, and response.

FAQ

– What funding did Dessn raise? 

 Dessn raised $6 million in funding in May 2026. Connect Ventures led the round, and Betaworks plus N49P also participated; separate reporting says the total included a seed round and a smaller pre-seed that hadn’t been announced before.

– How does the Dessn design tool work? 

 Dessn connects to a company’s repo with read access and creates a cloud environment where teams can prototype directly against the real codebase. It’s built for React web apps today, uses isolated microVMs, and keeps the repo read-only so teams can explore changes without automatically writing back to production code.

– Who are the founders of Dessn? 

 Dessn was founded in 2024 by Gabriella Hachem and Nim Cheema. Hachem worked in product and UX roles at Planned and Automat.ai, while Cheema came up through software engineering, data visualization, product engineering, and AI work before helping start the company.

– Is Dessn a Figma competitor or an AI developer tool? 

 It’s a bit of both, but not in the usual way. Dessn sits closer to an AI product-design workflow tool for teams with existing software, while tools like Figma Weave, Flora, Visual Electric, and Krea are more centered on generative creation, editing, or ideation rather than working directly inside a production codebase.

Exaforce builds software that uses AI agents to automate security operations work inside the modern SOC. The Exaforce SOC platform just pulled in a $125 million Series B at a $725 million valuation, a big jump that shows investors think security teams will spend serious money on tools that cut through alert noise fast. The problem is simple: false positives and repetitive triage bury analysts, which is why Umesh Padval of Seligman Ventures compared the job to hunting for a needle in a haystack. Founded in 2023, Exaforce is led by CEO Ankur Singla and co-founders Ariful Huq, Jakub Pavlik, and Devesh Mittal.

The timing isn’t subtle. A year after landing a $75 million Series A, Exaforce is back with a much larger round, bringing total funding to $200 million. Singla’s pitch is blunt: use AI to catch and stop threats as they happen. “It’s a very simple mandate, but it’s very complex to execute,” he told TechCrunch.

What is Exaforce SOC platform and how does it work?

The Exaforce SOC platform is an agentic security operations system that pulls together security data, analyzes it, and moves teams from alert to decision much faster than the usual SIEM-plus-manual-investigation workflow. Exaforce splits that work across four task-specific agents: Exabot Detect, Exabot Triage, Exabot Investigate, and Exabot Respond. Those bots run on a unified, real-time view of the environment. Customers can use the software directly or buy it as a managed detection and response service.

Under the hood, Exaforce isn’t pitching a single-LLM chatbot for analysts. It uses what it calls a multi-model AI engine that combines semantic models with behavioral and statistical models. It also uses LLM-based reasoning. The point is to work with the messy stuff security teams already deal with — logs, cloud telemetry, identity data, third-party alerts, source code, files, folders, and AI tool usage data — without asking humans to stitch the whole story together by hand.

That changes the workflow in practical ways. The platform can automatically triage alerts and enrich them with context. It also surfaces attack paths and helps threat hunters run hypothesis-driven investigations in plain English. Exaforce’s newer “vibe hunting” feature builds on that idea: analysts can ask whether new attacks appear to be coming from Iran and start investigations from a hunch instead of relying on rigid query language.

It also reaches into response. Exaforce says the system can handle chores like resetting MFA and killing user sessions. It can also disable devices, confirm actions with users or managers, and draw on prior ticket history during new investigations. The company puts the reduction in manual, time-consuming work at as much as 90%. That’s an aggressive number.

Who founded Exaforce and why are they credible?

How Exaforce got started

Exaforce was founded in 2023 by Ankur Singla, Ariful Huq, Jakub Pavlik, and Devesh Mittal. The company’s pitch from day 1 was that security teams needed more than another AI assistant bolted onto legacy tools — they needed one platform that could cover detection, triage, investigation, and response. By April 2025, Exaforce was already framing that vision as a 10x productivity push for SOC teams, and by Q4 2025 it had formally brought the product to market after 2 years working with design partners.

Why the founders fit this category

Singla isn’t new to security infrastructure. He previously held roles at F5, Juniper, and Cisco, and the founding bench has operated at companies including Google, F5, and Palo Alto Networks. Pavlik’s background is especially relevant for the operational side: before Exaforce, he led SRE and security operations work tied to F5XC and Volterra. Earlier, he helped build private cloud company tcp cloud.

That matters because Exaforce isn’t selling a generic AI layer. It’s trying to automate the ugliest, highest-context parts of security operations. That’s hard. Huq’s background spans product, engineering, and technical operations, which helps explain why Exaforce talks as much about workflow and usability as it does about models.

Past ventures, traction, and fundraising

There’s some repeat-founder signal here too. Singla and Pavlik previously worked together at Volterra, which F5 acquired, and Pavlik was also involved in tcp cloud, which sold to Mirantis. That doesn’t guarantee a win this time. But it does mean investors aren’t betting on first-time founders learning enterprise security sales, infrastructure, and platform design from scratch.

On traction, Exaforce is still early but no longer in science-project mode. It launched commercially in Q4 2025 after testing with enterprise design partners, and it has added 20 customers so far, including Replit and Guardant Health. Singla told TechCrunch he expects that figure to reach 40 to 50 customers by the end of 2026.

The new money is the headline: $125 million in Series B funding at a $725 million valuation, with participation from HarbourVest, Peak XV, Mayfield, Khosla Ventures, and Seligman Ventures. Exaforce raised $75 million in Series A in April 2025, so total funding now sits at $200 million. A round of this size is meant to fund heavy product development and the expensive enterprise go-to-market motion required to sell an end-to-end AI SOC platform.

Competition is getting crowded fast. TechCrunch named 7AI, Dropzone AI, and Prophet Security as direct startup rivals, while Palo Alto Networks and CrowdStrike loom as giant incumbents. Exaforce is trying to sell breadth: not just AI triage, but a full workflow spanning detection through response. It also pitches a data layer that correlates cloud, SaaS, identity, endpoint, and email signals. Dropzone AI raised a $37 million Series B in July 2025 and says it serves more than 100 enterprises; Prophet Security raised a $30 million Series A that same month; and 7AI raised $130 million in December 2025 at a $700 million valuation. Investors are piling into AI SOC tooling. Exaforce won’t have much room for execution mistakes.

Why does Exaforce funding round matter?

A round this size matters because Exaforce isn’t building a lightweight add-on. It’s trying to replace a messy stack of alerting and investigation workflows. It also wants to cover hunting and response with one AI-native system. That takes a lot of engineering, a lot of integrations, and a lot of customer hand-holding. The $125 million raise looks less like vanity financing and more like acknowledgment that enterprise security buyers want proof, precision, and support before they trust automation in high-stakes environments.

For customers, the signal is clear too. Singla said recent attacks have “supercharged” Exaforce’s path into accounts, and that buyers are no longer asking why they need automation so much as how to operationalize it. That’s a shift from curiosity to deployment.

But money alone doesn’t settle the question. Buyers will still care about whether the platform is accurate, explainable, and safe when it starts taking response actions. Exaforce’s human-in-the-loop language and multi-model architecture are meant to answer that concern. Now it has to prove those design choices hold up outside early adopters.

How big is the AI SOC market getting?

The macro setup is favorable. Gartner said enterprise spending on cybersecurity software and network security would grow 14% in 2025 to $118.5 billion. That doesn’t map one-to-one to the AI SOC category, but it shows how much budget is already flowing into tools that help enterprises defend increasingly complex environments.

The labor problem hasn’t gone away. ISC2’s 2024 Cybersecurity Workforce Study was based on 15,852 respondents, and its findings showed a profession under strain: 58% said staffing shortages pose significant risk to their organizations, while 64% said skills gaps can hurt security even more than pure headcount shortages. That’s why startups like Exaforce, Dropzone, Prophet, and 7AI keep getting funded. The pitch isn’t just “AI is cool.” It’s that the old model of throwing more analysts at more alerts has stopped penciling out.

Final take on Exaforce SOC platform

Exaforce has gone from stealthy infrastructure bet to one of the better-funded companies in agentic security operations in just 3 years. The Exaforce SOC platform is ambitious — maybe uncomfortably ambitious — because it’s trying to automate the full loop, not just the easy first step. That’s why investors are interested.

What to watch next is simple: customer growth. Exaforce says it has 20 customers today and wants 40 to 50 by the end of 2026. If it hits that while keeping response quality high, this won’t look like just another big AI security round. It’ll look like early proof that the SOC is being rebuilt around agents.

Read how Dil Foods raised ₹72 Cr in Series B funding led by Bikaji Foods Family Office to turn underused local kitchens into scalable delivery-first food brands across India.

FAQ

– What is the latest Exaforce funding round? 

 Exaforce raised a $125 million Series B on May 12, 2026, at a $725 million valuation. The round included HarbourVest, Peak XV, Mayfield, Khosla Ventures, and Seligman Ventures, and it came roughly a year after the company’s $75 million Series A, bringing total funding to $200 million.

– How does Exaforce SOC platform work? 

 It works by combining a unified security data layer with 4 AI agents — Detect, Triage, Investigate, and Respond — that handle different parts of the SOC workflow. Exaforce also uses a multi-model architecture, not just a single LLM, so it can reason across logs, identity data, cloud telemetry, third-party alerts, and response actions with more context than a chatbot-style copilot.

– Who founded Exaforce? 

 Exaforce was founded in 2023 by Ankur Singla, Ariful Huq, Jakub Pavlik, and Devesh Mittal. Singla brings experience from F5, Juniper, and Cisco, while Pavlik previously worked on Volterra and tcp cloud, giving the company a founding team with real infrastructure, cloud, and security operations depth.

– Is Exaforce part of AI cybersecurity or broader enterprise software? 

 It’s both, but the cleaner label is AI cybersecurity — more specifically, agentic security operations software. Exaforce sits in the emerging AI SOC category, where vendors try to automate alert triage, investigation, threat hunting, and response for security teams that can’t keep scaling headcount at the same pace as alert volume and cybersecurity spending.

Dil Foods is a virtual restaurant operator that turns spare kitchen capacity at local restaurants into delivery-first food brands sold on Swiggy and Zomato. It’s chasing a simple problem: lots of neighborhood kitchens have unused capacity, but building a dependable online food brand from scratch is expensive, operationally messy, and brutally margin-sensitive. The Dil Foods funding round brings in ₹72 Cr in fresh Series B capital led by Bikaji Foods Family Office, with V3 Ventures, MJV Ventures, and Alteria Capital also joining. Founded in 2022 by Arpita Aditi, the Bengaluru startup wants to use that money to widen its reach in Tier I and II cities. It also plans to add more regional cuisines and tighten back-end production and supply chains.

What is Dil Foods and how does it work?

Dil Foods doesn’t build a big network of its own kitchens and then hope demand shows up. It creates the brand, menu, standard recipes, ingredients, packaging, and operating playbook. Then it plugs that system into existing restaurants that already have kitchen infrastructure and spare capacity. Orders still arrive through food delivery apps, but local partner kitchens fulfill them.

For a restaurant partner, the setup is pretty step-by-step. First, the outlet signs up for one or more Dil brands. Then Dil’s chefs and hygiene controllers train the kitchen staff on recipes and SOPs. The company also sends branded packaging so the outlet can start operating under the new label quickly.

Once that’s done, the restaurant logs into Dil OS and starts receiving orders there. Dil makes payments weekly. Restaurants are compensated on a per-dish basis, and the company covers aggregator commissions, discounts, and advertising spend tied to order acquisition. That’s a big deal. Those costs are exactly where small delivery-first operators usually get squeezed.

The food is also designed for speed, not kitchen drama. Aditi said much of what Dil supplies can be regenerated in 2 to 3 minutes, which keeps gas and LPG costs low and makes quick delivery easier. That’s why the model fits comfort food and regional daily-meal formats so well. It’s less about theatrical cooking and more about repeatable execution.

Who founded Dil Foods and how has it grown?

Arpita Aditi’s route into food operations

Dil Foods was founded in 2022 by Arpita Aditi, who serves as founder and CEO. Her background isn’t the standard chef-founder story. Before Dil Foods, she had already co-founded Nutnbolt Business Solutions, a venture focused on helping small restaurateurs. She’d also worked across partnerships, sales, and process-heavy roles at Swiggy, Little Internet Private Limited, Reliance General Insurance, The Himalaya Drug Company, and Biocon. She studied biotechnology at Manipal Institute of Technology.

That mix matters more than it sounds. Aditi’s credibility in this category comes less from culinary celebrity and more from seeing restaurant pain from the inside. Time spent in national partnerships at Swiggy and earlier work with smaller food businesses likely shaped Dil’s central bet: independent restaurants don’t always need more real estate. They need better utilization, tighter systems, and brands that can travel well across delivery apps.

Brands, footprint, and early traction

The company now runs 9 brands: Khichdi Bar, Bihari Bowl, House of Andhra, Junglee Kitchen, Aahar, Dil Punjabi Daily, Bhole ke Chole, The Chaat Cult, and Vegerama.

And it’s moved past the idea stage.

Dil Foods has onboarded more than 300 restaurant partners across 6 cities—Hyderabad, Bengaluru, Chennai, Pune, Mumbai, and Ahmedabad. It currently operates in 340 pincodes and wants to take that to 600 by FY28. That’s real distribution breadth for a company that’s only been around since 2022. The harder question is whether brand consistency can keep up.

The fundraising details

The latest round brings in ₹72 Cr, or about $7.7 Mn, in Series B funding. Bikaji Foods Family Office led the round, while V3 Ventures, MJV Ventures, and Alteria Capital also participated.

This isn’t Dil Foods’ first outside capital. It last raised $2 Mn in a pre-Series A round in 2023, and with the new raise the startup has brought in more than ₹113 Cr in total so far.

The capital has a practical destination. Dil Foods plans to use it to expand into more Tier I and II cities. It also wants to launch more regional cuisines and strengthen its production and supply-chain backbone. That last piece is the least glamorous part of the story. It’s probably the most important.

How Dil Foods compares with Rebel Foods, Curefoods, and EatClub

Dil Foods isn’t entering a blank category. Rebel Foods built the large internet-restaurant play in India with brands such as Faasos, Behrouz Biryani, and Oven Story, alongside its EatSure ordering layer. Curefoods has gone broader, combining cloud kitchens, kiosks, and restaurants; as of March 31, 2025, it listed 281 cloud kitchens, 99 kiosks, and 122 restaurants. EatClub, meanwhile, positions itself as a full-stack cloud kitchen company with a large multi-brand network.

Dil Foods is taking a lighter, more distributed route. Instead of owning a giant kitchen footprint, it uses local restaurants with spare capacity. It standardizes recipes and inputs, supplies branded packaging, and handles the expensive app-side customer acquisition costs. That gives it a lower-capex path into more neighborhoods and smaller cities. But it also creates a brutal execution challenge: when you don’t control the kitchen walls, your operating system has to do the heavy lifting.

Why does Dil Foods funding matter now?

This round matters because Dil’s model can scale fast on paper but break fast in reality.

An asset-light food business sounds great until you remember what food customers actually care about. They care whether the order tastes the same every time. They care whether delivery survives the trip. They care whether one outlet in Pune feels like the same brand as another in Bengaluru. So when Dil says this money is going into back-end production and supply chain, it’s saying it knows where the weak spot is.

Bikaji Foods Family Office leading the round also stands out. This isn’t just another financial investor writing a check into a trendy category. A strategic food-linked backer tends to care about sourcing discipline, product consistency, shelf-life logic, and distribution economics. Those are the unsexy things that decide whether a virtual restaurant brand becomes durable or just noisy.

For restaurant partners, the upside is obvious. If Dil gets this right, a kitchen with underused staff and equipment can switch on new brands without spending on a fresh storefront. It also avoids a separate branding exercise and a delivery-app marketing war. That’s a compelling pitch in a market where plenty of local operators are busy but not necessarily profitable.

How big is India’s cloud kitchen market?

The demand backdrop is large enough to explain why investors are still interested. The source article pegs the Indian food delivery market as a $59 Bn opportunity by 2030, driven by rising disposable incomes, deeper internet access, and the simple fact that convenience keeps winning.

The cloud-kitchen slice of that market is also getting bigger. IMARC estimates India’s cloud kitchen market was worth $1.24 Bn in 2025 and could reach $3.69 Bn by 2034, with a 12.28% CAGR over 2026 to 2034. South India alone held more than 35% share in 2025. That’s relevant for a Bengaluru startup that already has strong presence in southern cities.

And the delivery rails are thickening. The same IMARC report notes that Swiggy’s average monthly transacting users rose 19% to 17.1 Mn in Q2 FY2025. Put that together with urbanization, busy work schedules, and better smartphone-driven ordering habits, and cloud kitchens start to look less like a temporary format and more like permanent food infrastructure.

What should you watch after Dil Foods funding?

The interesting thing about Dil Foods funding isn’t just that another food startup raised money. Dil is betting neighborhood restaurants can become a scalable distribution layer for regional brands—if the software, supply chain, and training are tight enough.

That’s the next test. Watch whether Dil Foods can get from 340 to 600 pincodes by FY28 without losing consistency, and whether new cuisine launches feel like thoughtful brand additions rather than just more menu clutter. In delivery-first food, expansion is the easy headline. Repeatable taste is the real milestone.

Read how HrdWyr raised a $13M Series A led by Ideaspring Capital to build AI-native semiconductor chips for edge devices, power systems, and intelligent IoT workloads.

FAQs about Dil Foods funding

– What is the latest Dil Foods funding round? 

 Dil Foods has raised ₹72 Cr in a Series B round. Bikaji Foods Family Office led the investment, and V3 Ventures, MJV Ventures, and Alteria Capital also participated. The fresh capital will go into geographic expansion, new regional cuisines, and stronger back-end operations.

– How does Dil Foods work with local restaurants? 

 Dil Foods gives partner restaurants the operating layer they usually don’t have on their own. It provides the brand, menu, recipes, ingredients, packaging, and SOPs. Then it trains staff, routes orders through Dil OS, pays partners weekly, and absorbs commissions, discounts, and ad spends tied to delivery platforms.

– Who is Arpita Aditi, the founder of Dil Foods? 

 Arpita Aditi is the founder and CEO of Dil Foods, which she started in 2022. Before that, she co-founded Nutnbolt Business Solutions and worked at Swiggy, Little Internet, Reliance General Insurance, The Himalaya Drug Company, and Biocon. That gave her a mix of food-tech exposure, partnerships experience, and process-heavy operating discipline.

– Is Dil Foods a cloud kitchen company or a food delivery company? 

 It sits closer to a virtual restaurant or cloud-kitchen enabler than a delivery app. Dil Foods doesn’t own the customer apps like Swiggy or Zomato, and it doesn’t depend on a huge owned-kitchen network either. Instead, it uses partner restaurants as fulfillment nodes inside a cloud-kitchen market that IMARC values at $1.24 Bn in 2025.

HrdWyr builds AI-native semiconductor chips for edge devices and power-heavy systems. HrdWyr funding just got a big boost with a $13 million Series A led by Ideaspring Capital, giving the Bengaluru startup room to push its chip roadmap faster. The problem it’s chasing is simple enough: generic silicon still forces a lot of products to trade off power efficiency and response time. Cost, too. Founded in 2023 by Ramamurthy Sivakumar and Guruswamy Ganesh, the company is trying to replace that compromise with purpose-built silicon and software designed around specific workloads.

What is HrdWyr and how do its chips work?

HrdWyr is a fabless semiconductor startup building AI-native system-on-chip products and the software stack around them. Its edge lineup sits under the Indus family. It combines compute and memory with precision metrology, power management, and embedded control into ultra-low-power chips for battery management and motor control. Intelligent IoT use cases are part of the pitch too.

For a customer, the workflow isn’t “buy a generic chip and bolt AI on later.” HrdWyr’s stack starts with customer data, then moves through a simulator and containerized tooling. It also includes optimized frameworks, libraries, model-weight tuning, and deployment tools. The bet is pretty clear: push intelligence closer to where data is generated, keep the software path simpler, and get the system running with C-based low-code deployment instead of a much messier embedded integration effort.

The edge products are aimed at things like TWS charging cradles and hearing-aid charging cases. Smart wearable cradles are in the mix too. So are motor controllers, smart machines, and low-power embedded controllers. The actual silicon ingredients are practical, not flashy — a 32-bit ARM M0+ microcontroller, on-chip SRAM and flash, ADC channels, temperature sensing, charging and boost features, and configurable control blocks. That mix tells you what HrdWyr is really doing: not chasing the giant training-chip race, but building tight, efficient silicon for physical devices that have to run on limited power.

Its data-center product takes a different route. The iPMM platform watches microsecond-level power irregularities, uses time-series AI models to spot fault patterns, and runs decision-making on-device rather than sending everything upstream first. That’s a very specific pitch. It matters because power delivery and failure prediction are fast becoming bottlenecks in AI infrastructure.

Who founded HrdWyr and what has it done so far?

The founding story

HrdWyr was started in Bengaluru in 2023 by Ramamurthy Sivakumar and Guruswamy Ganesh. The company is a full-stack fabless chip startup, and that framing matters: it isn’t positioning itself as an IP licensor or a services-led design shop. It wants to ship semiconductor products built around vertical use cases, with AI integrated into the architecture from the start.

Why the founders fit this category

Sivakumar brings heavyweight semiconductor credibility. He joined Intel in 1989 and later ran Intel’s South Asia business; he also held a managing director role at Intel Capital focused on ultrabooks and perceptual computing. That’s not startup-theater résumé padding. HrdWyr’s CEO has spent decades inside the kind of global chip company and investment machine most young semiconductor founders only know from the outside.

Ganesh, HrdWyr’s co-founder and COO, comes from the systems and execution side. He held prior leadership roles at NexGen Power Systems, Western Digital, SanDisk, and Motorola, after an earlier engineering role at AMD. That background fits the company’s product choices almost perfectly — battery, power, storage-adjacent reliability, and embedded control all sit close to the problems HrdWyr is trying to solve.

Early signals from the market

HrdWyr is still early, but it isn’t invisible. LinkedIn lists the company at 11-50 employees. That’s about what you’d expect for a startup trying to get serious semiconductor work done without pretending it’s already a scaled business. The stronger signal came in August 2025, when boAt partnered with HrdWyr on the Indus 1011 chip, a locally designed chipset expected to first appear in premium TWS charging cases.

That boAt tie-up matters more than the headline might suggest. Consumer electronics brands don’t usually take chances on unproven chip startups unless there’s a real reason to do it — cost, localization, feature control, or all 3. In this case, boAt contributed market requirements while HrdWyr developed low-power and reliability-focused chip features. That’s exactly the sort of co-development motion a young semiconductor company needs if it wants design wins instead of just demos.

The round and the competitive set

The new round totals $13 million, or about ₹124 crore, and Ideaspring Capital led it with participation from Singularity AMC, Avatar Growth Capital, and existing investor Persistent Systems. HrdWyr will use the money to accelerate development of its AISoC products and deepen customer engagements in global markets. Persistent’s presence as an existing backer also suggests the company had already cleared at least one earlier trust test before this Series A.

Competition is awkward but interesting. Netrasemi is also building edge-AI SoCs for smart IoT products. Mindgrove is pushing secure RISC-V microcontroller SoCs into industrial IoT, wearables, and EV battery-management adjacencies. C2i Semiconductors is working on power-management silicon for AI data centers and cloud infrastructure. HrdWyr’s differentiator is that it’s trying to bridge those worlds with domain-specific AI-native products for both intelligent edge and power infrastructure, instead of chasing a generic compute story. Its bigger enemy, honestly, is still the old way of doing things: imported general-purpose chips, stitched-together controller designs, and lots of custom engineering on top.

Why this HrdWyr funding round matters

Semiconductor startups don’t raise Series A rounds just to “scale operations.” They raise because silicon is expensive, product cycles are long, and customers need proof long before meaningful revenue shows up.

That’s why this HrdWyr funding round matters. The company isn’t building a single feature chip. It’s trying to create a product family at the edge and a data-center power platform. The software tooling that makes both usable is part of the plan too. That requires money, yes. It also signals that investors think the company has moved past the pure-concept stage and into something customers may actually deploy.

There’s also a sharper investor thesis underneath it. Ideaspring has backed a company that fits two narratives at once: India-designed semiconductor products for global customers, and AI infrastructure that isn’t limited to giant model-training clusters. HrdWyr is betting that “physical AI” will need efficient silicon in appliances, EV systems, wearables, smart machines, and power modules — not just more GPUs in cloud racks. If that thesis holds, the company’s addressable market gets a lot wider than a typical niche chip startup.

Why India’s semiconductor market is heating up now

The macro backdrop is doing HrdWyr a favor. India’s semiconductor market is projected at roughly $62 billion in 2026 and could climb to about $155 billion around the turn of the next decade, as domestic demand, supply-chain diversification, and state support all pull in the same direction. That’s a big enough number to attract founders, capital, and a lot of ambition. Even if plenty of those bets still won’t work out.

Policy is part of the story too. India’s semiconductor push has been backed by a ₹76,000 crore outlay under the India Semiconductor Mission, and recent cabinet approvals added 2 more Gujarat projects worth about ₹3,936 crore, taking the running total to 12 approved projects with roughly ₹1.64 lakh crore in planned investment. That doesn’t solve design execution, packaging bottlenecks, or demand risk overnight. But it does reduce the old excuse that India has no serious institutional commitment to chips.

Money is spreading across adjacent layers as well. Turiyam.ai raised $4 million this year for inference-focused AI hardware infrastructure, which tells you investors aren’t only chasing software anymore. They’re backing the compute, power, and systems plumbing around AI too. That’s the lane HrdWyr wants to occupy.

What to watch after HrdWyr funding

The HrdWyr funding round is a serious vote of confidence, but confidence isn’t the same thing as execution. The next thing to watch is whether the company can turn its Indus edge products and data-center power stack into repeat design wins, not just strong demos and patriotic headlines. If it can, HrdWyr could become one of the more credible examples of India building semiconductor products for global markets rather than staying stuck as a talent pool for everyone else.

Read how Mekr raised ₹67 Cr in Series A funding led by Avaana Capital to help brands design, source, and manufacture consumer electronics through one integrated India-based production platform.

FAQ

– What is the HrdWyr funding round about? 

 HrdWyr raised $13 million in a Series A round in May 2026 to speed up development of its AI-native system-on-chip products and expand customer work in global markets. Ideaspring Capital led the round, and Singularity AMC, Avatar Growth Capital, and Persistent Systems also participated.

– How do HrdWyr’s AI-native chips actually work? 

 HrdWyr builds chips that combine embedded compute and power management with sensing and AI-oriented software tooling for specific use cases rather than general-purpose computing. Its edge products handle things like battery and motor management. Its iPMM platform for data centers uses precision metrology and time-series AI to predict failures from real-time power behavior.

– Who founded HrdWyr? 

 HrdWyr was founded in 2023 by Ramamurthy Sivakumar and Guruswamy Ganesh. Sivakumar is a longtime Intel executive who later led Intel’s South Asia business, while Ganesh has held senior roles across NexGen Power Systems, Western Digital, SanDisk, Motorola, and AMD.

– Why is HrdWyr part of India’s semiconductor push? 

 Because it sits right at the intersection of 2 national priorities: more domestic chip design and more AI infrastructure built around local needs. India’s semiconductor market is projected at about $62 billion in 2026 and roughly $155 billion by the start of the next decade, while the government-backed mission has already earmarked ₹76,000 crore and supported 12 approved projects.

Mekr is an electronics manufacturing platform that helps brands design and source consumer electronic products, then build them through one managed production stack. A lot of Indian brands still rely on imported finished goods or a messy patchwork of vendors, which makes timelines, pricing, and quality harder to control. Now the Delhi NCR startup has raised ₹67 Cr in Series A funding led by Avaana Capital, with Titan Capital Winners Fund also participating. Mekr was founded in 2022 by Anand Yadav and Gaurang Kuchhal, and it’s betting that more brands want India-based manufacturing instead of just importing box-packed appliances.

What is Mekr and how does it work?

At a basic level, Mekr works like an outsourced electronics manufacturing team for brands that don’t want to stitch together sourcing, components, assembly, and supply-chain execution on their own. A customer can come in with product requirements — or even just a physical reference sample. Mekr’s in-house team then maps materials and manufacturing processes. It also creates digital designs before lining up production. It’s built around electronics, but the actual work spans PCBA and plastics molding. It also covers metal processing, parts, and semiconductor sourcing.

What makes that more than a broker is the operating layer around it. Mekr gives customers a personalized dashboard to track project status and reports. Dedicated project managers handle inspections and audits. They also coordinate production. On the supplier side, it offers timely payments and working capital access. That matters.

The product stack isn’t limited to assembly. Mekr builds around modular engineering blocks such as BLDC motor systems, precision gear mechanisms, heating systems, load cells, and in-house mould design. Its catalog also shows it already manufactures across categories like electric kettles, tyre inflators, weighing scales, vacuum cleaners, trimmers, and hair dryers. So the platform is a mix of ODM capability and component sourcing. Production management is part of it too.

There’s a practical filter here too. Mekr typically prefers mass-volume orders of more than 500 to 1,000 units, which tells you it isn’t chasing tiny prototype jobs. It can support early-stage product development. For more complex electronics design or firmware work, it can connect brands with external R&D partners. But the sweet spot is moving products into manufacturing with quality checks such as IQC, PDI, FPA, and online inspection.

Who founded Mekr before this funding round?

Company founding story

Mekr was started in 2022 by Anand Yadav and Gaurang Kuchhal. The company’s registered roots are in Delhi, and its manufacturing base sits in Sonipat, Haryana. The founding idea was direct: both founders had already gone through the pain of taking hardware from prototype to mass production, and they saw how even simple electronics could get stuck across separate suppliers for plastics, PCBs, metals, imports, and assembly.

Founder market fit

That background is why Mekr doesn’t read like a software-first startup that wandered into manufacturing. Earlier coverage described Yadav and Kuchhal as second-time founders who had firsthand experience with the jump from proof-of-concept hardware to real production runs. Public profiles show a technical bent: Yadav studied at IIT Delhi, while Kuchhal studied at Delhi Technological University and had worked on a home automation project called Iota Homes.

Early traction and fundraising details

Mekr isn’t operating in stealth mode anymore. It has developed more than 100 SKUs and works with over 40 brands, including Amazon Basics, Croma, Flipkart, and Wipro. Before this Series A, it had raised ₹5.8 Cr in 2022 from Better Capital, Titan Capital, and 2AM VC.

The new round is much bigger — ₹67 Cr, or about $7 Mn. Avaana Capital led it, and Titan Capital Winners Fund joined in. Mekr will use the money for R&D and product engineering. It also plans to spend on proprietary tooling, supplier localisation, manufacturing automation, stronger quality systems, and export readiness. Very hardware-specific. No fluff.

Competition and market position

Mekr is entering a market where the biggest names are much larger contract manufacturers and ODM players. Prospectus and market materials for listed consumer durables manufacturers place companies such as Dixon Technologies, Amber Enterprises, PG Electroplast, Elin Electronics, and EPACK Durable among the established peers in outsourced manufacturing. Those firms operate at a different scale. Many brands also still fall back on Chinese imports or fragmented local vendor chains instead of using one coordinated manufacturing partner.

Mekr’s pitch is straightforward. It isn’t trying to out-muscle the giant EMS players on sheer volume. It’s going after brands that want private-label or ODM manufacturing for small appliances and related electronics with more visibility and lower planning friction. India-based lead times are part of the appeal. Earlier founder comments made the positioning clear: match China pricing where possible, but do it with domestic manufacturing and faster supply-chain control.

Why does the Mekr funding round matter?

Hardware rounds matter differently from SaaS rounds. When a company says it will spend on tooling, automation, and supplier localisation, it’s saying it wants tighter control over unit economics, defect rates, and delivery timelines. For Mekr, that could be the difference between being a useful manufacturing partner and becoming a repeat supplier that brands build product roadmaps around.

The export angle matters too. Yadav has argued that India is becoming a serious base not just for local demand but for exports, especially in labor-intensive appliance manufacturing. If Mekr uses this round to make its factory processes more automated and its quality systems more reliable, it could move from import substitution to export-grade manufacturing. That’s a harder place to earn.

There’s also a signal in who wrote the check. Titan Capital was already in from the earlier round, and it came back. That usually means the company hit enough milestones after seed to justify another bet. In manufacturing, follow-on support says a lot.

Why are investors backing electronics manufacturing in India now?

India’s electronics opportunity is no longer a niche policy story. The source report pegs the broader electronics market at more than $400 Bn by 2030. A government investment platform also noted that domestic electronic hardware production rose from $37 Bn in 2015-16 to $74.7 Bn in 2020-21, a 17.9% CAGR, while national targets call for $300 Bn in electronics manufacturing and $120 Bn in exports by 2026.

Policy is pushing in the same direction. Earlier in 2026, MeitY approved 29 more applications under the Electronics Component Manufacturing Scheme across 16 product categories, including display modules, capacitors, connectors, resistors, flexible PCBs, and lithium-ion cells. Those proposals are expected to bring in ₹7,104 Cr of investment. That kind of component-level push matters because companies like Mekr can’t localize end products unless the underlying supply chain gets deeper too.

Mekr isn’t the only company attracting capital here. In March 2026, Bacancy Systems raised ₹40 Cr in a Series A round led by Sabre Partners and Greenstone Capital. So this isn’t one isolated bet. Investors are putting money into Indian electronics manufacturing because import dependence is still high, supply chains are shifting, and local brands increasingly want India-based partners who can do more than final assembly.

What should you watch after Mekr funding?

The next test for Mekr funding isn’t the headline number. It’s whether the company can turn this round into deeper supplier localisation and sharper quality control. It also needs more export-ready manufacturing without losing the speed that made brands sign up in the first place. If that happens, Mekr could become one of the more credible new ODM names in India’s consumer electronics and appliance market.

Read how Cowboy Space raised a $275M round to build orbital data centers and eventually launch them on its own rockets as AI compute demand collides with power, land, and infrastructure limits on Earth.

FAQ

– What is the latest Mekr funding round?

 Mekr has raised ₹67 Cr in a Series A round announced on May 12, 2026. Avaana Capital led the round, and Titan Capital Winners Fund also participated; the company plans to use the money for R&D, tooling, automation, supplier localisation, and export readiness.

– How does Mekr’s manufacturing platform work? 

 Mekr acts as an end-to-end electronics manufacturing partner for brands. It can start from a requirement sheet or even a physical sample. It then converts that into digital designs and production plans, manages sourcing and assembly, and gives customers project visibility through a dashboard while its team runs inspections and quality checks.

– Who founded Mekr and what is their background? 

 Mekr was founded in 2022 by Anand Yadav and Gaurang Kuchhal. They were described as second-time founders with hands-on experience in taking hardware from prototype to mass production; public profiles also connect Yadav to IIT Delhi and Kuchhal to Delhi Technological University and an earlier home automation project called Iota Homes.

– Is Mekr an electronics manufacturing company or a consumer appliance ODM? 

 It’s basically both. Mekr operates as an electronics manufacturing platform, but its visible product footprint sits heavily in consumer appliances and adjacent categories like electric kettles, weighing scales, vacuum cleaners, tyre inflators, trimmers, and hair dryers, which places it squarely inside India’s broader push to localize electronics and appliance production.