Tag: startup funding india

NowPurchase is a Kolkata-based manufacturing materials marketplace that helps metal factories source scrap, alloys, and additives more efficiently. For a lot of foundries, raw-material buying is still opaque, fragmented, and painfully manual. That mess shows up on the shopfloor fast. The new NowPurchase funding round brings in ₹80 Cr, led by Bajaj Finserv, to push deeper into scrap recycling, branded products, and its AI-led SaaS platform MetalCloud. Founded in 2017 by Naman Shah and Aakash Shah, the company is trying to own both sides of the workflow: what factories buy and how they melt it.

What is NowPurchase and how does MetalCloud work?

At the front end, NowPurchase works like a specialized procurement layer for metal manufacturers. Buyers can source raw materials such as metal scrap, alloys, and additives through the platform. The experience is tied to a WhatsApp bot that handles real-time price and stock discovery. That matters because most factories in this category still don’t want another bloated enterprise system. They want quick visibility, faster quotes, and someone who can actually support the order on the ground.

MetalCloud is the software layer sitting inside production. Its core job is to help foundries decide the right charge mix based on inventory, market prices, and available supply, then turn that plan into something usable on the factory floor. The platform captures data through kiosks, IoT hooks, and software inputs. It pushes live production information to computers and WhatsApp, including heat data, sample chemistry, raw-material consumption, breakdown logs, and power use. It also generates a digital melting log sheet and dashboard views for furnace utilization, idle time, liquid metal tap, and specific power consumption.

One of MetalCloud’s more practical modules is the Suggest engine. It gives addition and dilution recommendations during melting and sends spectrometer readings to WhatsApp. It’s designed to reduce the tiny chemistry errors that quietly wreck margins in a foundry. NowPurchase says this module can reach up to 98% accuracy in those recommendations. That’s the kind of detail operators actually care about.

The product is getting broader, too. A newer Defect Bot AI can analyze casting defect images in about 30 seconds. It returns confidence-scored diagnoses, ranks likely root causes, and suggests corrective actions. Put simply, the software is moving from procurement support into a fuller operating stack for foundries: melt planning and shopfloor monitoring. Quality control, too.

Who founded NowPurchase and what has it built?

How the company started

NowPurchase didn’t begin as a narrow foundry-tech company. Naman Shah started it after seeing how little industrial buying had changed compared with consumer commerce, and the early thesis was broader B2B procurement. His cousin Aakash Shah joined as co-founder, and the two validated the idea by visiting factories across Delhi-NCR, Kolkata, and Mumbai before building the company out.

The sharper version of the business came later. By December 2019, NowPurchase had pivoted toward the metal manufacturing market after the founders decided a horizontal model wouldn’t make them important enough to customers. That move gave the company a clearer customer and a more urgent workflow. It also gave it a better shot at becoming part of the supply chain rather than just another seller.

Why Naman Shah and Aakash Shah fit this market

Naman brought startup experience from the US and Singapore, including a stint leading BizEquity’s expansion in Asia. That doesn’t make someone a metallurgist overnight, of course. But it does explain why the company has always leaned hard into software, process design, and category specialization rather than just brokering materials.

Aakash’s fit is more operational. He came in with exposure to mechanical engineering, rural marketing, and B2B consultative selling, and the family’s manufacturing business gave both founders a close-up view of how procurement headaches pile up inside factories. That mix matters. Software ambition on one side, industrial reality on the other.

What NowPurchase has executed so far

This isn’t a beta-stage story. NowPurchase has delivered more than 1.95 lakh tonnes of raw materials to over 200 clients, and it currently operates 6 warehouses and 2 scrap processing centers. After its 2024 raise, the company expanded in Maharashtra with a scrap recycling unit near Pune. It also started micro-centres in Punjab, Gujarat, and Tamil Nadu. By September 2024, Naman Shah said MetalCloud was already being used by more than 100 factories across India.

Its tech bench also got stronger when Ankan Adhikari joined as CTO in January 2021. He had previously founded Pyoopil Education and sold it to upGrad in 2016. That helps explain why NowPurchase’s software side looks more deliberate than what you usually see from industrial marketplaces.

The ₹80 Cr round and how NowPurchase stacks up

Here’s the deal. The latest NowPurchase funding round is ₹80 Cr, or about $8.5 Mn, led by Bajaj Finserv. Existing backers InfoEdge Ventures, Orios Venture Partners, and Real Ispat Group also joined, along with investors and family offices including S Four Capital partner Shikhar Raj and Lloyds Group promoter-director Madhur Gupta. The company has now secured ₹120 Cr in equity overall, and this comes after a $6 Mn mix of equity and debt in 2024 led by InfoEdge Ventures.

Competition is real, but it’s split. Metalbook is a digital metal marketplace with financing and logistics built around supply-chain transactions. ScrapEco focuses on digital scrap buying and selling. Then there are the old-school alternatives: local traders, brokers, dozens of phone calls, manual quote comparisons, and plant teams running procurement from WhatsApp threads and spreadsheets.

NowPurchase isn’t just a commerce layer, and it isn’t just software. It combines physical infrastructure and on-ground service. It also has scrap processing, branded materials, and a foundry-focused SaaS stack that reaches into melt execution and defect analysis. That hybrid model is harder to scale. It’s also probably why investors are still writing checks.

Why does NowPurchase funding matter now?

Because this round changes the company’s shape more than its headline.

The money is earmarked for 3 concrete things: strengthening scrap recycling, expanding the branded products portfolio, and scaling MetalCloud. That means NowPurchase isn’t using fresh capital just to sell more material. It’s trying to deepen control over supply quality, margin structure, and customer stickiness at the same time.

There’s also a geographic signal in the plan. Naman Shah has called Tamil Nadu “the most promising market” for the company right now, and NowPurchase plans to add another scrap processing center there while also setting up end-to-end marketplace operations. It’s also looking to open 2 more facilities in Jharkhand and Tamil Nadu in the next 3 to 6 months. If that build-out lands on schedule, the company gets closer to being a regional operating network rather than a single procurement brand.

Bajaj Finserv leading the round matters, too. Not because a finance brand automatically makes a startup better. But because it suggests institutional belief in a category that sits awkwardly between industrial commerce, recycling infrastructure, and vertical SaaS.

How big is the market NowPurchase is chasing?

Big enough to attract a lot of attention, and messy enough that specialists still have room.

One way to look at it is through the foundry side. Mordor Intelligence pegs India’s foundry market at $28.72 billion in 2026 and projects it to reach $46.72 billion by 2031. On the upstream side, India produced 54.19 MT of crude steel in FY26 between April and July 2025, and the country is expected to reach about 330 MT of steelmaking capacity by 2030. That’s a huge industrial base.

The scrap story is just as important. EY says India consumed about 34.2 million tons of ferrous scrap in 2024, while scrap utilization in crude steel production was only around 23%, below global norms. It also notes that Maharashtra, Punjab, and Tamil Nadu accounted for roughly 35% of India’s scrap consumption in scrap-based steelmaking that year. So the timing makes sense: more scrap demand, more pressure on efficiency, and plenty of room to formalize collection, processing, and plant-level decision-making.

What should you watch after this NowPurchase funding round?

What matters next is whether the company can turn this ₹80 Cr into tighter recycling capacity, a stronger branded-materials business, and a MetalCloud product that becomes part of daily plant operations instead of a nice-to-have dashboard. Watch Tamil Nadu. Watch the new centers in Jharkhand and Tamil Nadu. Watch whether NowPurchase can keep proving that a metal marketplace can also become factory software.

Read how Xovian raised $2M to advance RF satellite systems for reliable and scalable space networks.

FAQ

What is the latest NowPurchase funding round? 

NowPurchase has raised ₹80 Cr, or about $8.5 Mn, in a round led by Bajaj Finserv. Existing investors including InfoEdge Ventures, Orios Venture Partners, and Real Ispat Group also participated, along with names such as Shikhar Raj and Madhur Gupta.

How does MetalCloud work for foundries?

MetalCloud is an AI-led factory software stack for metal manufacturers. It helps teams choose charge mixes and captures production and heat data through kiosks and software inputs. It sends updates over WhatsApp and now extends into defect diagnosis and melt execution support.

Who founded NowPurchase? 

NowPurchase was founded in 2017 by Naman Shah and Aakash Shah. Naman had earlier startup experience in the US and Singapore, including BizEquity’s Asia expansion, while Aakash brought operating exposure in mechanical engineering, rural marketing, and B2B selling.

Is NowPurchase a SaaS company or a metal marketplace? 

It’s both. NowPurchase sells and processes industrial raw materials through its marketplace and physical network, while MetalCloud handles production intelligence inside the plant, which puts the company in the overlap between vertical SaaS, industrial procurement, and scrap recycling.

Xovian is a Bengaluru startup building RF satellites that turn radio signals from space into real-time intelligence. It has now raised $2 million in fresh funding led by Ashish Kacholia, with existing investor Inflection Point Ventures joining in. The pitch is simple: when ships or aircraft go dark, optical satellite imagery often can’t keep up, and that blind spot matters for defence, logistics, aviation, and maritime operations. Founded in 2019 by Ankit Bhateja and Raghav Sharma, the new capital will go into satellite development. It’ll also fund deeper AI and engineering hires, along with commercial partnerships.

What does Xovian’s RF satellite platform do?

Xovian’s product is a full-stack RF intelligence system. Its satellites are built to capture radio frequency emissions across the spectrum. Its AI layer interprets those signals. The output becomes a decision layer that mixes signal intelligence with geospatial context for customers tracking assets or monitoring activity across land, sea, and air.

That’s the core idea.

The workflow is more specific than the usual “AI plus space” line. Xovian is validating a multi-frequency RF payload first, then moving toward a nanosatellite deployment. Once in orbit, the system is designed to continuously scan Earth’s radio spectrum. It detects shifts in intent, exposure, or volatility, then pushes low-latency insights from spacecraft to cloud software. Customers don’t have to stitch together separate hardware, software, and analytics vendors on their own.

That’s the operational difference. Bhateja said older decision chains can take 4 to 4.5 hours because teams are waiting on imagery. They’re cross-checking signals and manually interpreting movement. Xovian’s pitch is that RF-first monitoring can shrink that to under 10 minutes. For a customer watching a vessel, aircraft, or sensitive corridor, that’s the whole product.

It also removes some of the clunky parts of legacy intelligence work. Instead of relying only on what a camera can see, Xovian’s architecture is built to listen for activity and classify it. It then delivers contextual alerts for sectors ranging from maritime and aviation to defence and climate monitoring. Vertical integration — hardware, payload, sensing, AI, and delivery in one stack — keeps the latency low enough to matter.

Who built this RF satellite startup and why?

The founding story

Xovian was founded in 2019 by Ankit Bhateja and Raghav Sharma, though the company’s incorporation dates back to October 25, 2018. It was built around a sharp thesis: optical satellites miss too much of the world’s live activity, so intelligence systems need to understand radio behavior in real time, not just images after the fact. That’s the gap the founders chose to chase.

Why the founders have real market fit

Bhateja didn’t arrive at this through a generic software route. Before Xovian’s current satellite push, he was already speaking publicly about an indigenously developed passive-radar approach for maritime and environmental monitoring, and he said he had support from ISRO on earlier projects. Sharma brings a different angle. He’s a chemical engineering graduate from NIT Jalandhar who, after a stint at Escorts, moved into building Xovian with a focus on satellite manufacturing and services.

Early execution and technical signals

The company’s earlier work wasn’t limited to slide decks. Sharma’s SGAC speaker profile ties Xovian to amateur rocket testing and CanSat programs. It also links the company to a PES University collaboration around satellite development and payloads for drought, glacier, and biomass monitoring. That doesn’t make the current RF satellite program de-risked. Space hardware never is. But it does show the founders have been building in this domain for years, not just since deeptech became fashionable.

Product status and traction

Right now, Xovian is still in the build-and-validate phase. That’s exactly where you’d expect an early hardtech company to be. It’s preparing its first AI-native RF satellite, planned payload validation on an ISRO launch vehicle, and early customer pilots and data trials in 2026. The company lists itself in Bengaluru with an employee band of 11-50. It’s still a compact, engineering-heavy team.

Fundraising details

This new round brings in $2 million, led by Ashish Kacholia, with Inflection Point Ventures participating again, and takes Xovian’s disclosed funding to $4.5 million. Before this, the startup raised $2.5 million in August 2025 from Piper Serica, Turbostart, IPV, and Eaglewings Ventures. The fresh capital is earmarked for satellite development. It’ll also go toward stronger engineering and AI teams, plus commercial tie-ups that can turn payload capability into paying use cases.

How Xovian compares with rivals

Xovian doesn’t sit neatly beside India’s better-known spacetech names. Pixxel is identified far more with Earth imaging and hyperspectral data. SatSure is a downstream decision-intelligence and Earth observation player. Dhruva Space is stronger on satellite platforms and mission infrastructure, while Bellatrix is about propulsion. Xovian’s wedge is narrower and more specialised: RF sensing for real-time situational awareness when optical methods are too slow, too limited, or simply blind.

Legacy competition matters too. In practice, Xovian isn’t only competing with startups. It’s up against a patchwork of optical satellite feeds and ground-based monitoring. It also has to beat manual analyst workflows and delayed intelligence handoffs. Investors backing the company are betting that an RF-first, vertically integrated stack can produce faster, more usable intelligence than those fragmented systems.

Why does Xovian’s new funding round matter?

Because this isn’t a consumer app where more money just means more marketing.

For Xovian, the new round matters because it helps bridge the hardest gap in any space startup: moving from a technical concept to hardware in orbit. That means payload development and satellite integration. It also means AI model refinement, plus the kind of engineering hiring that can’t be faked with flashy branding. If the company misses on execution, the thesis collapses fast. If it hits, it owns a much harder-to-copy layer of space intelligence.

It matters for customers too. Maritime operators, aviation users, logistics networks, and defence-linked buyers don’t need another dashboard. They need better visibility when assets are moving, disappearing, or behaving strangely. Xovian’s use of funds suggests it’s trying to get from “interesting RF tech” to “commercially usable monitoring system.” That’s a much more serious milestone.

The round also says something about investor appetite. Kacholia leading the round, with IPV participating again, signals belief in a deeptech model where defensibility comes from proprietary hardware plus intelligence software, not just one or the other. It’s a tougher build. It’s also why a company like this can still stand out in a crowded Indian startup market.

Why are RF satellites and Indian spacetech attracting capital now?

The market backdrop is doing some heavy lifting here. One widely cited estimate puts India’s space sector on a path from about $13 billion to $77 billion by 2030. A separate FICCI-EY projection, cited in 2025, pegs India’s space economy at $44 billion by 2033, up from roughly $8.4 billion in 2024, with the country targeting an 8% share of the global market. Those numbers aren’t identical. They point the same way: investors see a much bigger commercial space market forming in India than existed a few years ago.

Policy has changed the timing. India opened the sector to private participation in June 2020, followed that with Indian Space Policy 2023, and loosened foreign investment rules in February 2024. Business Standard also noted roughly 250 startups are now operating across upstream and downstream space segments. That helps explain why new categories, including RF intelligence, are finally getting funded instead of being treated like science projects.

There’s also a practical demand story here. The same FICCI-EY outlook sees Earth observation and remote sensing contributing about $8 billion by 2033, while satellite communication is projected to become the largest slice of the market at $14.8 billion. That matters because Xovian sits in the part of the stack where sensing, intelligence, defence relevance, and commercial monitoring start to overlap.

Recent deal flow backs that up. Bellatrix Aerospace raised $20 million in late March 2026 to expand satellite propulsion manufacturing, and Dhruva Space was back in the market for a much larger round in February 2026. Investor interest in Indian spacetech is real. But the bar is rising too. Startups now need clear technical moats, not just patriotic pitch decks.

Xovian’s bet is that RF satellites could become one of those moats.

If the company can get its first satellite and customer pilots working on schedule, it won’t just be another Indian spacetech fundraising story. It’ll be a test of whether RF intelligence can become a durable commercial category.

Read how Sycamore raised $65M to create an agent operating system for managing AI agents inside enterprise workflows.

FAQ

What funding did Xovian just raise?

Xovian raised $2 million in a fresh round led by Ashish Kacholia, with existing backer Inflection Point Ventures also participating. The round takes the Bengaluru startup’s disclosed funding to $4.5 million and is meant to push satellite development, AI hiring, and commercial partnerships further.

How do Xovian’s RF satellites work? 

Xovian’s RF satellites are designed to detect and interpret radio frequency signals rather than relying only on optical imagery. The company combines space-based sensing and AI-led signal analysis. It also has a cloud delivery layer so customers can get real-time monitoring and situational awareness from RF activity across land, sea, and air.

Who are the founders of Xovian?

Xovian was founded by Ankit Bhateja and Raghav Sharma in 2019. Bhateja had already been working on passive-radar ideas tied to maritime and environmental use cases, while Sharma came from an engineering background at NIT Jalandhar and earlier industry experience before co-building the company.

Is Xovian a spacetech company or a defence-tech company? 

It’s best described as a spacetech company with strong defence-tech and intelligence applications. Its product sits at the intersection of satellite infrastructure, signal intelligence, geospatial analytics, and asset monitoring. That’s why it can sell into maritime, aviation, logistics, and defence-type use cases at the same time.

Sycamore builds an agent operating system that lets large companies create, govern, and run autonomous AI agents inside real enterprise workflows. On March 30, 2026, the Palo Alto startup closed a $65 million seed round led by Coatue and Lightspeed — a huge first round for a company launched by founder and CEO Sri Viswanath in late 2025 after leaving his full-time investing role at Coatue. The pitch is simple enough to understand and hard enough to execute: enterprises want AI agents to do real work, but most companies still don’t have a safe way to control those systems once they start touching production apps, data, and infrastructure.

That’s why this deal stands out.

A lot of AI startups are shipping wrappers, copilots, or narrow workflow bots. Sycamore is trying to own the layer underneath them — the control system that decides how agents are built and what they’re allowed to do. It also governs how they improve and who can audit the result. That’s a much bigger bet. Investors usually fund this kind of company early only when they think the founder has already seen a platform shift up close.

What is Sycamore’s agent operating system and how does it work?

Sycamore’s agent operating system is a full-lifecycle platform for enterprise AI: companies can discover use cases, build agents, deploy them, observe what they do, and evolve those systems over time. Users describe what they want in natural language, and Sycamore generates production-ready applications and integrations tailored to that company’s environment. It also builds agents, rather than forcing teams to stitch together a pile of separate tools.

The most interesting part is the trust model. Sycamore says agents don’t just get full autonomy on day 1 — they move “from observation to action” as they prove reliability. Every operation is isolated and auditable. Governance is built in from the start, with roles, permissions, control planes, and traceability. For enterprise buyers, that matters a lot more than a flashy demo.

The platform is also pitched as more than orchestration. Sycamore describes 4 core building blocks: a progressive trust system, adaptive system generation, continuous improvement, and collective intelligence. In plain English, that means the software is supposed to connect company data and workflows. It learns from outcomes and preserves institutional knowledge across deployments instead of treating each agent like a disposable one-off.

That’s the before-and-after story here. Before, an enterprise team has to wire together models, permissions, logs, integrations, and human review by hand. After, Sycamore wants a single agent operating system to handle that work. It generates the system, watches it, and keeps tightening the loop as it learns.

Who founded Sycamore and why are investors backing it?

The founding story

Sycamore was founded by Sri Viswanath, who launched the company after leaving his full-time role at Coatue in the fall of 2025. His argument is that AI agents are the next platform shift in enterprise computing: models can now reason and act, but companies still lack the infrastructure to deploy that autonomy safely. That’s basically the whole company thesis.

Why Sri Viswanath fits this category

This isn’t a first-time founder guessing his way through enterprise plumbing. Viswanath has spent more than 20 years building enterprise platforms, with stops at Sun Microsystems and VMware, then CTO roles at Groupon and Atlassian. At Atlassian, he led the company’s cloud transformation and said he scaled the engineering organization to more than 7,000 people. That’s exactly the kind of operating experience investors like to see when the product is all about control, reliability, and scale.

There’s another reason the round came together fast. Viswanath told TechCrunch that “the round came together through long-standing relationships,” which makes sense given the cap table. Before starting Sycamore, he was a general partner at Coatue focused on AI and enterprise. He’d already spent years around the buyers, builders, and backers now crowding into this category.

Early signals and the seed round

Sycamore hasn’t named customers, but Viswanath said the company already has traction with large enterprise buyers. The team works directly with Fortune 100 companies, and the company describes a founding group that includes researchers from Stanford and Cornell plus engineers from Meta, Google, and Atlassian. That’s not the same thing as published revenue. It is a real signal that the startup is selling into serious accounts early.

The round itself is stacked. Coatue and Lightspeed led the $65 million seed. Additional participation came from Abstract Ventures, Dell Technologies Capital, 8VC, Fellows Fund, and E14 Fund. The angel list is unusually heavyweight too: Bob McGrew, Lip-Bu Tan, Ali Ghodsi, Frederic Kerrest, Soham Majumdar, Mike Knoop, BJ Jenkins, Francois Chollet, Jerry Tworek, Jay Simons, and others all show up around the deal.

How does Sycamore compare with other agent operating systems?

Sycamore isn’t walking into an empty category. The source deal report names smaller startups like Maisa AI, bigger newly funded entrants like OpenAI-backed Isara with a reported $94 million raise, and growth-stage players like Airia and Port. Those two each announced $100 million rounds in late 2025. Then you’ve got platform giants trying to own the same control point — OpenAI with Frontier and Anthropic with Cowork. Microsoft Azure has Foundry, and AWS has Amazon Bedrock AgentCore.

But the real competition isn’t only other startups. It’s also the messy status quo inside big companies: internal platform teams bolting together model APIs, access controls, observability tools, workflow software, and homegrown security review. That approach can work for a pilot. It gets painful fast when agents start crossing business functions or making decisions with real consequences.

Sycamore’s differentiation is that it’s trying to sell the whole system, not a narrow add-on. Viswanath told TechCrunch most tools “layer agents on top” of existing workflows, while Sycamore starts with the problem and builds the right mix of agents and back-end systems. It also builds front ends and integrations from scratch. Pair that with the company’s progressive trust model and governance-heavy design, and you can see the investor bet: if enterprises really do move from assistants to autonomous operators, the control plane may be worth more than any single agent app.

Why does this $65M seed round matter for Sycamore?

Because $65 million is a giant seed for a company that’s still early. It changes what Sycamore can attempt.

A smaller round would’ve pushed the company toward a tighter, maybe safer product. This one gives it room to chase the broader thesis — infrastructure, governance, research, and enterprise deployment all at once. That lines up with how Sycamore presents itself: frontier research that ships, a trust layer for autonomy, and direct work with large enterprises rather than a quick self-serve tool.

It also says something about investor psychology right now. Coatue and Lightspeed aren’t backing Sycamore because enterprise agent demand is already fully proven. They’re backing it because they think the bottleneck is shifting from model quality to control, security, and orchestration. If that’s right, the valuable company won’t just be the one with the smartest model. It’ll be the one that helps enterprises trust autonomous systems enough to actually deploy them.

And frankly, that’s a more defensible story than “we built another AI coworker.”

How big is the market for an agent operating system?

The numbers are why founders and VCs keep piling in. Grand View Research estimates the enterprise agentic AI market was worth about $2.58 billion in 2024, will reach $3.67 billion in 2025, and could climb to $24.5 billion by 2030, implying a 46.2% compound annual growth rate. North America held more than 39% of the market in 2024, which fits Sycamore’s focus on big U.S. enterprises.

Gartner’s adoption forecasts are just as aggressive. It said in August 2025 that 40% of enterprise applications would feature task-specific AI agents by the end of 2026, up from less than 5% in 2025. Gartner has also said that by 2028, 33% of enterprise software applications will include agentic AI capabilities, up from less than 1% in 2024.

But this isn’t a clean gold rush. Gartner also warned that more than 40% of agentic AI projects could be canceled by the end of 2027, largely because many efforts won’t show enough value or mature autonomy. That skepticism helps explain Sycamore’s pitch: if failure rates stay high, buyers will care even more about governance, auditability, and measured autonomy instead of raw demo magic.

Conclusion

Sycamore’s agent operating system pitch is ambitious, maybe uncomfortably so. That’s also why investors wrote such a big first check: they’re not funding a feature, they’re funding a bid to become the operating layer for enterprise AI agents. The next thing to watch is whether Sycamore can turn unnamed big-company traction into visible deployments before the giants swallow the category whole.

Read how ScaleOps funding lands $130M for cloud efficiency to automate Kubernetes and AI infrastructure optimization in real time

FAQ

What funding did Sycamore raise?

Sycamore raised a $65 million seed round announced on March 30, 2026. Coatue and Lightspeed led the deal. The investor list also included firms such as Dell Technologies Capital, 8VC, Fellows Fund, E14 Fund, and Abstract Ventures, plus angels like Bob McGrew, Lip-Bu Tan, and Ali Ghodsi.

How does Sycamore’s agent operating system work?

It’s built to let enterprises describe intent in natural language and then generate production-ready agents and apps around that goal. It also builds integrations. The system adds governance from the start — with isolation, audit logs, permissions, human oversight, and a “progressive trust” model where agents earn more autonomy over time instead of getting it automatically.

Who is Sri Viswanath? 

Sri Viswanath is Sycamore’s founder and CEO, and he previously worked as CTO at Atlassian and Groupon after earlier engineering roles at Sun Microsystems and VMware. He also spent time at Coatue as an investor focused on AI and enterprise companies, which helps explain both the company’s strategy and the strength of its early backers.

Why is the agent operating system market attracting so much money? 

Because enterprises are moving from AI assistants to AI systems that can actually take actions across apps and workflows, and that creates a new control problem. Market researchers and Gartner both expect fast adoption and sharp revenue growth in enterprise agentic AI over the next few years. That’s why investors are willing to fund platforms that promise orchestration, governance, and security — not just another chatbot front end.

Starcloud builds orbital computing satellites that process data in space and aim to grow into full-blown off-world cloud infrastructure. Its latest Series A values the company at $1.1 billion, a striking number for a startup pitching space data centers just 17 months after Y Combinator demo day. The pitch is simple: Earth is running into power, land, and political constraints as AI data centers scale. Philip Johnston, Ezra Feilden and Adi Oltean founded Starcloud in 2024 in Redmond, Washington.

What is Starcloud and how do its space data centers work?

Starcloud’s product is basically orbital compute infrastructure. A customer with a satellite or space station can send raw data to Starcloud’s spacecraft and run GPU-heavy processing in orbit. It can store results there, then send down smaller, more useful outputs instead of dumping huge raw files back to Earth. Starcloud-2 delivers this through a smallsat with a GPU cluster, persistent storage, 24/7 access, and custom thermal and power systems. The company plans to make it fully operational in sun-synchronous orbit by 2027.

Starcloud already has its first proof point in orbit. Starcloud launched Starcloud-1 in November 2025 with the first Nvidia H100 GPU in orbit. In December 2025, that satellite ran a version of Gemini in space and became the first spacecraft to train an LLM in orbit using nanoGPT. That matters less as a stunt than as a hardware test: if a terrestrial AI chip can survive launch and operate in space, the roadmap stops sounding totally ridiculous.

For spacecraft operators, the before-and-after is pretty clear. Before Starcloud, an Earth observation company often has to downlink massive data sets to ground stations, wait for bandwidth, then process the payload on Earth. With Starcloud, the bet is that a lot of that work gets done in orbit first. That cuts latency and avoids wasting bandwidth on raw data. That’s why Starcloud’s first satellite is already analyzing data from Capella Space’s radar spacecraft, and why the company talks about satellite data processing as its first real business rather than chasing giant AI training jobs on day 1.

There’s a second customer type built into the plan. Starcloud-2 isn’t only pitched to in-space users. It’s also framed as a secure, sovereign cloud node for terrestrial users who want storage and compute that sit outside any single country’s physical infrastructure. That’s niche today. Still, it shows where Starcloud wants to go: not just edge compute for satellites, but orbital data centers that can eventually pull workloads away from Earth.

Who founded Starcloud and why build space data centers?

The founding story

Johnston’s pitch has always been blunt: if AI keeps scaling, Earth-bound data centers will slam into energy and permitting limits. Starcloud was built around that thesis in 2024, first with smaller orbital compute missions and then with a longer-term plan to launch much larger platforms once heavy-lift economics improve. Y Combinator’s profile shows the company was already designing a “micro data center” for a 2026 launch and a larger “Hypercluster” for the Starship era.

Why this team has unusual founder-market fit

Johnston isn’t a random AI founder trying on a space startup costume. He’s a second-time founder who previously worked at McKinsey on satellite projects for national space agencies, and he has degrees from Harvard, Wharton, and Columbia, plus a CFA charter. It’s an odd résumé for a rocket-adjacent company. But that mix of aerospace, policy, and capital markets fits a business that lives or dies on both engineering and launch economics.

Feilden brings the spacecraft side. He spent about a decade in satellite design, with work at Airbus Defence & Space and Oxford Space Systems, including missions tied to NASA’s Lunar Pathfinder. His background in deployable solar arrays and large structures is especially relevant because Starcloud’s long-term design problem isn’t just compute. It’s also power generation and thermal management.

Oltean rounds out the compute piece. At SpaceX, he worked on Starlink networking for in-motion use cases, including Starship-related work. Before that, he worked at Microsoft on large GPU production clusters and early LLM infrastructure, and Y Combinator says he holds more than 25 patents. That’s the kind of operator background investors like because Starcloud is trying to merge spacecraft engineering with data center engineering. Not replace one with the other.

Early execution, fundraising, and the real competition

Starcloud has moved fast enough to make investors tolerate a very capital-intensive story. The company has now raised $200 million in total. Benchmark and EQT Ventures led this Series A, and it closed just 17 months after demo day. Starcloud had already launched Starcloud-1, booked follow-on missions, and secured LOIs for H100 compute time in space before landing this round. It also set up payload manufacturing in Redmond. This isn’t a slide-deck company anymore.

The competition is getting real, though it’s still messy. Aetherflux, founded by Robinhood co-founder Baiju Bhatt, has shifted from laser power transmission toward powering space data centers and expects its first data center satellite in 2027. Aethero is coming at the category from a different angle. It’s building radiation-hardened edge computers for satellites; its Jetson-based NxN module was built to hit 100 TOPS in a CubeSat-compatible form factor.

Starcloud’s edge is simple: it has already put a terrestrial H100 in orbit, and it’s talking about commercial workloads now rather than only future architectures. The legacy alternative is still Earth. And that’s a massive incumbent. Investors aren’t backing Starcloud because space is easier. They’re backing it because if launch costs fall enough, energy economics could flip.

Why does Starcloud’s Series A matter?

This round matters because Starcloud isn’t raising money to polish software. It’s raising to build hardware that only starts to make sense at scale.

Later in 2026, the company plans to launch Starcloud-2 with multiple GPUs, including an Nvidia Blackwell chip and an AWS server blade. It’ll also carry a bitcoin mining computer. That sounds a little chaotic. It’s also smart. Starcloud needs data on power, cooling, fault tolerance, and mixed workloads in orbit, not just one clean demo.

Then comes Starcloud-3. The company is designing that spacecraft to be a 200-kilowatt, 3-ton orbital data center designed for SpaceX’s Starship deployment architecture — the “PEZ dispenser” system built for Starlink satellites. Johnston thinks that vehicle could be the first one to compete with terrestrial energy costs, with power on the order of $0.05 per kilowatt-hour if launch prices land around $500 per kilogram.

That “if” is doing a lot of work.

Starship still isn’t in commercial service, and Johnston has said he expects access to open in 2028 and 2029. He’s also been candid that Starcloud won’t be competitive on energy cost until Starship is flying frequently. If that slips, the fallback is to keep launching smaller versions on Falcon 9. So this Series A is really a bet on two roadmaps at once: Starcloud’s spacecraft roadmap and SpaceX’s launch roadmap.

Johnston also broke the business model into 2 parts. Near term, Starcloud sells processing power to other spacecraft. Longer term, if launch gets cheap enough, it wants distributed orbital clusters to pull work from Earth-based data centers. Investors didn’t just fund a satellite company here. They funded a staged transition plan.

What’s driving demand for space data centers now?

The terrestrial data center market is a huge part of the story. In the Americas alone, operational data center capacity has reached 43.4 GW, with another 25.3 GW under construction, and nearly 89% of that pipeline was already pre-committed in early 2026. Vacancy was just 4.2%. In plain English: the market is building like crazy and still looks tight.

It’s not just demand. It’s where and how you’re allowed to build. Cushman & Wakefield points to power availability, grid access, permitting friction, land use, and local regulation as the bottlenecks shaping the next wave of projects. That lines up almost perfectly with Starcloud’s pitch that resource and political obstacles on Earth create room for in-space computing.

There’s still a giant reality check, though. Only dozens of advanced GPUs currently operate in orbit, while Nvidia sold nearly 4 million chips to terrestrial hyperscalers in 2025. SpaceX’s Starlink network may be the biggest satellite constellation ever built, but even that produces only around 200 megawatts of power. Meanwhile, terrestrial facilities with more than 25 gigawatts of capacity are under construction in the U.S. alone. The scale gap is absurd right now.

That’s why the interesting question isn’t whether orbital data centers replace terrestrial ones soon. They won’t. The question is whether some high-value workloads — especially Earth observation, defense, sovereign compute, and latency-sensitive edge inference — migrate first. If they do, Starcloud doesn’t need to win the whole market to build a very real business.

Should you take Starcloud’s orbital data center plan seriously?

Honestly? Yes — but only if you treat it as a long-duration infrastructure bet, not a normal startup growth story.

Starcloud has already done the part that many deep-tech companies never do: it put hardware in orbit and ran modern AI silicon there. It turned a weird thesis into something concrete. That’s a big deal. So is the fact that Benchmark and EQT were willing to fund it at a $1.1 billion valuation.

But the company still depends on breakthroughs that aren’t fully under its control. Launch cadence. Launch cost. Space-rated cooling. Multi-satellite synchronization. All of that has to work before space data centers look anything like a mainstream cloud category.

So the next thing to watch isn’t the valuation. It’s whether Starcloud-2 launches in 2026 and whether Starcloud can turn orbital compute from a technical flex into repeatable revenue.

Read how Qodo raises $70M for AI code review verification as it builds a system to check and secure AI-written code before deployment

FAQ

What is Starcloud’s latest funding round?

Starcloud’s latest round is a Series A that values the company at $1.1 billion. Benchmark and EQT Ventures led the financing, and the company has now raised $200 million in total after reaching that milestone only 17 months after Y Combinator demo day.

How do Starcloud’s space data centers actually work? 

They work by putting GPU-equipped satellites in orbit so data can be processed before it ever comes back to Earth. Starcloud-2 is designed with a GPU cluster, persistent storage, always-on access, and custom thermal and power systems, while Starcloud-1 already proved an H100 could run AI workloads in space.

Who founded Starcloud?

Starcloud was founded in 2024 by Philip Johnston, Ezra Feilden, and Adi Oltean in Redmond, Washington. Johnston came from McKinsey satellite work, Feilden from Airbus and Oxford Space Systems, and Oltean from SpaceX and Microsoft’s large-scale compute infrastructure world.

Is Starcloud in the cloud market or the space market? 

It’s really both. Starcloud sits in the emerging orbital data center category — part satellite infrastructure, part cloud computing, part edge AI — and its near-term focus is selling in-space compute to spacecraft operators before chasing broader terrestrial workloads.