Tag: startup funding

Spense builds secured credit infrastructure that lets Indian banks launch asset-backed cards and credit lines without tearing out their old core systems. The Bengaluru startup has now raised $2.8 million, or about ₹27 crore, in a seed round led by Arkam Ventures, as lenders get a lot more careful about unsecured retail credit. That caution is the whole opening here: small-ticket loans can grow fast, but defaults and recovery costs can wreck the math just as quickly. Founded in 2022 by Pawan Kumar and Srinivas Krishnamurthy, Spense is betting that the next big lending buildout in India won’t start with better guesswork on risk. It’ll start with collateral.

What is Spense funding building in secured credit infrastructure?

Spense’s secured credit infrastructure is a bank-facing software layer for launching products like secured credit cards and reusable credit lines backed by customer assets such as fixed deposits, mutual funds, insurance policies, and other financial holdings. A bank plugs Spense into its existing systems, maps an eligible asset, creates a revolving limit against that asset, and then lets the customer spend through a RuPay credit card, a UPI credit line, or another payment rail. The key point is simple. The customer experience feels like digital credit, while the lender still has collateral underneath.

That matters because the old journey is clunky. A customer who needs a fresh loan usually has to apply again, upload documents again, and wait again. Spense is trying to turn that into a standing line of credit that can be used, repaid, and reused. More like a credit card than a one-off personal loan.

Its latest product is CLOU, short for Credit Line on UPI. The idea fits neatly with India’s payment rails. NPCI launched Credit Line on UPI in 2023, giving banks a new way to offer formal credit directly through the UPI interface instead of forcing every use case through a plastic card.

Banks care just as much about the second layer here: operations. In secured card programs, somebody still has to handle screening and decisioning. Card origination, management, controls, billing, collections, and the logic around releasing or enforcing collateral still need to work too. Competitors in this market talk openly about those steps because they’re exactly where legacy banks get slowed down. Spense’s pitch is that banks shouldn’t have to rebuild all that plumbing just to launch a modern secured product.

Who founded Spense before the funding round?

The idea behind Spense funding

Spense was started in 2022 by Pawan Kumar and Srinivas Krishnamurthy. Their thesis is blunt: unsecured lending is getting harder to scale profitably, especially at the small-ticket end where default recovery can cost about ₹1,000. If the loan itself is only ₹2,000 or even ₹10,000, one bad outcome can wipe out the upside from a bunch of good ones.

That’s why Kumar’s line from this round lands so cleanly: “We cannot underwrite the next 300 Mn Indians the same way we underwrote the first 150 Mn.” He’s not saying underwriting data is useless. He’s saying the starting assumption should change.

Why the founders look credible

Kumar’s background is in applied science at Uber India, while Krishnamurthy previously worked as a technical lead at BNP Paribas. That’s a pretty specific mix for this problem. One side brings modeling and product thinking. The other brings experience with bank-grade systems and rails. It also helps explain why Spense isn’t trying to be a consumer lender. The company sits one layer lower, inside the infrastructure banks use to launch and manage credit products.

Before this seed round, Spense had already positioned itself around programmable banking infrastructure rather than a single narrow card workflow. In its earlier pre-seed round, the company was described as building infrastructure for secured credit cards, forex programs, prepaid programs, and connected banking flows. That broader starting point matters because CLOU looks less like a random new feature and more like the next logical product on the same stack.

Early traction and the new round

The operating numbers are strong for a seed-stage infrastructure startup. Spense works with 7 major banks across India and powers more than 2 lakh active cards. It issues over 40,000 cards every month. On its own math, that’s close to 8% of India’s monthly credit card issuances.

Arkam Ventures led the $2.8 million seed round, with Razorpay Ventures, GrowthCap Ventures, and Atrium Ventures also participating. Before that, Spense had raised a $1.85 million pre-seed round led by GrowthCap Ventures.

How Spense compares with other secured credit infrastructure players

Spense isn’t alone in bank-tech plumbing. M2P sells an API-first stack for deposits and lending. It also handles cards, fraud, compliance, onboarding, and collections. Hyperface positions itself as a credit-cards-as-a-service platform with ready SDKs, PWA tooling, analytics, and fast co-branded launches. Zeta sits higher up the scale curve, selling card-processing and banking software used by large institutions for digital onboarding and instant issuance.

Spense is narrower, and that’s probably the point. It’s building around secured, asset-backed credit from day 1, then extending that into Credit Line on UPI. Legacy alternatives are even messier: banks either build much of this in-house or stitch together multiple vendors across onboarding, card management, risk, collections, and payments. Spense’s edge is that it packages secured credit and reusable lines into one bank-ready layer. Payment access is part of the same system, instead of an afterthought.

Why are investors backing secured credit infrastructure now?

This round matters because it gives Spense room to push beyond secured cards and make CLOU a real bank product, not just a demo-worthy concept. If that works, banks get a way to serve first-time borrowers without jumping straight into pure unsecured risk. That’s a useful wedge.

There’s also a credit-building angle that’s easy to miss. Spense wants banks to start customers on collateral-backed revolving lines, watch repayment behavior for 6 to 12 months, and then gradually open up unsecured limits as trust is earned. Kumar compares that to the secured-card journey many immigrants in the US use to establish a credit file. For India, that could be a smarter bridge for homemakers, retirees, informal workers, and small business owners who do have assets but still don’t show up well in standard underwriting models.

Investors aren’t just backing volume. They’re backing a cleaner risk structure. When the collateral is already pledged, collections look very different, and so do unit economics. That’s a lot more interesting to bank partners than another pitch about a slightly better scoring model.

Why is India warming up to secured credit infrastructure now?

The timing lines up with a clear shift in the market. In March 2025, TransUnion CIBIL said 41% of India’s first-time borrowers were Gen Z. It also noted that 40% of new-to-credit consumers enter formal borrowing through consumption-led products such as credit cards, personal loans, and consumer durable financing. But originations in those consumption-led products for new-to-credit borrowers were down 21% year over year in the quarter ended December 2024, showing how much lender caution had already kicked in.

By the quarter ended December 2025, CIBIL’s overall Credit Market Indicator had improved to 102 from 97 a year earlier, helped by higher gold-loan supply and stronger participation from first-time borrowers. That’s the interesting part. Growth is still there, but the center of gravity is moving toward products with clearer collateral or tighter control.

Banks have had regulatory reasons to rethink risk too. In November 2023, the RBI increased risk weights on unsecured consumer credit from 100% to 125%, while excluding categories such as housing, education, vehicle loans, and loans secured by gold and gold jewellery. That didn’t kill unsecured lending, but it did make lenders more selective.

There’s also sheer borrower volume. India’s new-to-credit borrower base reached 4.4 crore in the 12 months ending February 2026, up from 3.6 crore 4 years earlier. That’s a huge pool of people who may have savings, deposits, or other financial assets before they have a deep credit history. Products built on secured credit infrastructure are trying to turn that mismatch into a business.

What should readers watch next?

Spense has picked a tough but timely lane. It isn’t promising magic underwriting. It’s trying to make secured lending feel as instant and reusable as the best digital credit products.

If Spense can turn bank partnerships into large-scale CLOU rollouts, it won’t just be another fintech funding round. It could become an early signal that India’s next phase of credit is getting more collateral-aware and more reusable.

Read how Oxmiq Labs raised a $35M Series A co-led by Fundomo and Samsung Catalyst Fund to scale its licensable GPU architecture, helping chipmakers build custom AI silicon with lower costs and CUDA-compatible software.

FAQ

• What funding did Spense raise? Spense raised $2.8 million in a seed round worth about ₹27 crore. Arkam Ventures led the round, and Razorpay Ventures, GrowthCap Ventures, and Atrium Ventures joined in. The company had also raised a $1.85 million pre-seed round earlier.
• How does Spense’s platform work for banks? It gives banks a software layer to launch asset-backed revolving credit products without replacing their existing core systems. A bank can map collateral like a fixed deposit or mutual fund to a reusable limit, then let the customer spend through a RuPay card, a UPI credit line, or similar payment rails.
• Who are the founders of Spense? Spense was founded in 2022 by Pawan Kumar and Srinivas Krishnamurthy. Kumar came from applied science work at Uber India, while Krishnamurthy previously worked at BNP Paribas, which gives the founding team a mix of data, product, and banking-systems experience.
• Is Spense a lender or a banking infrastructure startup? Spense is a banking infrastructure startup, not a balance-sheet lender. It builds the secured credit infrastructure banks use to issue cards and credit lines, manage workflows around collateral, and distribute those products through payments channels like cards and UPI.

Oxmiq Labs builds licensable GPU architecture and AI infrastructure software for chipmakers and data center operators. The Campbell, California-based company has raised $35 million in Series A funding in the latest Oxmiq Labs fundinground, a deal that matters because AI demand is outrunning the supply of affordable compute. Founded in 2023 by GPU architect Raja Koduri, Oxmiq is trying to lower the cost of building custom AI silicon without forcing customers into a full chip program from scratch. It’s a hard pitch. But it’s a clear one.

Fundomo and Samsung Catalyst Fund co-led the round, with MediaTek, AM Intelligence Labs, Pegatron Venture Capital, CDIB-TEN, Darwin Ventures, and Morgan Creek Digital also joining. Total funding now stands at $60 million. Oxmiq will use the new capital to scale OxCore, its licensable GPU architecture platform for semiconductor companies and AI system builders.

Koduri framed the thesis in blunt terms: “Today, state-of-the-art AI reaches most people through a handful of channels, and the cost of the compute underneath is the reason. Bring that cost down, and you widen who gets to build with it.” That’s the wager.

What does Oxmiq Labs build?

Oxmiq isn’t selling a single chip. It’s building a stack that other companies can license, customize, and tape out under their own brands. The core piece is OxCore, a licensable GPU compute core that combines CPU-style scheduling and GPU tensor compute. It also includes CUDA-compatible programmability in one architecture. It’s meant to scale from a chiplet at the edge to a larger SoC and up to data center systems.

Under the hood, OxCore is split into distinct engines. There’s OxORC, a RISC-V orchestrator for async dispatch and hardware task scheduling. There’s OxTEN for tensor workloads. And there’s OxSIMT, the CUDA-compatible engine that supports native CUDA and PTX. That mix matters because Oxmiq isn’t asking customers to start over with a blank-sheet software stack. It’s trying to meet them where the AI world already is.

That software bridge is OxPython. In plain English, it lets code written for CUDA keep running even when the hardware underneath isn’t Nvidia. Oxmiq shows unmodified PyTorch and vLLM workflows running through its runtime. It also shows Hugging Face workflows, with verified targets including Tenstorrent systems and an FPGA implementation of OxCore. For anyone who’s watched alt-silicon startups die on the software hill, that’s probably the most interesting part.

Then there’s OxCapsule, which handles heterogeneous compute. A customer can use one command to authenticate and discover hardware. It can also spin up remote dev environments, stream workloads, and place models across mixed infrastructure. Oxmiq even shows a 397B-parameter model spread across 3 RTX A6000 systems as a single inference endpoint. Before that, teams would be doing manual sharding and driver wrangling. And a lot of swearing.

Who is Raja Koduri and how is Oxmiq Labs set up?

Founded in 2023 around one thesis

Oxmiq was founded in 2023 by Raja Koduri and is built around a simple idea: the GPU stack needs to be re-architected “from atoms to agents,” not merely tweaked at the margins. The company’s position is that customers should be able to own their compute with licensable GPU IP and modular chiplet platforms. It also wants a software layer that avoids dependence on proprietary GPU ecosystems. Oxmiq’s main offices are in Campbell, California, and Hyderabad, India.

Why Koduri has real market fit

Koduri isn’t a first-time founder with a fancy AI deck. He previously held senior roles at Intel, AMD, and Apple. He was Intel’s former executive vice president and chief architect for high-performance computing and GPUs, AMD’s former head of Radeon Technologies Group, and Apple’s former director of GPU architecture. He also worked on visual effects technology in India through Makuta VFX Studio, and he holds a master’s degree in electronics and communications from IIT Kharagpur.

That background is why investors are paying attention. At AMD, Koduri was associated with the architecture shift behind Polaris, Vega, and Navi. At Apple, he helped steer graphics hardware during the Retina era. You don’t have to buy every promise Oxmiq is making to see why people are willing to fund a serious look.

Early traction, team depth, and the round itself

Oxmiq hasn’t published revenue, and that’s fine — this is still a deep-tech startup. But it has shown a few early signals that matter. OxCore has been validated on FPGA hardware and, in simulation, has generated tokens on GPT-4-class models. OxCapsule entered public beta on November 4, 2025, and Oxmiq has 150 beta users across 15-plus organizations and access to 300-plus GPUs. The company has also announced AM Intelligence Labs as a customer.

The team story is stronger than the usual stealth-mode blur. Oxmiq says its staff spans the US and India and represents 500-plus years of combined experience. The advisory roster includes Jim Keller, now CEO of Tenstorrent, as a board member. That doesn’t guarantee execution. But it does tell you this isn’t a two-slide science project.

On funding, the facts are straightforward. The Oxmiq Labs funding round brought in $35 million in Series A capital, co-led by Fundomo and Samsung Catalyst Fund, and joined by MediaTek, AM Intelligence Labs, Pegatron Venture Capital, CDIB-TEN, Darwin Ventures, and Morgan Creek Digital. Total funding is now $60 million, and the company will use the new money to scale OxCore.

Where Oxmiq sits against incumbents

Oxmiq is entering a category that already has real players. Arm licenses IP broadly and offers access to pre-designed IP packages through its licensing programs. Imagination sells GPU and AI IP through its PowerVR family. VeriSilicon markets Vivante GPGPU IP ranging from embedded devices to server-class use cases. Those are the cleanest apples-to-apples comparisons on the IP side.

The legacy alternative is uglier: either license conventional GPU IP and adapt your system around it, or stay tied to Nvidia-centric hardware and software stacks. Oxmiq’s pitch is that customers should be able to tape out custom AI silicon under their own brand. They’d keep CUDA-style software compatibility and build across chiplets, SoCs, and mixed fleets without getting trapped by one vendor. That’s a sharper position than “we also do AI chips,” which, frankly, is where a lot of startups stop.

Why does Oxmiq Labs funding matter now?

Chip startups usually burn huge amounts of capital before they prove much of anything. Oxmiq is trying a less reckless route. Its model is IP-first, not full-SoC-first, and the company already generates revenue from customer engagements while preserving cash for the stack itself. That’s a lot more capital-efficient than pretending every ambitious architecture company should also become a product company. A systems company. And a manufacturing story all at once.

That’s also why Rajeev Surati’s comment from Fundomo matters. He said most compute IP forces customers to bend memory, packaging, and foundry choices around the chip, while Oxmiq is doing the reverse. Strip out the investor polish and the point is simple: if customers can start with their own constraints instead of the vendor’s, the economic case gets stronger.

The use of proceeds is targeted. Oxmiq isn’t saying it will build a moonshot factory or a giant proprietary cloud. It plans to scale OxCore. For buyers, that could mean faster access to custom AI silicon design paths. For investors, it’s a bet that licensable architecture and compatibility layers may be a smarter wedge than trying to outspend entrenched GPU vendors head-on.

How big is the market for custom AI GPU IP?

The timing isn’t random. Grand View Research estimates the global graphic processor market could reach $357.99 billion by 2030. It also sizes the data-center GPU chip market at $2.31 billion in 2024, with that segment projected to hit about $5.03 billion by 2030 at a 13.9% CAGR. Those numbers don’t map perfectly to Oxmiq’s addressable market, but they do show why investors are hunting for picks-and-shovels plays below the hyperscaler layer.

The structural trend is obvious. AI inference is spreading across edge boxes, enterprise clusters, sovereign infrastructure projects, and mixed fleets that don’t look anything like a neat rack of identical accelerators. Oxmiq’s own software messaging leans hard into that reality: heterogeneous hardware, CUDA gravity, and the need to make existing code run without rewrites. If that shift keeps accelerating, companies that license adaptable compute IP could get a real opening.

What to watch after Oxmiq Labs funding

The next thing to watch isn’t the headline round size. It’s whether Oxmiq can turn impressive architecture language into repeatable customer wins.

The Oxmiq Labs funding story is interesting because it sits between two extremes: old-school IP licensing and all-in AI hardware moonshots. If Koduri’s team can prove that custom GPU IP plus software compatibility really cuts time, cost, and lock-in, Oxmiq could matter a lot more than a typical Series A startup. If not, it’ll end up as another smart idea that ran straight into the brutality of the compute market.

Read how Together AI raised an $800M Series C led by Aramco Ventures to expand its open AI infrastructure platform, scale GPU compute capacity, and help developers build, fine-tune, and deploy open-source AI models at enterprise scale.

FAQ

• What is the latest Oxmiq Labs funding round? Oxmiq Labs has raised $35 million in a Series A round. Fundomo and Samsung Catalyst Fund co-led the deal, and the round brought the company’s total funding to $60 million. The money is earmarked to scale OxCore, the startup’s licensable GPU architecture platform.
• What does OxCore actually do? OxCore is licensable GPU IP that customers can customize and tape out as part of their own AI silicon programs. It combines a RISC-V orchestration layer and tensor acceleration. It also includes CUDA-compatible programmability, so developers can keep using familiar software flows instead of rebuilding everything from scratch.
• Who founded Oxmiq Labs? Raja Koduri founded Oxmiq Labs in 2023 and serves as CEO. He’s one of the better-known names in GPU architecture, with senior roles at Intel, AMD, and Apple before launching the company.
• Is Oxmiq Labs a GPU IP company or an AI infrastructure startup? It’s both, and that’s the point. Oxmiq sells licensable GPU IP through OxCore, but it also builds software and orchestration tools like OxPython and OxCapsule to make heterogeneous AI compute usable across different hardware environments.

Together AI, a neocloud provider that rents GPU clusters and open-source AI infrastructure, has raised an $800 million Series C in a massive Together AI funding round announced on Wednesday, July 1, 2026. The pitch is pretty simple: developers want cheaper, more flexible AI compute than closed-model APIs and hyperscaler queues often give them. Founded in 2022 by Vipul Ved Prakash with Percy Liang and Ce Zhang, the company is betting that open models — not just proprietary ones — will power a lot of production AI.

What is Together AI and how does it work?

Together AI is basically a full-stack platform for running, tuning, and serving open-source models. A developer can start with serverless inference through an OpenAI-compatible API, move heavier traffic onto dedicated endpoints, and then graduate to single-tenant GPU infrastructure when latency, privacy, or throughput matters more than convenience.

That workflow is a big part of the appeal. You can pick a model from Together AI’s model library and call it through standard API tooling. You can also upload your own weights from Hugging Face or S3 for dedicated deployment, then keep everything on the same platform instead of stitching together separate vendors for experimentation and production. For teams building agents, batch jobs, or internal copilots, that continuity saves a lot of ugly platform work.

The customization layer is more than a checkbox. Together AI supports fine-tuning jobs from the command line, including LoRA and preference tuning methods like DPO, RPO, and SimPO. It then lets users deploy those tuned models for inference. It also offers an evaluations API and a sandbox product, so developers can test model behavior and run code in isolated environments without bolting on extra infrastructure.

And if a customer needs raw horsepower, Together AI sells that too. Its GPU clusters are built on Kubernetes and support Slurm-style job scheduling. They use high-speed InfiniBand for multi-node workloads and offer hardware configurations including Nvidia H100, H200, and B200 systems. Clusters can be provisioned in minutes. They can scale in real time and pair with persistent storage — exactly the sort of plumbing most AI teams don’t want to build themselves.

Who founded Together AI and why are investors betting $800M?

The founding story

Together AI started in 2022 with a blunt thesis: foundation models were getting centralized because the compute bill was getting absurd, and open alternatives needed their own infrastructure stack. That founding logic still runs through the company now. It isn’t just selling cloud capacity. It’s selling the idea that open-source AI can be production-grade if someone builds the right rails around it.

Why these founders actually fit the problem

Ved Prakash brings the startup operator résumé. Before Together AI, he founded social media search company Topsy, which Apple bought in 2013 for a reported $200+ million. That matters because Together AI isn’t a research project pretending to be a business — it has a CEO who’s already built and exited a data-heavy platform company.

Liang is the academic heavyweight in the mix. He’s a Stanford computer science professor and director of the Center for Research on Foundation Models, and his work has focused on making foundation models more accessible, understandable, and benchmarked in a rigorous way. If your company thesis is that open models should be usable in the real world, that’s strong founder-market fit.

Zhang is the systems builder. He’s a Neubauer Associate Professor at the University of Chicago, previously taught at ETH Zurich, and has spent years working on machine learning platforms and the infrastructure bottlenecks behind them. That’s the kind of background you’d want if your product sits between model research and ugly production compute.

Traction, fundraising, and the neocloud race

Together AI now has thousands of paying customers, with names including Cursor, Cognition, and Decagon. Annual bookings topped $1.15 billion as of the last quarter. That’s the line that makes this round feel less like speculative hype and more like a capacity deal for a business already under serious demand.

Aramco Ventures led the Series C, which came in at $800 million on an $8.3 billion valuation. The round also included Vista Equity Partners, General Catalyst, Emergence Capital, Nvidia, March Capital, Pegatron, SentinelOne’s S Ventures, and others. Before that, Together AI raised a $305 million Series B at a $3.3 billion valuation about 16 months earlier, and a $102.5 million Series A in 2023 led by Kleiner Perkins with Nvidia and Emergence Capital. Back in March 2026, it was seeking $1 billion at a $7.5 billion valuation, so this final outcome looks like less money but a richer price.

Competition is getting crowded fast. Direct rivals include other neocloud and AI infrastructure providers such as CoreWeave, Lambda, TensorWave, Fluidstack, and newer entrants chasing inference-heavy workloads. The older alternative is still the same trio everyone knows — AWS, Google Cloud, and Azure. The other substitute is just paying premium token prices to closed-model providers and living with the margin hit. Together AI’s edge is that it wraps open-model inference, fine-tuning, sandboxing, and GPU clusters into one stack. It sells that on cost, control, and speed rather than raw capacity alone.

Why does the Together AI funding round matter?

This round matters because AI infrastructure is brutally capital-intensive. Software startups can fake it with a small burn and a good demo. GPU clouds can’t. If Together AI wants to keep winning enterprise workloads, it needs actual hardware access, real capacity planning, and enough balance-sheet strength to support customers that don’t tolerate outages or latency spikes.

That’s why the size of this deal stands out. Together AI didn’t just raise equity. It also disclosed commitments for more than 500 MW of compute capacity to be capitalized independently by new investors. Pair that with thousands of customers already on the platform, and the message is obvious: investors aren’t just betting on open-source AI as an ideology. They’re betting that the open-model production stack is becoming a real infrastructure business.

There’s also a timing angle here. If open models keep improving, the vendor that can deliver solid inference economics and custom deployment without forcing companies into a closed ecosystem becomes a pretty attractive choke point.

How big is the AI infrastructure market behind Together AI?

The macro numbers are huge, and frankly a little wild. Grand View Research pegs the global AI data center market at $147.3 billion in 2025, with an estimate of $180.6 billion for 2026 and a jump to $810.6 billion by 2033. That implies a 23.9% compound annual growth rate. It helps explain why investors keep pouring giant checks into compute suppliers instead of only into model labs and app startups.

North America held the largest share of that market in 2025 at 37.5%, which lines up with where a lot of enterprise AI demand is landing first. The structural shift is broader than data center buildout alone. Together AI says open-source model usage across the industry tripled over the last year, echoing a change in buyer behavior: companies still want top-tier model quality, but they also want lower inference costs, customization, and less vendor lock-in than closed APIs usually offer.

Final take on Together AI funding

The easy read is that Together AI just raised a lot of money because AI is hot.

The better read is that this Together AI funding round is a bet on a specific future: one where enterprises run a mix of open and custom models, care obsessively about inference cost, and don’t want their entire AI stack locked inside one provider. If Together AI can turn its bookings into durable margins while keeping GPU supply ahead of demand, it could end up being a lot more important than just another cloud startup.

Read how Dovetail Capital raised ₹100 crore in a Series A led by Elev8 Venture Partners to expand its asset servicing platform, strengthen global operations, and simplify fund administration, compliance, and cross-border investing for institutional investors.

FAQ

• What is the latest Together AI funding round? Together AI raised an $800 million Series C announced on July 1, 2026, at an $8.3 billion valuation. Aramco Ventures led the round, and the investor list included names such as Vista Equity Partners, General Catalyst, Emergence Capital, Nvidia, March Capital, and Pegatron.
• How does Together AI work for AI developers? It gives developers one platform to run open-source models, fine-tune them, evaluate them, and deploy them on shared or dedicated infrastructure. A team can start with API-based inference, then move to custom endpoints, sandboxed development environments, or full GPU clusters as workloads get bigger and more sensitive.
• Who founded Together AI? Together AI was founded in 2022 by Vipul Ved Prakash, Percy Liang, and Ce Zhang, with Ved Prakash serving as CEO and Zhang as CTO. Ved Prakash previously sold Topsy to Apple, Liang is a Stanford professor who leads CRFM, and Zhang is a University of Chicago computer science professor with deep systems and ML infrastructure expertise.
• Is Together AI a cloud company or an AI startup? It’s really both, but the cleaner label is an AI neocloud. Together AI sells the underlying compute, inference, tuning, and deployment stack for open-source and custom models, which puts it closer to infrastructure than to a typical application-layer AI startup.

Dovetail Capital is a Mumbai-based investment asset servicing startup that handles fund administration, derivative clearing, investment advisory, and compliance for institutional investors. The Dovetail Capital funding news is a ₹100 crore Series A round led by Elev8 Venture Partners, and it matters because back-office finance infrastructure is getting harder as funds spread across India and offshore markets. Founded in 2017 by Dev Sampat, Mahesh Shekdar, and Vivek Singhania, the company is betting that asset managers want an independent service partner instead of relying on the same banks that also sell them competing products.

This is Dovetail’s first institutional round. It includes both primary and secondary capital. The fresh money will go into expanding the company’s international business.

What is Dovetail Capital and how does it work?

Dovetail Capital is basically an operating layer for funds and institutional investors that want to access Indian markets without stitching together 6 vendors on their own. Its funds platform works with offshore funds, foreign portfolio investors, offshore investment managers, institutional investors, and accredited investors. From there, Dovetail handles setup and administration. It also manages compliance processes and market access support across equity, derivatives, and fixed income.

The workflow is more specific than the usual “we do back office” pitch. On the fund administration side, Dovetail handles onboarding and new-fund setup. It also does shadow accounting, audit coordination, policy review, and detailed regulatory reporting. It runs its own Dovetail Compliance Management System, or DCMS, alongside investor login tools. So it isn’t just selling manpower.

Its derivative clearing business is another big piece of the stack. Dovetail’s clearing platform offers real-time trade confirmation and online access. It also includes risk management tools, business continuity features, and compliance checks that run as trading happens. For a fund or family office, that means less waiting around for manual confirmations and fewer ugly surprises after the market closes.

There’s a newer layer sitting on top of that core engine. Dovetail Bridge is built as a cross-border distribution and matching platform that connects wealth managers with global fund managers. It standardizes fund profiles and supports distribution agreements. It also adds admin support from first meeting to final close. The Bridge process is pretty direct: discovery meeting, then fund setup on the platform. After that comes investor access or allocation execution.

Who founded Dovetail Capital and what did they build?

The founding story

Dovetail was started in 2017 by Dev Sampat, Mahesh Shekdar, and Vivek Singhania. All 3 came from the machinery of institutional finance rather than the glossy consumer-fintech side of the market. That matters because fund administration and clearing are trust businesses first and software businesses second.

Sampat summed up the original thesis in the funding announcement:

“When we started Dovetail, asset servicing in India sat almost entirely inside large banks, the very institutions that also ran asset management, wealth management and broking. We believed Indian investors, just like their global counterparts, deserved an independent, technology-first partner with no competing agenda. This first institutional round, led by Elev8, validates that conviction and the team that has built on it.”

That’s still the cleanest explanation of what Dovetail is trying to be. Not another broker. Not a bank-owned custody unit. An independent asset servicing house.

Why these founders fit the market

The founder-market fit is real here. Dev Sampat studied economics at the University of Delhi and later earned an MBA from Symbiosis Institute of Business Management. Before Dovetail, he spent years at Kotak Mahindra Bank covering capital markets and securities services. He was the bank’s head of sales for the securities services business for more than 5 years.

Mahesh Shekdar brings the relationship and transaction-banking side. He previously led domestic asset management company relationships in Kotak Mahindra Bank’s financial institutions group. Before that, he worked in transaction banking roles at Standard Chartered and HDFC Bank. His profile shows more than 25 years of experience. That kind of tenure matters when you’re selling to mutual funds, insurers, and foreign institutional clients.

Vivek Singhania rounds out the operating core. He’s a chartered accountant and company secretary who previously handled global custodian tie-ups and network management at Kotak Mahindra Bank. Before Kotak, he spent 9 years at Citibank and served as co-head of operations at Citi Custody India. The founders didn’t learn custody and fund ops from startup blogs. They learned it inside the incumbents they’re now trying to beat.

Traction, fundraising, and competition

The company is live and clearly beyond the early-experiment stage. It serves institutional clients including FPIs, AIFs, mutual funds, insurers, family offices, and algorithmic funds. Dovetail services more than $4.5 billion in assets across Indian and global markets. Its fund administration unit alone manages over $2.2 billion in assets under administration. Its LinkedIn profile shows 51 to 200 employees, with 159 staff profiles listed, headquarters in Mumbai, and a DIFC office in Dubai.

The regulatory footprint is also wider than a standard India-only fintech. The company operates in GIFT City and works with regulated entities in Mauritius, Dubai, and Singapore. IFSCA, SEBI, and the Dubai Financial Services Authority regulate it. That cross-border setup is a selling point because fund managers don’t want one provider for India, another for Dubai, and a third for compliance tracking if they can avoid it.

On fundraising, the details are straightforward: ₹100 crore, or about $10.5 million, in Series A from Elev8 Venture Partners, with a mix of primary and secondary shares. Because it’s Dovetail’s first institutional round, the signal is bigger than the amount. Asset servicing companies don’t usually get this kind of backing unless investors think the underlying compliance, clearing, and fund-ops rails can grow.

Competition is where it gets interesting. The legacy alternative is still the large-bank model that Sampat called out directly. The more obvious independent rivals are global fund administrators like Apex Group and SS&C GlobeOp. Apex secured a GIFT City license in 2021 to offer fund administration services there. SS&C GlobeOp expanded its fund administration business in India with a GIFT City office and compliance capability.

Dovetail’s edge is different. It’s pitching independence and local-market depth. It also has a tighter product loop between operations and software — DCMS for compliance, real-time clearing tools, and its Bridge distribution layer. That won’t automatically beat larger global firms on scale. But it’s a credible argument for managers who want India and offshore support without handing sensitive workflows to a universal bank or a giant global admin platform.

What does Dovetail Capital funding change now?

This round gives Dovetail room to do the expensive stuff. International expansion in fund services isn’t just about sales teams. It means compliance hires, local presence, product hardening, and enough balance-sheet confidence that institutional clients are comfortable handing you critical workflows.

The timing also lines up with a real product opening. Dovetail’s GIFT IFSC platform says it can support third-party fund launches with physical presence in IFSC and a dedicated principal officer. It also offers structures that help global or domestic managers launch funds through its setup. That matters a lot more now that IFSCA has formally enabled third-party fund management services — commonly called platform play — in GIFT City.

And because the round includes secondary transactions too, this wasn’t just a survival raise. It looks more like a validation round for a business that had already built enough depth to attract institutional capital without pretending to be a consumer-facing fintech story.

Why are investors backing asset servicing startups in India?

The market math is getting hard to ignore. India’s asset management market is estimated at $2.7 trillion in 2026 and is projected to reach $5.82 trillion by 2031, growing at a CAGR of 16.59%. In parallel, the mutual fund industry’s average assets under management stood at ₹83.5 lakh crore in May 2026. Commitments to alternative investment funds reached ₹16.9 lakh crore as of March 2026, up 25% year on year.

That growth creates pain. More money means more reporting and more cross-border structures. It also means more derivative exposure and more regulators in the room. That’s where firms like Dovetail make their pitch.

There’s also a GIFT City angle that’s easy to miss if you only read the headline. As of March 31, 2025, IFSCA said the IFSC already had 162 fund management entities, 229 schemes, and 427 direct jobs in fund management. On June 24, 2025, the authority approved a framework for third-party fund management services. The July 2025 amendments let outside managers launch restricted schemes through registered FMEs without building their own physical presence in IFSC.

That’s why this category is heating up. The money is growing. The rules are maturing. And the operational mess behind cross-border investing is finally big enough to support specialist infrastructure companies.

Final take on Dovetail Capital funding

The Dovetail Capital funding round isn’t flashy. That’s the point.

This is a bet on boring-but-crucial financial plumbing — the admin, clearing, compliance, and distribution rails that let capital move without falling apart in 3 jurisdictions at once. If Dovetail can turn its GIFT City strength and founder credibility into a broader global asset servicing franchise, this Series A could end up looking small in hindsight. What to watch next is simple: more international clients, deeper productization, and whether Dovetail can win against both bank incumbents and giant global administrators.

Read how 3one4 Capital and British International Investment launched the $15M IIDEA Fund to back women-led startups, founders from tier II and tier III cities, and early-stage ventures in underserved sectors with long-term follow-on support.

FAQ

• What is the Dovetail Capital funding round?
  Dovetail Capital raised ₹100 crore in a Series A round led by Elev8 Venture Partners. It’s the startup’s first institutional funding round, and the deal includes both fresh capital for the company and secondary share sales for existing holders.
• How does Dovetail Capital work for fund managers and institutional investors?
  Dovetail runs an asset servicing stack that covers fund administration, derivative clearing, investment advisory, and compliance workflows for institutional clients. Its product set includes a real-time clearing platform, a compliance system called DCMS, investor login tools, and a cross-border distribution product called Bridge.
• Who founded Dovetail Capital?
  Dovetail Capital was founded in 2017 by Dev Sampat, Mahesh Shekdar, and Vivek Singhania. All 3 founders came from institutional banking and custody backgrounds, with senior experience across Kotak Mahindra Bank, Citibank, HDFC Bank, and Standard Chartered.
• Is Dovetail Capital a fintech company or a fund administration company?
  It’s both, but the cleaner label is fintech infrastructure for asset servicing. Dovetail sits in the fund administration, clearing, and compliance layer of capital markets, using software and operational systems to serve clients such as FPIs, AIFs, mutual funds, insurers, family offices, and algo funds.

3one4 Capital is a Bengaluru-based early-stage venture firm that backs Indian startups, and it has teamed up with British International Investment to launch the $15 million IIDEA Fund. Venture money in India still tends to bunch up around metro networks, repeat founder patterns, and a familiar set of categories. That leaves a lot of solid companies outside the usual line of sight. Founded in 2016 by Pranav Pai and Siddarth Pai, 3one4 is using IIDEA as a targeted vehicle for women-led ventures, founders from tier II and tier III cities, and startups working in sectors that don’t always get easy seed capital.

What is the IIDEA Fund and how will it work?

The IIDEA Fund is a focused early-stage pool managed by 3one4 Capital with BII as the sole LP. The corpus is $15 million, or roughly ₹141 crore, and the plan is to back about 10 to 15 startups. Initial cheques will be around $500,000, while the rest of the money will be held back for follow-on rounds. Plenty of seed funds can write the first cheque, but fewer can keep supporting a company when the next round gets hard.

Sector-wise, this isn’t a generic “we invest in everything” fund. IIDEA targets energy transition, agriculture, health, deeptech, and manufacturing. The fund prioritizes founder inclusion. Women entrepreneurs, non-metro founders, and businesses tackling developmental gaps sit at the center of the thesis rather than serving as side bets.

For startups, the product here isn’t software. It’s access. 3one4’s model has long been to stay close to portfolio companies through finance, research, platform, governance, and growth support. Nruthya Madappa’s growth and capital development team works on follow-on fundraising, revenue acceleration, M&A, and exits. So the fund is selling a specific promise: first cheque, sector conviction, and hands-on help after the term sheet lands.

Who built the IIDEA Fund and why does 3one4 matter?

The founding story

3one4 Capital started in 2016 with Pranav Pai and Siddarth Pai as co-founders. The firm was built as an early-stage investor with a research-heavy, close-involvement style rather than a spray-and-pray seed model. A decade later, that original bet has turned into a broader venture platform. IIDEA looks like the firm’s clearest attempt yet to carve out a dedicated inclusion-and-impact lane inside that platform.

Why this team has market fit

Pranav Pai runs investments and portfolio construction as founding partner and CIO. He’s led more than 70 seed and venture investments across India and the US, and he studied electrical engineering at Stanford. That helps explain why 3one4 has stayed comfortable with technical categories instead of only chasing consumer hype cycles.

Siddarth Pai brings a different kind of edge. He’s the firm’s founding partner, CFO, and ESG officer, a chartered accountant, and an active policy voice through the Indian Venture Capital Association and startup-related committees. For a fund like IIDEA, that matters. Inclusion-led investing isn’t only about sourcing founders. It also depends on fund structure, compliance discipline, and knowing how regulation affects early-stage capital.

Nruthya Madappa is central to this launch too. She’s a partner and head of growth and capital development at 3one4, and before joining the firm she founded and ran The CoWrks Foundry, investing in more than 25 companies, and also held leadership roles at Cuemath. Her operating mix of fundraising, growth, and portfolio scale-up makes her a believable person to run a thesis that depends on helping overlooked founders cross the gap from “interesting” to institutionally fundable.

Execution track record and early signals

This isn’t a first-time manager asking the market for trust on vibes alone. 3one4 has backed more than 100 startups and logged 26 profitable exits across its first two funds. Last year it partially exited Kuku FM and generated about 90% IRR with a 38.4x MOIC on that investment. The firm’s portfolio includes AGNIT Semiconductor, smallest.ai, Lumio, Licious, Jupiter, Darwinbox, Raise Financial Services, and Eka.Care.

The new fund isn’t waiting around either. 3one4 has already started deploying the IIDEA Fund and has closed nearly 5 investments so far. That matters. A lot of thematic funds spend months talking about intent; this one is already in market.

Fundraising details and where IIDEA sits against rivals

BII has already committed the full corpus as sole LP, so IIDEA launches fully capitalized instead of fundraising in public. That’s happening while 3one4 is also preparing a much larger fifth fund with a likely target of $225 million for broader early-stage bets across AI and SaaS, enterprise and manufacturing automation, fintech, deeptech, and consumer internet. In plain English: IIDEA is the specialist vehicle. Fund V looks more like the flagship.

Competition is real, but it’s fragmented. Saha Fund has long positioned itself around women entrepreneurs. Avaana is one of the better-known names around climate and frontier innovation. Capital-A focuses on manufacturing and climate. It writes first institutional cheques in the ₹5 crore to ₹12 crore range. IIDEA’s angle is different because it combines founder inclusion and sector neglect in one mandate. Not just women, not just climate, not just deeptech, but the overlap where a lot of founders still struggle to get seen.

Why are investors backing the IIDEA Fund now?

A small, tightly scoped fund can do things a flagship fund usually can’t. It can tolerate categories that take longer to mature. It can spend time on founder discovery outside Bengaluru, Mumbai, or Delhi. And it can write conviction cheques in sectors where early data is thinner but the upside is still real.

That’s why this launch matters for 3one4 itself. Instead of forcing every inclusion bet through the same funnel as mainstream venture software deals, the firm now has a dedicated vehicle with a development-finance LP that wants those outcomes. For BII, the logic is just as clear. It gets exposure to Indian startups through a manager it already knows, while pushing capital toward gender, inclusion, health, agriculture, and climate-linked outcomes that fit its broader mandate.

There’s also a timing benefit. Because the fund is already raised and already deploying, 3one4 can test whether this thesis produces stronger pipelines and better follow-on behavior before its next flagship becomes fully active. That’s smart.

What market trends support the IIDEA Fund?

The numbers show why a fund like this can exist. India tech funding grew from $11.9 billion in FY 2018-19 to a peak of $45.8 billion in FY 2021-22 before the market reset, and women co-founded startups in India accounted for about $1.2 billion in funding in FY 2025-26 — roughly 11% of total startup funding. That’s not trivial. But it’s still a narrow share when you remember how big the overall market is.

There’s a second clue in the stage data. Tracxn says seed funding for women co-founded startups dropped from $280 million in FY 2024-25 to $224 million in FY 2025-26, while early-stage funding rose to $657 million. That usually means investors are still writing checks, but they’re being pickier at the earliest stage. Bain’s 2025 India venture report also pointed to a recovery in activity, with deal volumes rising from 880 in 2023 to 1,270 in 2024. So the market is open again — just not evenly open.

BII’s own priorities line up with that imbalance. In its 2026-31 strategy, the institution said it wants 30% of new core investments to qualify under the 2X Challenge and singled out women’s inclusion, quality jobs in manufacturing and services, and women-linked climate resilience as target areas. That makes IIDEA feel less like a one-off press announcement and more like a practical expression of how development finance wants to show up in venture right now.

Will the IIDEA Fund change who gets funded?

Maybe. But let’s not pretend a $15 million vehicle rewrites Indian venture by itself.

What it can do is prove that underrepresented founders and underfunded sectors aren’t charity cases — they’re investable if the fund design matches the reality on the ground. Over the next 12 to 18 months, the thing to watch isn’t just how many startups it backs, but whether those companies attract strong follow-on rounds from the rest of the market.

Read how Kapture CX raised a $10M pre-Series B led by Bajaj Finserv Ventures to expand its AI-powered customer service platform that automates enterprise support across voice, chat, email, and messaging channels.

FAQ

• What is the IIDEA Fund? The IIDEA Fund is a $15 million early-stage fund launched by 3one4 Capital with British International Investment as the sole LP. It’s aimed at startups in energy transition, agriculture, health, deeptech, and manufacturing, with a clear bias toward women-led ventures and founders from tier II and tier III cities.
• How does the IIDEA Fund work for startups? It works like a dedicated first-cheque vehicle with follow-on capacity. 3one4 plans to back around 10 to 15 startups, start with roughly $500,000 per company, and keep reserve capital for later rounds. It also plugs founders into the firm’s research, platform, governance, and growth support functions.
• Who are the founders behind 3one4 Capital? 3one4 Capital was founded in 2016 by Pranav Pai and Siddarth Pai. Pranav leads investing as CIO and comes from an engineering background at Stanford, while Siddarth runs finance and ESG and is a chartered accountant deeply involved in venture policy through IVCA.
• Why is the IIDEA Fund focused on women-led and non-metro startups? Because that’s still where a lot of India’s venture blind spots sit. In FY 2025-26, women co-founded startups took about 11% of total startup funding, and activity remained concentrated in big metro hubs, which leaves room for a fund built around founder inclusion instead of only pattern-matching old networks.

Kapture CX is an enterprise customer service software company that uses agentic AI to run support workflows across voice and digital channels. It has now raised $10 million in a pre-Series B round led by Bajaj Finserv Ventures, with Cactus Partners and India Alternatives also participating. The pitch is simple: big companies want AI in support, but stitching together bots, ticketing tools, analytics, and human handoffs is still messy and expensive. Founded in 2014 by Sheshgiri Kamath and Vikas Garg, the Bengaluru company is trying to sell a more opinionated answer—a verticalized full-stack platform instead of another point tool.

What does Kapture CX actually do?

At the product level, Kapture CX is building a customer experience operating layer where AI agents handle routine requests first. Humans step in for exceptions. Supervisors can monitor the whole thing in real time. Its stack spans voice agents and non-voice agents. It also includes advanced ticketing, agent workspaces, reports and analytics, quality audits, conversational intelligence, self-serve tools, and an observability platform meant to catch errors before they turn into bad customer outcomes.

A typical workflow looks pretty direct. Customer queries come in through phone, email, WhatsApp, chat, or social channels. AI agents try to contain and resolve the issue. The platform pulls context from connected systems. When the case needs judgment, it hands off to a human agent with customer history and suggested next steps already in place. Kapture says those agents can also connect into existing CRMs and ERPs. They can also tap knowledge bases to fetch data, update records, and complete actions instead of just answering questions.

That matters because a lot of support teams still waste time clicking across tabs. Kapture frames the goal as “contain first, execute next, then optimize continuously,” with low-latency voice automation for routine inbound and outbound requests. It also pitches smoother digital automation across email and messaging, plus built-in quality monitoring after the interaction ends. Its observability layer tracks every AI interaction, surfaces intent accuracy and sentiment shifts on live dashboards, and can trigger alerts when flows stall or SLAs are at risk.

It’s also more mature than the new “agentic” label suggests. Kapture has described a single dashboard with 100+ out-of-the-box APIs and 500+ report formats. That hints at where the real work is: not just answering customers, but fitting into enterprise process sprawl without a six-vendor integration project.

Who built Kapture CX and how far has it scaled?

The founding story

Kapture CX was founded in Bengaluru in 2014 by Sheshgiri Kamath and Vikas Garg, but the roots go back earlier. Kamath has said the team first started Adjetter in 2011 as an offline marketing automation business, then pivoted into CRM and support software after seeing how limited legacy systems felt in real operating environments. In another earlier interview, he tied that insight to time spent at via.com, where working across countries exposed how brittle customer systems became once companies scaled beyond a single market.

Why the founders make sense for this category

Kamath’s background is unusually operational for a CX software founder. Older profiles place him in global roles at via.com across India and Southeast Asia, with earlier stints at ITC and the Manipal Group. Kapture later put him in the CEO seat, while Vikas Garg took charge of product leadership as co-founder and CPO. Garg has been described in founder interviews as an IIT Guwahati computer science graduate who originally led technology and ops. That lines up with the company’s product-heavy, customization-first build philosophy.

Pivots, resilience, and traction

That early pivot matters because it says something about execution. Cactus Partners said the founders had already gone through two pivots and built a profitable, steadily growing SaaS business before the firm backed them in 2023. That’s not a small detail. Tons of AI startups are still telling a prototype story. Kapture is selling from an older SaaS base that was already in market before the agentic AI rush started.

Today, the company serves more than 1,000 enterprises across 18 countries. Its customer base includes Bajaj Finance and companies inside the Tata Group and Reliance Group, while its partner network includes consulting firms and hyperscalers. LinkedIn currently lists Kapture CX in the 201–500 employee band. That gives a rough sense of operating scale behind the product.

The fundraising trail

This new $10 million round comes at the pre-Series B stage, and Bajaj Finserv Ventures led it. Before that, Kapture raised $4 million in a Series A round led by Cactus Venture Partners in July 2023, then another $4 million in an extended Series A round led by India Alternatives in December 2023. The new capital is earmarked for global expansion, R&D, and product development.

Where Kapture sits against Salesforce, Decagon, and Sierra

This is where the story gets harder. Kapture isn’t just competing with legacy helpdesk software anymore. It’s up against Salesforce, which is pushing Agentforce as a unified service platform combining AI agents, human reps, and trusted enterprise data. It also faces Sierra, which focuses on customer service AI agents and had raised $110 million by February 2024 before announcing a $350 million round in September 2025. Then there’s Decagon, which raised $250 million in January 2026 at a $4.5 billion valuation to expand AI customer support agents.

Kapture’s answer is specialization. It argues that enterprises don’t want another layer sitting on top of a fragmented stack. They want one system that owns models, agent orchestration, workflow logic, and the UI, then tunes that whole setup for verticals like BFSI, retail, travel, and consumer durables. That’s a real differentiator if it leads to faster deployments and fewer integration headaches. But it also means Kapture has to prove it can beat both giant horizontal suites and better-funded AI-native challengers.

Why does Kapture CX funding matter now?

This round matters because it’s not survival capital. It looks more like expansion capital for a company that already has product depth, enterprise references, and a live install base. Bajaj Finserv Ventures isn’t backing a raw demo here—it’s backing a company trying to turn AI from a support feature into the core operating layer for high-stakes enterprise workflows.

Bajaj’s involvement says something else, too. Kapture already counts Bajaj Finance among its enterprise users, so the investment has a strategic feel beyond pure financial sponsorship. In the same calendar year, Bajaj Finserv Ventures also led an $8.6 million round in NowPurchase and a $25 million round in Assiduus Global. That suggests it’s building a broader thesis around applied enterprise AI rather than making one-off bets.

For customers, the practical question is whether the new money speeds up deployments and deepens industry workflows. If the company uses the capital well, buyers should see better product depth in regulated and high-volume sectors, stronger global implementation muscle, and more mature AI governance. That last part matters a lot more than flashy demos.

How big is the AI customer service market?

The timing is obvious. Grand View Research puts the global AI for customer service market at $13.0 billion in 2024 and projects it to hit $83.9 billion by 2033, which implies a 23.2% CAGR. It also flags Asia Pacific as the fastest-growing region and retail and e-commerce as the fastest-growing end-use segment. Those are useful signals for a company that already leans into vertical deployments.

But growth alone doesn’t mean easy money. Gartner said in January 2026 that generative AI cost per resolution in customer service will exceed offshore human agent costs by 2030, largely because of infrastructure costs and more complex use cases. That’s the part many AI startup decks glide past. Buyers are still interested, sure, but they’re going to ask harder questions about observability, governance, and ROI. That’s why Kapture keeps pushing a tightly controlled full-stack model instead of a loose collection of copilots.

What should you watch next at Kapture CX?

Kapture CX has a credible shot at becoming more than a regional SaaS success story. It has real customers and a fairly clear product thesis. It also has enough funding now to test whether its verticalized agentic AI model travels well outside India.

But this next phase is tougher. The company now has to show that Kapture CX can win global enterprise deals while giants like Salesforce and richly funded startups like Sierra and Decagon crowd the same budget line. The next 12 to 18 months will show whether that pitch holds up.

Read how The Indus Valley raised a $17M Series B led by Gaja Capital to expand its toxin-free cookware brand with new products, stronger omnichannel distribution, and wider retail reach across India.

FAQ

• What is the latest Kapture CX funding round? Kapture CX has raised $10 million in a pre-Series B round announced on June 30, 2026. Bajaj Finserv Ventures led the round, and existing backers Cactus Partners and India Alternatives also joined, with the capital set aside for overseas expansion and continued product work.
• How does Kapture CX work for enterprise support teams? Kapture CX works as an AI-led customer operations layer across voice, chat, email, WhatsApp, and social channels. Its agents can resolve routine issues and pull information from connected business systems. They can hand off tougher cases to humans with full context and give managers live visibility into performance through dashboards, alerts, and quality monitoring.
• Who founded Kapture CX? Kapture CX was founded in 2014 by Sheshgiri Kamath and Vikas Garg. The company’s earlier roots trace back to Adjetter in 2011, and Kamath has linked the original product idea to what the founding team saw while working through cross-border operations at via.com.
• Is Kapture CX a CRM company or an AI customer service platform? It started life closer to a CRM and support automation product, but that description is too narrow now. Kapture CX is better understood as an enterprise AI customer service platform that bundles AI agents and ticketing. It also includes analytics, observability, and industry-specific workflow orchestration in one stack.

The Indus Valley is a Chennai-based cookware brand that sells toxin-free, non-coated kitchen products for Indian homes. It has now raised $17 million in Series B funding, led by Gaja Capital, at a time when buyers are getting a lot more skeptical about what actually touches their food. Founded in 2016 by Jagadeesh Kumar and Madhumitha Uday Kumar, the company is trying to turn that anxiety into a scaled consumer business. It’s already operating at a ₹200 crore annual revenue run rate. That makes this more than a niche wellness pitch.

What is The Indus Valley and how does it work?

The Indus Valley isn’t a kitchen gadget app or a smart appliance play. It’s a direct-to-consumer cookware brand built around non-coated materials such as cast iron, sheet iron, tri-ply stainless steel, tri-steel stainless steel, and pressure cookers. Shoppers don’t just browse “cookware.” They buy by use case — tawa, kadai, fry pan, biryani pot, saucepan, grill pan, paniyaram pan, appam pan, puttu maker, and more. That sounds basic, but in Indian kitchens it matters because cooking behavior is still deeply dish-specific.

Its product design leans hard into material-first positioning. The cast iron range is pre-seasoned. The stainless steel line includes tri-ply builds with 3-layer thick bodies and heavy bottoms. Pressure cookers such as the RapidCuk series come in multiple capacities for different household sizes. A lot of the range works with gas and induction. That matters.

The buying flow is pretty simple. You pick the material you trust, then the format you need. Then comes the size and heat-source compatibility. The brand also sells through its own site, ecommerce marketplaces, quick commerce channels, and offline retail stores, so it isn’t relying on one sales pipe the way many D2C brands do in their early years.

What manual work does it remove? Mostly the confusing part. Instead of asking buyers to decode vague “healthy cookware” claims, it organizes products around clear materials and cooking needs. It backs that up with care guides, recipes, combo sets, and category education. That’s smart, because non-coated cookware only wins if customers know how to use and maintain it without giving up after the first sticky dosa.

Who founded The Indus Valley and what has it built?

The founding story

The company began after a kitchen mishap. In 2015, a plastic cookware incident in an oven pushed Madhumitha Uday Kumar and Jagadeesh Kumar to look for healthier alternatives, and what they found was messy: options were either unreliable, hard to trust, or just not easily available. That frustration became the starting point for The Indus Valley, which was formally founded in 2016 in Chennai.

Why the founders had category fit

This wasn’t a random jump into cookware. Madhumitha came from consulting, with prior experience at Deloitte US-India and a PGDM from KJ Somaiya. Jagadeesh brought consumer and distribution exposure from roles at Healthkart and Linde India, along with a PGDM from IIM Raipur and an engineering background. That mix is useful here. One side understands structured execution. The other understands selling physical consumer products.

That’s probably why the company doesn’t read like a trend-chasing wellness brand. It reads like a business built by operators who understood early that cookware isn’t bought once through a pretty Instagram ad. You need repeat demand, reliable supply, and enough category education to convince people to switch habits in the kitchen.

Traction, reach, and early proof points

The brand is very much live and scaled, not experimental. It sells through its own website, online marketplaces, quick commerce channels, and offline stores. Across that footprint, it has served more than 20 lakh customers, reached 15,000-plus pin codes, and built a catalog of 300-plus products.

There are also a few strong brand signals. The Indus Valley appeared in Inc42’s FAST 42 list for 2025, and its brand pages highlight recognition such as India’s 9th fastest-growing D2C brand in the growth category, plus earlier awards from FICCI and TiE Con Chennai. None of that guarantees durability. But it shows the company has moved past the “interesting niche startup” phase.

On the numbers that really matter, the company has reached an ARR of ₹200 crore and wants to cross ₹1,000 crore ARR by 2030. That’s ambitious. Very ambitious. But it also explains why this round is bigger and why a firm like Gaja Capital would care now.

The funding round and what comes with it

On June 30, 2026, The Indus Valley announced a $17 million Series B round led by Gaja Capital, with participation from existing investors DSG Consumer Partners and Rukam Capital. The company plans to use the new money for product innovation and stronger omnichannel distribution. It also wants deeper brand presence. Including this round, total funding now stands at about $21.8 million.

How it stacks up against rivals

The direct competition is getting more crowded. Cumin Co has been pushing enamel-led healthy cookware and raised $5 million in a pre-Series A round earlier in 2026. Ember has taken a different route with premium clean cookware built around its Arcilla ceramic coating and raised $3.2 million. Wonderchef sits in a broader kitchen products lane, while newer smart-kitchen companies are focused on connected appliances rather than bare cookware.

The Indus Valley’s edge is pretty clear. It isn’t trying to sell “tech” to justify the price. It’s selling material trust and a broad catalog across cooking formats. Its distribution now stretches from D2C to quick commerce to physical retail. In other words, it’s betting that the safer-cookware customer doesn’t just want one hero pan — they want a full kitchen migration.

Why does The Indus Valley funding matter?

This round matters because cookware isn’t a capital-light category forever. Once a brand wants better inventory depth, new materials, offline expansion, and stronger recall, the bill goes up fast. So a $17 million raise here isn’t vanity capital. It’s scale capital.

Gaja Capital’s entry also says something about investor confidence. DSG Consumer Partners has backed the company before and is staying in, which usually signals belief in execution, not just a good story. The company already has real revenue and distribution. The fresh money is likely being used to widen the moat rather than simply prove that demand exists.

For customers, the practical implication is simple. Expect more categories and more retail visibility. There will probably also be more effort to make healthier cookware feel mainstream instead of specialist.

How big is India’s kitchenware market in 2033?

The broader market is big enough to justify the chase. India’s kitchenware market was estimated at $5.23 billion in 2024 and is projected to reach $10.89 billion by 2033, growing at an 8.5% CAGR from 2025 to 2033. Within that, cookware is expected to be one of the faster-growing categories, helped by rising health awareness, more online buying, and a willingness to pay for safer materials and better aesthetics at home.

That trend lines up neatly with what The Indus Valley is selling. Buyers are increasingly questioning coated surfaces, cheap alloys, and low-trust manufacturing. At the same time, distribution has changed. Ecommerce, quick commerce, and digital brand-building make it easier for a specialist consumer brand to reach national demand without waiting years for old-school retail.

There’s another shift too. Kitchen spending in India is splitting into two different stories. One is healthier materials, where The Indus Valley, Cumin Co, and Ember are all competing. The other is smarter kitchens, where appliance-led names like Wonderchef and upliance.ai are trying to automate cooking itself. Different bets. Same wallet.

Can The Indus Valley hit its 2030 target?

This round doesn’t feel speculative. The Indus Valley already has scale, a recognizable thesis, and a product category people can understand in 5 seconds: safer cookware. That’s powerful.

But the next stretch is harder. Brand trust is easy to lose in kitchenware, and offline expansion can expose quality gaps fast. If The Indus Valley can turn this $17 million into sharper products and broader distribution without watering down what made people care in the first place, then the ₹1,000 crore ARR goal stops sounding wild and starts sounding like a hard, but real, possibility.

Read how Copperlane raised a $4.1M seed led by TQ Ventures to automate mortgage origination with an AI-powered platform that helps lenders process applications, verify documents, and streamline loan approvals through its AI assistant, Penny.

FAQ

• What funding did The Indus Valley raise? The Indus Valley raised $17 million in a Series B round announced on June 30, 2026. Gaja Capital led the round, and existing investors DSG Consumer Partners and Rukam Capital also participated; the company said the capital will go into product innovation, omnichannel distribution, and stronger brand presence.
• What does The Indus Valley sell? It sells toxin-free, non-coated cookware and kitchenware across cast iron, iron, stainless steel, triply cookware, and pressure cookers. The catalog spans everyday Indian cooking formats like tawa, kadai, fry pans, saucepans, biryani pots, appam pans, and pressure cookers, with products sold online, through quick commerce, and in offline stores.
• Who founded The Indus Valley? The company was founded in 2016 by Jagadeesh Kumar and Madhumitha Uday Kumar. Before starting the business, Madhumitha worked at Deloitte US-India, while Jagadeesh held roles at Healthkart and Linde India, giving the founding team a mix of consulting, sales, and consumer-brand operating experience.
• Is The Indus Valley part of the cookware market or the smart kitchen market? It sits squarely in the cookware market, not the AI appliance category. That still puts it inside a large and growing market: India’s kitchenware market was valued at $5.23 billion in 2024 and is projected to reach $10.89 billion by 2033, which is why both healthy cookware brands and smart kitchen players are attracting capital right now.

Copperlane builds software for AI mortgage origination, and the startup has raised $4.1 million in seed funding led by TQ Ventures. The pitch is simple: mortgage lenders still burn a lot of time and money chasing paperwork, re-checking documents, and cleaning up messy files before a loan gets approved. Founded in 2025 by Athan Zhang and Brianna Lin, Copperlane wants to move that grunt work onto an AI agent called Penny. Both founders grew up in mortgage families, so this isn’t some random fintech idea pulled from a trend deck.

What is Copperlane’s AI mortgage origination platform?

Copperlane is an AI-native mortgage origination platform built around Penny, an assistant that handles the front half of the loan process like a junior loan officer or loan officer assistant. In practice, Penny guides borrowers through the application and reads uploaded documents. It checks eligibility, spots missing items or conflicting information, and hands a more complete file to the human team.

The product looks more operational than flashy. Borrowers upload W-2s, bank statements, and other documents. Penny extracts details and pre-fills parts of the application. It also adapts the form based on what kind of borrower it’s dealing with. If someone has only W-2 income, it can hide self-employment questions. If the loan is conventional, it can skip VA-specific sections. That matters because mortgage applications get confusing fast. Then deals fall apart.

It also does the file-cleanup work that usually eats hours. Penny verifies document authenticity and checks whether the account holder matches. It flags issues like a large deposit or an employment gap, then asks follow-up questions before an underwriter has to. It can even draft a letter of explanation when a lender needs one for compliance or file support.

And the workflow isn’t limited to the borrower portal. Penny can answer staff questions inside Slack or Teams. It can take action through text, call, or email, and create internal tickets to keep a file moving. Copperlane also built multilingual voice support, with Penny able to speak to borrowers in 13 languages, including English, Mandarin, and Spanish. Before this, a lender’s process might involve a loan officer, a processor, a string of emails, and a lot of tab-hopping. Copperlane wants to replace that with one dashboard, fewer surprises, and less time spent playing detective.

Who founded Copperlane and why start it?

The founding story started with the secondary market

Copperlane traces back to a conversation Zhang had with his mother, who spent her career on the risk side of the secondary mortgage market. She described how, by the time a loan reaches investors, a complex borrower story has often been flattened into a spreadsheet. Zhang saw that as a data problem more than a paperwork problem.

That idea stuck.

He later teamed up with Brianna Lin through Y Combinator, where both had arrived separately before joining forces. They clicked in part because they shared the same kind of background: both describe themselves as coming from “mortgage families,” with parents whose careers touched Freddie Mac, Fannie Mae, and the Federal Housing Finance Agency.

Why these founders think they fit the job

Zhang is the CEO. He studied computer science at Princeton and worked as a quantitative developer before starting Copperlane. He’s also been on two startup founding teams, which helps explain why the product feels built around workflow, not just model demos.

Lin is the COO. She studied computer science and finance through Penn’s M&T program and worked in trading and investing. She has also said she founded a private-equity startup before Copperlane and served as the first hire at an earlier startup. That’s not the same as 20 years inside mortgage ops, and the founders are open about that. Their edge is different. They’re technical, they know how painful the process feels from inside mortgage households, and they’re embedding with lenders instead of pretending an outside AI team can wing it.

Early traction, fundraising, and what’s been disclosed

Copperlane is early. Really early.

The company is part of Y Combinator’s Winter 2026 batch and still has a listed team size of 2. But it isn’t pre-product. Penny is already live enough to demo end-to-end borrower intake, document review, internal workflow actions, and multilingual support. Copperlane hasn’t shared how many lenders use the product or how much loan volume runs through it, which means outsiders still can’t judge traction the way they can with a more mature fintech.

The fundraising details are clearer. Copperlane announced a $4.1 million seed round this month, led by TQ Ventures. Other backers include Y Combinator, US News Digital Ventures, Eight Capital, and angel investors tied to Mercor and others. The founders say much of that money will go into engineering and safety work around Penny. The rest will go into the ugly but necessary integration layer with lenders’ existing systems.

Who Copperlane is competing with

Copperlane isn’t entering an empty category. Its first competition is the old stack: legacy loan origination systems, manual processors, email chains, outsourced fulfillment teams, and human loan officers doing repetitive review work by hand. That’s still the real incumbent.

Then there’s the newer wave. Tidalwave is pushing AI into mortgage point-of-sale and automation. Maestro AI is building an agentic operating layer for mortgage workflows on top of existing systems. Other lenders and tech vendors are also trying to bolt AI onto origination, underwriting, or borrower support.

Copperlane’s angle is narrower and sharper. It wants Penny to feel less like a feature and more like an employee. The company positions the tool as either a copilot or, for lenders willing to go further, an autopilot for intake and early file prep. Investors are betting on that focus on borrower back-and-forth, document context, and pre-underwriting cleanup.

Why are investors backing AI mortgage origination now?

Because the mortgage business doesn’t need another shiny dashboard. It needs labor relief.

The most credible part of Copperlane’s pitch is that it targets work lenders already hate paying for: document chasing, repetitive borrower follow-up, and the same clarifications on every file. If Penny turns a 4-hour review into minutes, or even cuts that time by half, the economics get interesting fast for lenders that have spent years trying to survive thin margins.

There’s also a timing argument here. TQ Ventures’ Schuster Tanger said the industry has been waiting for software that “thinks through the complexity of a loan,” and that’s a sharp way to frame the bet. Plenty of mortgage tech has digitized forms. Less of it has tried to reason through borrower context in plain English, ask for missing items, and build an explainable file story before underwriting.

For customers, this round matters if it helps Copperlane become less of a demo and more of a dependable workflow system. That means integrations. Governance. Reliability. Lin argued that better lender tools “directly translates into a better experience for borrowers,” and that’s true only if the product keeps files cleaner without creating new compliance headaches.

How big is the AI mortgage origination market?

The software category is still forming, but the underlying mortgage machine is enormous. The Mortgage Bankers Association forecast total U.S. single-family mortgage originations at $2.2 trillion in 2026, up from $2.0 trillion in 2025. That’s the pool of activity startups like Copperlane are trying to skim efficiency from.

And lenders need the help. Independent mortgage banks posted an average loss of $1,056 per loan in 2023 before recovering to a slim average profit of $443 per loan in 2024. Production expenses alone still averaged $11,076 per loan in 2024. Smaller lenders stayed under pressure, and the fourth quarter of 2024 slipped back into a loss.

That’s why this category suddenly looks more urgent than experimental. When volumes are weak and margins are fragile, lenders stop treating automation as a nice upgrade and start treating it as survival math.

Can AI mortgage origination survive the compliance test?

This is where the hype hits a wall.

Mortgage lending is one of the worst places to be sloppy with AI. The Consumer Financial Protection Bureau has already made clear that there’s no advanced-technology pass for consumer finance law. If an applicant gets denied, the lender still has to explain why in a specific and legally usable way. Regulators including the Federal Reserve and the Office of the Comptroller of the Currency are also asking harder questions about vendor controls, oversight, and where humans stay in the loop.

And there’s recent precedent. In 2025, the Massachusetts attorney general settled a fair-lending case centered on an AI underwriting model. That was a useful reminder that when AI touches credit decisions, liability doesn’t magically stick to the software vendor. It lands on the lender.

That doesn’t kill Copperlane’s case. It just raises the bar. If Penny stays focused on intake, verification, follow-up, and file organization — and if every action is reviewable — Copperlane has room to grow. But if the product drifts into black-box decisioning without clean controls, the compliance story gets ugly fast.

Copperlane’s shot in AI mortgage origination is real because the pain is real. But this company won’t be judged by how futuristic Penny sounds. It’ll be judged by boring stuff — cleaner files, faster closings, fewer defects, and whether lenders trust it enough to keep it in the loop when the market gets busy again.

Read how Aseon raised $10M in seed funding led by Crane Venture Partners to build robotic service pods that charge, clean, inspect, and maintain robotaxis closer to where autonomous fleets operate.

FAQ

• What funding did Copperlane raise? Copperlane raised a $4.1 million seed round in June 2026. TQ Ventures led the round, and the investor list also included Y Combinator, US News Digital Ventures, Eight Capital, and angels connected to Mercor.
• How does Copperlane’s product actually work? Copperlane uses an AI agent called Penny to handle borrower intake and early file preparation in the mortgage process. Penny reads documents and adapts the application flow. It flags issues like missing paperwork or suspicious deposits, follows up with borrowers, and prepares organized files before they reach underwriting.
• Who are Copperlane’s founders? Copperlane was founded in 2025 by Athan Zhang and Brianna Lin, who are both 21 and met through Y Combinator. Zhang studied computer science at Princeton and worked as a quant developer, while Lin studied computer science and finance at Penn and worked in trading and investing.
• Is Copperlane a mortgage lender or a mortgage software company? Copperlane is a mortgage software company, not a lender. It sells workflow automation for mortgage origination, which puts it in the mortgage tech and lending infrastructure category rather than the direct home-loan market.

Aseon Labs builds robotaxi infrastructure that cleans, charges, and inspects self-driving cars in compact service pods placed close to where fleets operate. The Redwood City startup has raised a $10 million seed round led by Crane Venture Partners because empty miles to distant depots are still one of the biggest reasons robotaxi economics look shaky. Founded in 2026 by George Kalligeros and Dan Keene, Aseon is betting that if autonomous cars need to stay on the road all day, the service layer has to move into the city with them.

What is Aseon and how does the Aseon funding-backed infrastructure work?

Aseon’s robotaxi infrastructure is basically a robotic pit stop for autonomous fleets. A vehicle drives into a pod on its own and gets plugged into charging equipment by robotic hardware. It goes through inspection and cleaning, then leaves ready for more trips without heading back to a large depot on the edge of town.

The workflow is more detailed than the one-line pitch suggests. Inside the pod, the system can wash the vehicle and calibrate sensors. It can also transmit data back to the fleet operator, remove trash, and identify and recover lost items left by riders. Kalligeros has described the pod’s perception stack as advanced enough to grade vehicle cleanliness and distinguish between different objects inside the cabin.

That matters because a lot of the grunt work around autonomous fleets is still painfully manual. Aseon is trying to remove the human steps involved in plugging cars in and staging them for checks. It also wants to handle basic interior resets and cut the need to shuttle vehicles across town to centralized facilities. Its pods are designed to integrate with existing charging networks instead of requiring every fleet to build a giant purpose-built site from scratch.

Aseon also isn’t pretending robots should handle every mess. The company uses computer vision and vision-language-action models to decide when the pod should back off — like a melted chocolate stain that could get worse if a robot tries to scrub it. Early deployments will still have staff involved. The units can run on mobile power such as propane generators or plug into existing power through charging partners.

Who founded Aseon before the Aseon funding round?

The founding story behind Aseon funding

Aseon Labs was founded in 2026 by George Kalligeros, the company’s CEO, and Dan Keene, its COO. The pair came into autonomy from the infrastructure side, not from years spent building self-driving stacks. That’s the point: they saw that robotaxis may be learning to drive faster than the industry is learning to service them.

Their thesis came from visiting autonomous vehicle depots and seeing how much real estate, labor, and vehicle downtime those sites consume. Most of those depots sit outside city centers because land is cheaper there, but that pushes fleets into long empty trips for charging, cleaning, and inspections. Kalligeros has argued that self-driving services only get to ride-hailing parity if cars stay in “continuous operation” for as much of the day as possible.

Why these founders fit the job

Kalligeros has the more obvious hardware résumé. Before Aseon, he worked as a mechanical design engineer at Bentley Motors and Tesla, then moved into startup building with Pushme. Keene brought the operating and commercial side. Together, they already had a playbook for deploying physical mobility infrastructure across dense urban markets.

That earlier company matters here. Kalligeros and Keene co-founded Pushme in 2016 to build battery-swapping infrastructure for micromobility fleets, and Tier Mobility acquired the business in January 2020. The founders previously built and scaled Pushme to 5,000 stations across 40 cities before the acquisition. That’s about as direct a proof point as you can get for a startup whose challenge is half robotics and half real estate rollout.

Early signals, traction, and the seed round

Aseon is still early. It hasn’t signed contracts with robotaxi operators yet, but Kalligeros says interest is broad, and the company is using this seed round to get real hardware into the field. The plan is to build 5 prototypes, expand its robotics and engineering team from 6 people to roughly a dozen, and lock down the real estate needed for a distributed network.

The financing is solid for a hardware-heavy mobility startup. Crane Venture Partners led Aseon’s $10 million seed round, with Y Combinator, Expa, Robin Hood Ventures, and Founders Capital also participating. Angel backers include Adrian Aoun, Immad Akhund, Rajat Suri, and operators or founding team members from Anthropic, Nuro, Turo, and Revolut.

How Aseon is positioning itself against rivals

Aseon isn’t alone in thinking the infrastructure layer is the bottleneck. Joule Labs is building autonomous fleet charging infrastructure for robotaxis and EV fleets. Its AURA system covers charging, inspection, cleaning, and data synchronization across distributed service sites. Rocsys is taking a narrower but serious approach with hands-free charging hardware and software built for 24/7 autonomous operations, including robotaxi deployments.

Aseon’s pitch is more city-first and smaller-footprint than the typical depot model. Its pods are designed to fit into a single parking space, qualify as temporary structures, and move if a location underperforms. That gives it a different angle from the big centralized depot approach. It also sets Aseon apart from automated charging vendors that still assume cleaning and inspection will happen somewhere else.

Why does this robotaxi infrastructure round matter?

This round matters because Aseon is trying to prove something much tougher than a software demo. The company has to show that robotic servicing can work in the messiness of real cities — with permitting quirks, uneven power access, shifting fleet demand, and all the random cabin disasters that riders leave behind. Seed money in a pure software company buys product cycles. Here, it buys hardware prototypes, locations, and operational evidence.

There’s also a clear investor thesis under the surface. If robotaxi operators don’t want to own every piece of this service layer themselves, the winner could become a picks-and-shovels supplier to multiple fleets. That’s a more interesting business than being just another charging site operator. Especially if Aseon can prove its pods cut reset costs, shrink downtime, and keep vehicles closer to paying demand throughout the day. Fast Company reported Aseon’s estimate that this model can reduce reset costs by 50%, cut downtime by 65%, and lift per-vehicle revenue by more than $50,000 a year.

The timing lines up. Aseon is getting funded before the robotaxi market fully scales, which means it has a shot to become part of the default operating stack rather than a retrofit vendor called in later. That’s probably what Crane and the rest are backing here.

How big is the robotaxi market getting?

The market behind this bet is no longer tiny. Goldman Sachs Research projects the global robotaxi market will reach about $415 billion in 2035, with the U.S. alone at $48 billion. The same forecast says the global commercial robotaxi fleet could grow from roughly 7,000 vehicles last year to 1 million in 2030 and about 6 million in 2035.

That kind of expansion changes what counts as core infrastructure. When fleets are small, companies can absorb awkward manual resets and oversized depots. When fleets start pushing toward citywide density, labor-heavy charging and cleaning workflows stop looking like an inconvenience and start looking like a tax on the whole model. Goldman also expects some operators to be in 15 or more cities by the end of 2026.

You can already see the industry bending that way. Rocsys is rolling out automated charging for autonomous mobility, and Joule Labs is building full unattended service environments around the same logic: autonomous vehicles need autonomous support systems. Aseon’s bet is that the winning infrastructure won’t just live in giant depots — it’ll be sprinkled through the urban core, a lot closer to where rides begin and end.

The bet on robotaxi infrastructure

Aseon still has a lot to prove. It has no signed fleet contracts yet, the first versions will need human help, and hardware plus real estate is never an easy startup combo.

The idea is sharper than a lot of mobility pitches. Robotaxi infrastructure isn’t glamorous, yet it’s the kind of ugly operational layer that can decide whether autonomous fleets become a real business or stay an expensive demo. The next thing to watch is simple: whether Aseon’s first 5 pods can turn a convincing prototype into repeatable city deployments.

Read how Proception raised $11M in seed funding led by First Round Capital to build dexterous robotic hands that help humanoid robots perform human-like manipulation with advanced tactile sensing and AI-powered training data.

FAQ

• What funding did Aseon Labs raise? Aseon Labs raised a $10 million seed round in June 2026. Crane Venture Partners led the financing, and the round also included Y Combinator, Expa, Robin Hood Ventures, Founders Capital, and a long list of mobility and tech angels.
• How does Aseon’s robotaxi infrastructure actually work? It works like a compact automated service bay for autonomous vehicles. A robotaxi pulls into the pod and gets charged. It’s then cleaned, inspected, and checked for lost items, with machine vision helping the system decide what it can safely handle and what still needs a human-run depot.
• Who are the founders of Aseon Labs? Aseon was founded by George Kalligeros and Dan Keene in 2026. Before this, they built Pushme, a micromobility battery-swapping infrastructure company started in 2016 and acquired by Tier Mobility in January 2020, giving them unusually relevant experience in scaling physical urban infrastructure.
• Is Aseon Labs a robotaxi company or an infrastructure company? It’s an infrastructure company, not a robotaxi operator. Aseon is selling the operational layer around autonomous fleets — charging, cleaning, inspection, and fleet reset hardware. That puts it in the same broad category as autonomous charging and depot automation players rather than companies building the driving system itself.

Proception is a Mountain View startup building dexterous robotic hands for humanoid robots. On June 29, 2026, the robotic hand startup said it had raised an $11 million seed round led by First Round Capital, with Y Combinator and BoxGroup also participating. The pitch is pretty clear: humanoid robots have made real progress in movement and perception, but fine hand control is still where a lot of them break down. Jay Li founded Proception in 2024 with Jack Xu after work on Tesla’s Optimus program convinced them that dexterous manipulation is still the hardest unsolved piece in the stack.

That’s why this round matters more than the dollar amount suggests. Proception isn’t trying to build a full humanoid first. It’s starting with the part nearly everybody admits is brutal: the hand. Li also had to fight off a trade-secret lawsuit from Tesla that was settled in June 2026, clearing a big cloud over the company.

What is Proception and how does the Proception funding-backed technology work?

Proception’s product is ProHand 1.0, a research-grade robotic hand meant for robotics companies and labs that need human-like manipulation rather than a simple two-finger gripper. The hand is built around 22 degrees of freedom and multiple joints per finger. It uses tendon-driven actuation and skin-like tactile sensors, so it can do contact-rich tasks that usually defeat basic end effectors.

Here’s the interesting part. Proception isn’t only selling hardware. It also built ProGlove, a wearable data-capture glove that uses the same sensor-skin idea as the hand itself. A person wears the glove and a headset, manipulates real objects directly, and captures touch plus motion data without needing a robot in the loop first. That means customers can collect training data from human hands before they even spin up a robot fleet.

That workflow fixes two headaches at once. It avoids the scaling limits of classic teleoperation, where data collection is bottlenecked by however many robots you own. It also avoids losing subtle human contact signals — grip shifts, pressure changes, tiny recovery moves — that often disappear when an operator controls a robot remotely instead of touching the object directly.

Proception says ProHand still supports standard teleoperation setups out of the box, so it’s not asking customers to throw away existing robotics workflows. But the company’s roadmap shows a bigger ambition: scale manufacturing and launch a data platform in 2026. Then build a full humanoid prototype in 2027 and reach market deployment in 2028 with cloud-based task planning and analytics layered on top. That’s a long roadmap for a young company. It’s also a much bigger swing than “we made a nice robot hand.”

Who founded Proception before the Proception funding round?

The founding story behind Proception funding

Proception was founded in 2024 by Jay Li and Jack Xu, both alumni of Tesla’s Optimus humanoid robot effort. Li had been a technical lead on Optimus, and Tesla later accused him of taking trade secrets to start Proception. Tesla filed that lawsuit in 2025 and dismissed it in June 2026 after a settlement. Li’s take on the whole episode was blunt: it was a “resilience test” and a pressure test for the company.

The startup is now active, part of Y Combinator’s Winter 2025 batch, and shipping its first batch of hands to researchers and robotics companies. That timing matters. Proception didn’t come out of stealth with a vague demo reel. It came out with hardware on the way to early users.

Founder market fit

Li looks like the kind of founder deep-tech investors usually want for a problem this specific. He’s a Stanford alum and worked on humanoid robotics at Tesla. He’s also been tied to earlier hardware roles at Apple, Aurora, and Aeva. That mix matters because dexterous manipulation isn’t just a robotics problem. It’s a brutal hardware reliability problem too.

Xu brings a similar operator profile. He’s a University of Waterloo alum who worked at Tesla on Optimus and vehicle systems. Earlier, he built medical exoskeletons at Trexo Robotics. His bio also points to autonomous racing work at Waterloo, which is a pretty good signal that he’s comfortable in messy robotic systems instead of just simulations.

Traction and fundraising

On the disclosed numbers, Proception is still small. Y Combinator lists the team at 10 people. But small is normal here, especially for a startup trying to build both electromechanical hardware and a data engine at the same time. The product is live enough that the first batch is shipping now, and the company has opened up broader orders for ProHand.

First Round Capital led the seed round, with Y Combinator and BoxGroup also in. Bill Trenchard from First Round said the firm backed Proception because it thinks the startup may have the best hand in market today, plus the data and models to support it. That’s basically the whole thesis in one sentence: not just a hand, but a learning system attached to a hand.

Competition and market positioning

There are already serious players here. Shadow Robot has spent decades selling dexterous hands and teleoperation systems, with 20 degrees of freedom, 24 movements, and 120 sensors in its current setup. Sanctuary AI is building tactile, high-degree-of-freedom hands inside its broader physical-AI platform. Persona AI is taking another route, commercializing NASA robotic hand IP for heavy industrial humanoids.

So where does Proception fit? Right now, it looks more like a picks-and-shovels supplier than a full-stack humanoid vendor. That’s smart. Lots of robotics companies want better hands and better manipulation data, but they don’t want to sink years into building both from scratch. Proception’s edge is that it’s selling the hand while also trying to own the data-collection layer that teaches the hand what to do. If that works, it becomes harder to swap out than a plain hardware vendor.

Why are investors backing this robotic hand startup now?

Because the legal risk got smaller and the technical wedge got clearer.

A settlement with Tesla doesn’t prove the technology works, but it does remove an obvious diligence problem for investors, partners, and early customers. That alone can change a startup’s trajectory. Buyers don’t love betting on a component supplier that might get tied up in court for another year.

Then there’s the product logic. Proception isn’t chasing general-purpose humanoids from day 1. It’s focusing on one subsystem that nearly every humanoid builder still struggles with. Elon Musk has said robot hands are among the hardest engineering problems still unsolved, and outside Tesla the consensus has been similar: useful human-like robotic hands are still years away. Proception is telling investors it can compress that timeline by pairing better hardware with scalable data capture. That’s ambitious. But it’s at least a focused ambition.

The round also gives Proception room to do the unglamorous stuff hardware startups always need: expand the team and scale production. It also needs to keep building the sensing and data infrastructure behind the hand. If the company only had a clever demo, this raise wouldn’t mean much. What makes it interesting is that the cash is tied to a very specific path: ship hands, gather data, improve manipulation, then climb toward a fuller humanoid platform.

How big is the humanoid robotics market for dexterous hands?

Goldman Sachs Research has projected the humanoid robot market could reach $38 billion by 2035, up sharply from its earlier $6 billion forecast. It also raised its shipment estimate to 1.4 million units by 2035 and said manufacturing costs for humanoids had already dropped about 40%, helped by cheaper components and improving supply chains.

That doesn’t mean every humanoid startup wins. Far from it. But it does explain why investors are willing to fund narrower component plays inside the category. Goldman’s own research says many hardware building blocks — cameras, motors, force sensors, batteries — are close to commercial readiness, while manipulation and interaction remain bottlenecks. If that’s true, then a company obsessed with hands and touch data isn’t early to the market. It may be exactly where the bottleneck is moving.

That’s also why Proception’s timing makes sense. Humanoid robotics is no longer just a moonshot story about flashy demos. It’s becoming a supply-chain story and a data story. It’s also a unit-economics story. A startup that can make dexterous manipulation easier for everyone else could matter even if it never becomes the humanoid brand consumers recognize.

What should you watch next from this robotic hand startup?

The next real test isn’t whether Proception can raise money. It just did that. The test is whether researchers and robotics companies actually adopt ProHand as a default building block instead of treating it like another cool lab device. That’s a much higher bar.

If the company can turn its glove-plus-hand loop into a reliable source of training data, it could become one of the more interesting infrastructure bets in humanoid robotics. Jay Li floated an even wilder signal: Tesla or another big humanoid builder showing up as a customer.

Read how Pocket raised $11M led by Accel to expand its AI-powered meeting recorder that captures offline conversations and turns them into searchable transcripts, summaries, action items, and enterprise-ready workflows.

FAQ

• What funding did Proception raise?
  Proception raised an $11 million seed round announced on June 29, 2026. First Round Capital led the deal, and Y Combinator plus BoxGroup also participated, giving the company fresh capital to expand production and its manipulation-data stack.
• How does Proception’s robotic hand work?
  Proception’s system combines ProHand 1.0 with a wearable ProGlove. The hand uses 22 degrees of freedom and tendon-driven actuation. It also uses tactile sensing, while the glove captures human touch and motion data directly so customers can train manipulation models without relying only on robot teleoperation.
• Who founded Proception?
  Proception was founded in 2024 by Jay Li and Jack Xu, both of whom previously worked on Tesla robotics. Li came out of the Optimus effort and studied at Stanford, while Xu is a University of Waterloo alum who also worked at Trexo before Tesla.
• Is dexterous robotic hands a big market?
  Yes — but mostly because they sit inside the much larger humanoid robotics buildout. Goldman Sachs Research has estimated the humanoid robot market could reach $38 billion by 2035, and when manipulation is still one of the main unsolved technical problems, companies focused on hands can become critical suppliers rather than side bets.