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GobbleCube builds software that helps consumer brands find revenue leaks and act faster across ecommerce and quick-commerce channels. The GobbleCube AI platform has raised $15 million in Series A funding, led by Susquehanna Venture Capital, at a moment when brands are drowning in fragmented marketplace data and wasting money because sales, pricing, inventory, and media decisions still sit in separate tools. Founded in 2022 by former Blinkit executives Manas Gupta, Srikumar Nair, and Nitesh Jindal, the startup is trying to turn that mess into an answer-first operating layer for brand teams. The fresh capital will go into stronger AI, international expansion, and hiring as the company pushes deeper into the US, China, and Southeast Asia.

What is the GobbleCube AI platform and how does it work?

The GobbleCube AI platform pulls together marketplace sales, stock-on-hand data, purchase orders, invoicing, discounts, visibility spends, search ranking, competitor pricing, and availability data. It maps all of that into one consistent layer across channels and locations. Its attribution and prioritization logic then tries to answer a much harder question than a normal dashboard does: what exactly is hurting sales right now, who needs to act, and which action matters first. That’s the real pitch. Not more charts. Fewer dead ends.

The product is now split into clear modules. Gobbs Edge tracks what’s driving or dragging sales through pricing, visibility, and availability signals. Gobbs Boost handles goal-based campaign automation. It adapts to stock, competition, and performance in real time. Gobbs Flow follows the availability chain from depot to dark store. Gobbs Discover looks for micro-category trends and competitor moves that can shape launches and assortment bets.

For customers, a lot of ugly manual work disappears. Brand teams no longer have to reconcile different SKU names from Amazon, Blinkit, Zepto, or Flipkart. They don’t have to patch together email attachments and exports, then spend hours figuring out whether a sales dip came from a stockout, a listing issue, bad discounting, or wasted ad spend. In GobbleCube’s customer stories, teams end up working from one daily view of dark-store penetration, stock levels, days of inventory, share of voice, and hyperlocal demand signals instead of bouncing between spreadsheets and platform dashboards.

There’s real engineering under the hood. GobbleCube replaced Cube.js with an in-house analytics engine called Antman, built in Go on top of PostgreSQL and ClickHouse. The team says that shift made the system 20x faster, helped it handle production traffic beyond 1,000 requests per minute, and improved the low-latency decision paths needed for AI agents and near-real-time recommendations. It’s not magic. But it makes the “agentic AI” claim sound less like brochure language and more like infrastructure work.

Who built the GobbleCube AI platform?

The founding story

GobbleCube was founded in 2022 in Gurugram by Manas Gupta, Srikumar Nair, and Nitesh Jindal. The founders weren’t coming at the problem from the outside. They’d already spent 7+ years inside Blinkit, where they led category, engineering, and data functions and worked closely with more than 500 brands. That matters because GobbleCube’s product is built around the daily operational pain those brands kept hitting on digital commerce channels.

Gupta’s own background is a little less standard for a commerce SaaS founder. He has described growing up in a small town, studying at IIT and IIM, then working in investment banking and trading before joining Grofers, which later became Blinkit. Nair brought deep operating exposure from the Grofers-Blinkit years. Jindal handled the technology side that now sits at the center of GobbleCube’s product stack. It fits.

Why this team fits the problem

The founders seem to understand better than many broader ecommerce software players how messy quick commerce gets at the hyperlocal level. A product can be winning in one city and invisible in another. A campaign can be live while stock is low. A listing can look fine nationally and still be broken in specific dark stores. GobbleCube’s “answer-first” framing comes straight out of that operating reality. As Gupta put it, “We’ve designed our AI models to identify the most important problems and act on them.”

Traction, fundraising, and the competition

Traction arrived fast. GobbleCube came out of beta in September 2024. By July 2025, the company had gone from $0 to more than $2 million in ARR and from 0 to 200+ brands in the 9 months after leaving private beta. With this latest Series A announcement, that customer base has grown to 400+ brands across enterprise and D2C, including 45 large consumer goods companies such as HUL, Nivea, Tata Consumer Products, ITC, Godrej, Beiersdorf, MTR, L’Oréal, and Hershey’s. The startup says revenue grew 10x over the past year.

The cap table filled out in stages. GobbleCube raised $1.9 million in an early round led by Kae Capital in March 2024. Then it raised a $3.5 million pre-Series A round in 2025 backed by InfoEdge Ventures and Kae Capital. It has now added a $15 million Series A led by Susquehanna Venture Capital with participation from InfoEdge and Kae. That takes total funding to more than $20 million. The company will use the new money for AI product development, hiring, and international expansion, while also deepening its reach across 30+ digital marketplaces in India, MENA, and LATAM.

Competition is real, and it’s not coming from just one angle. CommerceIQ sells a much broader retail ecommerce management platform. It ties together sales, advertising, and supply chain automation. 42Signals focuses on digital shelf analytics, pricing, competitor monitoring, and voice-of-customer signals. Saras Pulse leans on AI-ready datasets, dashboards, and 200+ connectors for omnichannel brands. Then there are younger players like Dcluttr, plus the old incumbent setup most brands still use: spreadsheets, exports, BI tools, and platform category managers trying to patch everything together by hand. GobbleCube is betting that quick-commerce brands want something narrower, more hyperlocal, and more action-oriented than a generic analytics suite.

Why GobbleCube’s Series A matters

This round matters because GobbleCube is trying to graduate from analytics software into operating software. That’s a much bigger ambition. If the product can move from “here’s the problem” to “here’s the action, and we can execute parts of it for you,” it becomes much stickier inside a brand organization. That’s likely what Susquehanna is buying into.

The geography plan is also telling. India gave GobbleCube the right training ground because quick commerce is brutally data-heavy and highly localized. Expansion into the US, China, and Southeast Asia means the company now has to prove its model can survive different marketplace structures, data-sharing norms, and retail behavior. That won’t be easy. Still, the fact that it already operates across India, MENA, and LATAM suggests this isn’t a pure India-only product story anymore.

How big is the market GobbleCube is chasing?

Forecasts vary a lot because some reports talk about narrow e-retail GMV while others use a much broader ecommerce definition. Even with that caveat, the direction is obvious. A Bain-Flipkart estimate published through IBEF says India’s e-retail market could reach $170 billion to $190 billion in GMV by 2030, up from about $60 billion in 2024, with more than 270 million online shoppers already active. The same estimate says quick commerce already accounts for around 10% of total e-retail GMV and 70% to 75% of e-grocery GMV. It’s expected to grow at more than 40% annually.

The broader digital commerce view is even larger. Some market forecasts put Indian ecommerce near $300 billion by 2030, while the source article for this news pegs the opportunity at $400 billion and sees 10-minute delivery alone becoming a $35 billion to $40 billion market by then. Whatever number you pick, the structural shift is the same: more consumer brands are selling across marketplaces where pricing, assortment, availability, and search visibility can change by city, hour, and platform. That creates demand for software like GobbleCube.

What does the GobbleCube AI platform need to prove next?

GobbleCube has money, traction, and a product that sounds more grounded than a lot of AI startup pitches.

Now it has to prove that the GobbleCube AI platform can become daily decision software for global consumer brands, not just a very smart analytics layer. Watch the execution piece. If the agentic layer starts owning real ad, pricing, and inventory actions, this gets a lot more interesting.

Read how Pillar raised a $20M seed led by Andreessen Horowitz to replace spreadsheets and broker calls with an AI-powered commodity hedging platform built for businesses that can’t afford a full trading desk.

FAQ

What funding did GobbleCube raise?

GobbleCube raised $15 million in a Series A round announced on April 15, 2026. Susquehanna Venture Capital led the round, and existing backers InfoEdge Ventures and Kae Capital also participated, taking the startup’s total funding to more than $20 million.

How does the GobbleCube AI platform work?

The platform combines marketplace sales, inventory, pricing, promotions, search, and competitor data into one operating layer for brand teams. Its AI models then rank the highest-impact problems and recommend actions through products like Gobbs Edge, Gobbs Boost, Gobbs Flow, and Gobbs Discover, rather than leaving users to interpret raw dashboards on their own.

Who founded GobbleCube?

GobbleCube was founded in 2022 by Manas Gupta, Srikumar Nair, and Nitesh Jindal. All 3 previously worked at Blinkit, where they spent years across category, engineering, and data roles and worked closely with hundreds of consumer brands before starting the company in Gurugram.

Is GobbleCube an ecommerce analytics company or a quick-commerce software startup?

It’s really both, but the cleaner description is B2B revenue management software for brands that sell across ecommerce and quick-commerce marketplaces. What makes it different from a plain analytics tool is its focus on hyperlocal decisions, dark-store visibility, and an action layer that aims to automate what brand teams should do next.

Pillar is a commodity hedging platform that helps metals traders, recyclers, food companies, and airlines automate the work of managing exposure to swings in commodities, currencies, and freight. On April 14, 2026, the startup said it raised a $20 million seed round led by Andreessen Horowitz. The pitch is pretty simple: lots of physical businesses still handle hedging through spreadsheets, broker calls, and periodic check-ins. Price moves can hit margins fast. Pillar was founded in 2023 by CEO Harsha Ramesh and CTO Chinmay Deshpande, and it’s trying to bring institutional-style risk tools to companies that usually don’t have a full trading desk.

What is Pillar’s commodity hedging platform and how does it work?

Pillar’s commodity hedging platform starts with the transaction itself. A customer can submit a shipment or trade through the web app or even through WhatsApp, and the system links the hedge to that specific deal instead of treating risk as one giant monthly estimate. From there, Pillar ingests data from contracts, ERP systems, spreadsheets, inventories, cash flows, and messages to map the actual exposure sitting inside the business.

That matters because Pillar isn’t selling generic dashboards. The product builds hedges in real time and lets users execute trades with one click. It keeps tracking positions as quantities change. Hedges can be sized in 1 metric ton increments, which is a useful detail for smaller operators who don’t want to round exposure into blunt, oversized trades. It also supports paired commodity and FX coverage, so a business importing metal in one currency and selling finished goods in another can manage both sides in one workflow.

The more technical layer is where Pillar is trying to stand apart. It can calculate hedge ratios, react to forward-curve shapes, and automate strategies that take advantage of cross-exchange pricing differences. On the enterprise side, it also connects trade details to the right contracts. It continuously recommends hedge type, size, and timing based on changing exposure and market conditions.

Before software like this, a finance or ops team might juggle spreadsheets, broker messages, shipping changes, and approvals by hand. Pillar’s promise is that the operator types “hedge a shipment,” “check my position,” or “get a price,” and the system handles the mechanics in the background. It’s still not fully lights-out in every case. Humans remain involved for approvals, oversight, and especially large or unusual trades, but it’s a lot closer to continuous risk management than the old monthly-review model.

Who founded Pillar and what gives the team market fit?

How Pillar started

Pillar was founded in 2023. Ramesh’s argument is that sophisticated financial institutions have had strong hedging infrastructure for years, while the producers, importers, manufacturers, and recyclers actually moving physical goods often haven’t. He’s described that gap bluntly: risk management was treated as “a luxury” for businesses that still had to live with the volatility.

The company is based in New York and is live in market, not stuck in pilot mode. Its named customers include Shibuya Sakura Industries, Sigma Recycling, and United Metal Solutions Group. LinkedIn currently lists Pillar in the 11-50 employee range, with 14 employee profiles visible.

Why the founders look credible here

Ramesh’s background fits the category almost too neatly. He previously executed corporate FX hedging programs for Fortune 500 companies as an emerging markets trader, and he was an AVP at Barclays before becoming a general manager at Oliver Space. In the source interview, he also said he spent time inside a medium-sized import-export business, which helps explain why Pillar talks so much about operators rather than just treasury teams.

Deshpande brings the engineering side. He built automated trading products at Coinbase for retail and institutional customers and earlier worked at Amazon Logistics. That mix matters. You’re building software that has to understand messy operational data on one side and market execution on the other.

Both founders studied at Vanderbilt University Ramesh in economics, Deshpande in computer science. That doesn’t make the company by itself, obviously. But for a startup selling financial automation into commodity-heavy businesses, the combination of trading experience and systems-building experience is the thing investors are really buying.

Fundraising details and early traction

Andreessen Horowitz led the $20 million seed round, with Crucible Capital, Gallery Ventures, and Uber CEO Dara Khosrowshahi also participating. Pillar has now raised $23 million in total. For a seed-stage company serving conservative, risk-sensitive customers, that’s a chunky early round. It’s probably necessary, because selling into physical industries usually takes product depth and hands-on support, not just fast SaaS demos.

How Pillar compares with banks and commodity risk software

Pillar isn’t walking into an empty market. The obvious incumbents are bank hedging desks, which can structure and execute trades but usually don’t give smaller operators a clean operating layer tied to contracts, shipments, and day-to-day workflows. Then there are commodity risk software providers like RadarRadar. It focuses on integrating ERP and CTRM data into a harmonized view for advanced risk, margin, and reporting analytics.

Pillar’s angle is narrower and more opinionated. It ties hedges to individual deals and lets users trigger workflows in natural language or WhatsApp. It supports odd-lot hedging in 1 metric ton increments and keeps adjusting positions as the physical book changes. That sounds especially attractive for midsize firms that don’t want a giant enterprise implementation and don’t have the patience for bank-style process overhead.

Why does Pillar’s $20M seed round matter?

A round this size gives Pillar room to do the annoying parts right.

That matters because this isn’t a pure software story. The product touches trade execution, approvals, exposure modeling, and customer workflows that can go sideways if the data is sloppy. Pillar has already said humans stay in the loop for approvals, oversight, strategic decisions, and bigger transactions. So it has to build both product and operating discipline at the same time.

Andreessen Horowitz leading the round is also a signal about what kind of fintech investors want more of now. Not consumer finance. Not another neobank. More infrastructure for the physical economy the kinds of businesses that still run a lot of critical processes through email threads, spreadsheets, and phone calls. Pillar sits right in that zone.

The investor list says something else too. Khosrowshahi’s participation doesn’t prove a freight thesis on its own, but it does fit Pillar’s push beyond metal price exposure into FX and shipping risk. If the company can become the operating layer for all 3, it gets much harder to replace.

Why are commodity hedging platforms gaining traction now?

The underlying markets are massive. The BIS said OTC foreign exchange turnover reached $9.6 trillion per day in April 2025, up 28% from 2022, while outright forwards alone hit $1.8 trillion per day. Those are the instruments businesses use to lock in future exchange rates. That’s exactly the kind of workflow Pillar is building around.

Commodity and futures activity is still huge too. FIA data showed global futures volume reached 7.15 billion contracts in the first quarter of 2025, up 12% year over year, even as total exchange-traded derivatives volume was distorted by a sharp drop in options activity in India. In plain English: the pipes of the hedging world are busy. Real businesses are dealing with a lot more market movement than they used to ignore.

There’s also a structural shift in who needs better tools. Tariffs, shipping shocks, fragmented supply chains, and fast moves in industrial metals have turned hedging into an operating issue, not just a treasury issue. The software opportunity isn’t just about replacing spreadsheets. It’s about connecting contracts, inventory, logistics, and execution so exposure gets managed as the business moves.

What should you watch from Pillar’s commodity hedging platform next?

The next test isn’t whether Pillar can raise money. It already did that.

The real question is whether its commodity hedging platform can become boring in the best possible way something recyclers, traders, and importers use every day without needing a specialist desk to babysit it. If Pillar keeps winning customers in metals and adjacent categories, and if it proves the freight-plus-FX layer works as cleanly as the commodity side, this could turn from a neat fintech story into real infrastructure for physical operators.

Read how TraqCheck raised ₹75 crore to turn background verification — one of HR’s dullest jobs — into an AI-powered hiring workflow that enterprises can run from a single platform.

FAQ

What funding did Pillar raise?

Pillar raised a $20 million seed round announced on April 14, 2026. Andreessen Horowitz led the deal, with Crucible Capital, Gallery Ventures, and Uber CEO Dara Khosrowshahi also participating, bringing total funding to $23 million.

How does Pillar work for a customer?

Pillar connects to operational data like contracts, ERP records, inventories, and spreadsheets, then links hedges to specific transactions instead of broad estimates. A user can trigger actions through the web app or WhatsApp, execute a hedge, and keep adjusting it as the underlying shipment or position changes.

Who are Pillar’s founders?

Pillar was founded in 2023 by Harsha Ramesh and Chinmay Deshpande. Ramesh came from emerging-markets and corporate hedging roles at Barclays and later worked in operations, while Deshpande previously built automated trading systems at Coinbase and worked at Amazon Logistics.

Is Pillar a fintech company or commodity risk software?

It’s really both, but the cleaner label is commodity and FX risk management software with embedded execution. Pillar sits between bank hedging desks and heavier commodity risk platforms by giving physical businesses a more operational, transaction-linked way to hedge prices, currencies, and freight.

TraqCheck is an HR tech startup that automates employee background checks and is now pushing deeper into AI-led hiring workflows. It has raised nearly ₹75 crore in a Series A round led by IvyCap Ventures, a deal that matters because too many enterprises still run hiring, screening, and verification across disconnected tools that waste time and invite errors. Founded in 2020 by Jaibir Nihal Singh, Armaan Mehta, and Rishabh Jain, the New Delhi company is using this TraqCheck funding round to expand in Europe and build out its AI capabilities.

That’s a sharper story than a standard “AI for HR” pitch.

TraqCheck isn’t chasing the fluffiest part of recruiting. It’s going after one of the least glamorous, most compliance-heavy parts of the stack background verification and then trying to stitch that into the front end of talent acquisition. If it works, buyers get fewer vendors and faster hiring cycles. They also get less manual back-and-forth. If it doesn’t, it risks getting squeezed between specialist verification firms and giant HR suites that are adding AI fast.

What does TraqCheck actually do for hiring teams?

TraqCheck’s core product is Trace, an AI-driven background verification tool that handles checks across criminal records, education, identity, and work history. For an employer, the workflow is direct: a company triggers a verification request, the platform collects and validates records, flags inconsistencies, and turns the result into a cleaner decision-ready report instead of a pile of fragmented responses from institutions and prior employers.

But the company isn’t stopping at screening. It now talks about a broader “Human Operating System” for HR, with a second agent called Nina built for the top of the funnel. Nina sources candidates in real time and understands role requirements beyond blunt keyword matching. It sends personalized outreach, replies to candidates, handles follow-ups automatically, and returns a shortlist to hiring teams through a chat-style interface.

That changes the customer experience in a simple way. Before, recruiters often had one tool for sourcing, another for applicant tracking, and a separate vendor for checks. TraqCheck wants those steps to sit in one workflow source, engage, screen, verify and move the right people forward without the usual spreadsheet mess.

There’s a practical reason that pitch lands. Background checks are slow, repetitive, and easy to break when data collection is manual. Automating ID verification, employment checks, and record gathering doesn’t just save recruiter time. It also cuts the kind of process friction that kills offer acceptance or leaves hiring managers waiting on final clearance.

Who founded TraqCheck and how far has it scaled?

The founding story

TraqCheck started in 2020 with Jaibir Nihal Singh, Armaan Mehta, and Rishabh Jain. The founding idea was straightforward: hiring teams were relying on fragmented verification processes that were too slow for modern recruiting and too messy for enterprise compliance. So the company began with background checks, then widened the pitch toward a fuller hiring workflow.

Why the founders fit the problem

Singh studied media and communications at Pepperdine University and had early exposure to operating and business-building work. Mehta studied quantitative economics at UCLA and also trained in data science and machine learning. He previously interned with SoftBank Investment Advisers and worked on research projects tied to economic and technology themes. Jain came from a computer science background and had engineering experience in Silicon Valley roles focused on software and machine learning. The mix fits the problem.

Early execution and traction

TraqCheck is already live in market, not sitting in demo mode. The company serves about 300 enterprise clients across India and Europe, and it has cited customers such as Randstad Enterprise, Wipro, and The Digital College. Its current push is split between the mature verification business and the newer AI agent layer for talent acquisition. That gives it both a steady enterprise use case and a more ambitious expansion story.

Fundraising details

IvyCap Ventures led the fresh Series A round, with participation from IIFL Fintech Fund. Before this, TraqCheck had raised a pre-Series A round backed by Caret Capital and former Goldman Sachs executive Alok Oberoi, with Lenskart cofounder Peyush Bansal also investing as an angel. The new capital is earmarked for European expansion, stronger enterprise sales, and further development of the company’s AI agents.

How TraqCheck stacks up against rivals

This is where things get interesting.

On background screening, TraqCheck runs into specialist verification players such as Checkr, Truework, and Certn. On AI-led recruitment, it’s up against newer hiring startups like TurboHire and HireBound. Above them sit the heavyweights Workday, Greenhouse, and Lever which already own big chunks of recruiter workflow and are steadily layering in AI features.

TraqCheck’s edge isn’t that it invented background checks. It’s trying to join a boring but sticky enterprise function with higher-frequency recruiting actions in the same product flow. Legacy vendors often handle verification as a separate service line. Big HR suites do a lot, but they can feel bulky and slow to adapt. TraqCheck is betting buyers want speed, a cleaner user experience, and fewer handoffs between sourcing and verification. It’s a smart bet. It’s also a risky one, because the incumbents aren’t asleep.

Why does TraqCheck funding matter now?

Because this round lets the company move from being useful to being harder to ignore.

European expansion matters for a background verification business because compliance-heavy workflows travel well when the product is structured right. If TraqCheck can prove that its automation works across geographies and not just inside Indian hiring flows, it becomes more than a local HR tech story. It becomes a candidate for multinational enterprise budgets.

The product roadmap matters too. Building around Trace alone would have kept TraqCheck in a narrower category. Putting money behind Nina suggests the company wants a larger share of recruiting spend, not just a slice of verification budgets. That gives investors a bigger upside case but only if customers actually adopt the broader workflow instead of buying the company for checks and ignoring the rest.

The round also carries a sales signal. TraqCheck plans to grow enterprise sales and expand its UK team to 25 people while converting pilot projects into longer-term contracts. This isn’t just a product build story. It’s a go-to-market test at a more serious scale.

How big is the market TraqCheck is chasing?

The homegrown HR tech market TraqCheck sells into is expected to become a $2.3 billion opportunity by 2034. A separate market estimate puts India’s HR technology segment at $1.21 billion in 2025 and projects it to reach $2.33 billion by 2034, growing at a 7.56% CAGR. Recruitment already accounts for 25% of the market, which helps explain why startups and incumbents alike are trying to automate screening, matching, and hiring operations.

A few shifts are doing the heavy lifting here. Indian enterprises are buying more cloud software. Recruiters are under pressure to cut time-to-hire. AI tools are moving from “nice demo” territory into actual workflow software that promises measurable output. That doesn’t mean every AI recruiting startup wins. It does mean buyers are more open than they were to replacing manual verification and fragmented hiring steps with software.

Timing helps. Information technology is the largest end-use segment in India’s HR tech market at 32%, and that matters because large tech employers and staffing-heavy businesses feel hiring friction faster than almost anyone else. When those customers start looking for tighter automation and compliance, startups like TraqCheck get a real opening.

Conclusion

TraqCheck funding isn’t just another enterprise AI round with a vague automation pitch. It’s a bet that background verification one of the dullest parts of HR can become the anchor for a broader hiring product that companies actually pay to expand. The next thing to watch is whether TraqCheck can turn 300 enterprise relationships and its Europe push into durable, multi-product contracts before bigger HR platforms close the gap.

Read how Slate Auto raised $650M to build a modular, blank-canvas electric pickup that starts in the mid-$20,000s and lets owners keep customizing it long after they drive it off the lot.

FAQ

What is TraqCheck funding and who invested in the round?

TraqCheck funding refers to the startup’s nearly ₹75 crore Series A raise announced in April 2026. IvyCap Ventures led the round, and IIFL Fintech Fund joined in, giving the company fresh capital to expand in Europe and build out its AI hiring products.

How does TraqCheck work for background checks and hiring?

TraqCheck works through two product layers: Trace for background verification and Nina for AI-led recruiting tasks. Trace handles checks like identity, education, criminal records, and employment history. Nina sources candidates, runs outreach, follows up automatically, and returns a shortlist through a conversational interface.

Who are the founders of TraqCheck?

TraqCheck was founded in 2020 by Jaibir Nihal Singh, Armaan Mehta, and Rishabh Jain. Singh brings business and operating exposure. Mehta has economics and investment experience plus data science training. Jain comes from a software and machine learning background.

Is TraqCheck an HR tech startup or a background verification company?

It’s both, and that’s the point of the business. TraqCheck started from background verification but is now expanding into broader HR tech and talent acquisition software, which puts it somewhere between a screening specialist and a wider recruiting automation platform.

DAAKit is a Gurugram-based logistics company that helps brands store inventory closer to buyers and deliver faster through dark stores and last-mile operations. The hyperlocal fulfillment startup has raised $138,000 in a pre-seed round led by Inflection Point Ventures, at a time when Indian sellers can generate demand online but still lose customers on delivery speed and cost. Founded in 2024 by Chandan Singh Ghugtyal, DAAKit is trying to give smaller brands some of the logistics muscle that quick-commerce giants have already made shoppers expect.

What is DAAKit and how does hyperlocal fulfillment work?

DAAKit’s pitch is pretty direct: a brand signs up, connects its online store through an API or plugin, places stock inside DAAKit’s dark-store network, and lets DAAKit handle pick-pack-deliver operations from the nearest node. On its site, the company lays that flow out as onboarding, integration, storage, pick-and-pack, then same-day delivery through its fleet and partner network. Many brands can go live within 24 hours.

That matters because DAAKit isn’t just selling courier aggregation. It’s selling inventory placement. A customer in Lucknow doesn’t need to be served from Delhi if stock can sit closer to demand. In a 2025 profile, Ghugtyal said that approach can cut delivery time from 5 days to 1 and reduce logistics costs by 20% to 30% for brands that would otherwise ship from centralized warehouses.

The product stack is more specific than the source brief suggests. Sellers can manage inventory across locations, track expiry dates, automate replenishment, and monitor stock in real time through its warehouse management system. It has also built an intelligent courier allocation engine that chooses delivery partners based on cost, SLA performance, and serviceability. That’s the boring back-end stuff that actually decides whether fast delivery can stay profitable.

The customer experience is built around speed options, not one generic promise. DAAKit advertises 30-minute express delivery in metro areas. It also offers 60-minute and 2-hour options for quick-commerce use cases, plus same-day delivery for broader fulfillment needs. Its network is supported by AI-driven inventory management, real-time tracking, and GPS-linked routing.

Who founded DAAKit and what has it built so far?

The founding story

DAAKit was founded in 2024 by Chandan Singh Ghugtyal, who is also the startup’s Founder and CEO. The company’s central idea is simple enough to understand in one line: let brands decentralize inventory and get closer to customers instead of treating fulfillment like one giant warehouse problem. The business is built around dark stores and hyperlocal fulfillment centres. It also handles last-mile delivery for brands and sellers that want faster shipping without building that infrastructure themselves.

Founder-market fit

Ghugtyal brings 12 years of experience across e-commerce, warehousing, third-party logistics, and quick commerce. That’s useful here because DAAKit isn’t a pure software product pretending logistics is easy. It’s an operational business where warehouse placement, courier performance, and unit economics matter a lot more than a flashy dashboard. That experience also helps explain why the company talks so much about infrastructure and not just delivery speed.

Traction and early signals

The startup is already live across Delhi, Gurugram, Mumbai, Bengaluru, and Kolkata, with Lucknow running as a pilot market for deeper Tier-II and Tier-III expansion. ITLN reported this week that DAAKit is fulfilling thousands of orders daily and is aiming for nearly 500,000 orders per month as the Lucknow warehouse ramps. It has also partnered with organizations including the RP-Sanjiv Goenka Group and Krishna Ayurveda.

The company’s growth numbers are early, but they’re not trivial. IPV’s Mitesh Shah said DAAKit has been growing orders and revenue by 15% to 20% every month while maintaining double-digit EBITDA. That combination speed plus profitability this early is why investors will pay attention, even if the absolute scale is still small.

Fundraising details

This pre-seed round totals $138,000 and Inflection Point Ventures led it. Before this, DAAKit had already secured ₹20 lakh through CCD from the SISFS Fund at AIC IIT Delhi Sonipat, and it finished as runner-up at Eureka 2025 in the Logistics & Supply Chain category, which came with a ₹1 lakh cash prize.

The new money will go into 25 new dark stores across Tier-I and Tier-II cities, deeper tech work including infrastructure, licensing, and integrations, and team expansion through senior hires such as CXO roles, a VP for Sales and Business Development, and operations managers for new markets. It’s a pretty clear use-of-funds plan. No mystery deck language.

How does DAAKit compare with Prozo, Shiprocket, and legacy 3PLs?

DAAKit isn’t competing head-on with Blinkit or Zepto as a consumer app. It’s closer to the infrastructure layer behind the experience that quick commerce has trained shoppers to expect. That makes Prozo and Shiprocket more useful comparisons. Prozo runs a distributed hyperlocal network of 68+ dark stores with multiple last-mile partners, while Shiprocket operates 45+ dark stores across Delhi NCR, Bengaluru, Mumbai, and expanding Tier-I and Tier-II cities.

Legacy alternatives look different. A lot of D2C brands still rely on centralized warehousing plus standard courier networks, which works for national reach but usually doesn’t help if the brand wants 2-hour, 4-hour, or same-day delivery from its own site. That’s the gap DAAKit is chasing. Its pitch is that brands don’t need to build their own quick-commerce stack. They can rent the capability.

DAAKit differentiates itself with its asset-light model and its focus on early profitability rather than brute-force expansion. IPV’s Shah put it bluntly when he said DAAKit is “not just another delivery service” because the hard-to-copy piece is the infrastructure layer dark stores, fulfillment networks, and last-mile reach. That’s a sharper positioning than a plain courier marketplace.

Why DAAKit’s pre-seed round matters now

This round matters because DAAKit is at the point where local proof has to turn into repeatable infrastructure. Opening 25 new dark stores is not a vanity milestone. It changes service promise and inventory density in each city. It also changes brand adoption potential. And if the company is holding double-digit EBITDA while adding nodes, that makes the expansion story a lot more credible than the usual “grow first, explain margins later” startup playbook.

It also matters for customers. A D2C brand doesn’t just want speed for bragging rights. It wants fewer canceled orders, lower return-to-origin rates, and better control over post-purchase experience. DAAKit says its model can reduce RTOs sharply and improve same-day delivery accuracy. That’s the kind of operational result a seller will care about more than any glossy app interface.

It also shows what IPV is betting on. Not another demand-side app. Not another discount-driven consumer platform. The bet is that fast commerce in India will need more neutral infrastructure providers that help brands sell from their own channels. That’s a more defensible thesis if execution holds up.

How big is the hyperlocal fulfillment market in India?

The macro setup is favorable. India’s logistics sector is valued at more than $250 billion, yet logistics costs still account for nearly 14% of GDP, and more than 12 million small and medium-sized businesses still don’t have reliable fulfillment access. That’s the kind of operational mess a company like DAAKit wants to turn into software-plus-infrastructure revenue.

The retail base underneath this is huge. Redseer said in February 2025 that India’s retail market is projected to reach $1.5 trillion by FY29 and $2.4 trillion by FY34, with branded and organized retail expanding fast enough to create a much bigger role for third-party logistics providers. That same report argued that brands increasingly care about delivery timeliness and speed. They also care about shipment protection and reach, which sounds a lot like DAAKit’s sales pitch.

Quick commerce is part of the story too, even if DAAKit isn’t a grocery app. Redseer had projected India’s quick-commerce market at about $5.5 billion by 2025, and by July 2025 the firm said dark-store count could climb to roughly 5,500 in FY26 as the big platforms kept expanding. Once shoppers get used to near-instant delivery, brands outside groceries start feeling that pressure as well. That’s why hyperlocal fulfillment is no longer a niche idea. It’s becoming baseline infrastructure.

DAAKit still has a lot to prove. A 2024-founded company raising a $138,000 pre-seed round isn’t suddenly a category winner. But if it can keep margins intact while adding 25 new dark stores and pushing beyond metro-heavy demand, this hyperlocal fulfillment bet could get interesting fast. The next thing to watch is simple: whether Lucknow-style expansion works as cleanly as the company thinks it will.

Read how ClayCo Skincare Raises ₹34.6 Cr for New Categories to expand its product portfolio and enter new beauty segments.

FAQ

What funding has DAAKit raised?

DAAKit has raised $138,000 in a pre-seed round led by Inflection Point Ventures in April 2026. Before that, it secured ₹20 lakh through a CCD instrument from the SISFS Fund at AIC IIT Delhi Sonipat and also won ₹1 lakh as runner-up at Eureka 2025 in the Logistics & Supply Chain category.

How does DAAKit’s product actually work for brands?

DAAKit lets a brand connect its store, place stock in distributed dark stores, and use DAAKit for pick-pack-deliver operations from the nearest location. Brands can often integrate within 24 hours and then offer options ranging from 30-minute express delivery in metros to same-day fulfillment.

Who is the founder of DAAKit?

DAAKit was founded in 2024 by Chandan Singh Ghugtyal, who serves as Founder and CEO. He brings 12 years of experience across e-commerce, warehousing, 3PL, and quick commerce, which gives the company stronger operator DNA than a lot of software-first logistics startups.

What market is DAAKit selling into?

DAAKit sits inside India’s fast-growing last-mile logistics, dark-store, and quick-commerce infrastructure market. The broader logistics sector is already worth more than $250 billion, and Redseer has projected India’s retail market at $1.5 trillion by FY29, which is why infrastructure players serving D2C brands are getting more attention.

ClayCo skincare is a Mumbai-based premium skincare brand. It has raised ₹34.59 crore in a Series A round as Indian buyers spend more on homegrown brands that don’t feel like a compromise versus imported beauty labels. The problem ClayCo is chasing is clear: for years, shoppers who wanted prestige textures and globally popular actives often ended up looking outside India. Founded in 2023 by Niharika Jhunjhunwala, the company is trying to close that gap with a tightly edited, science-led portfolio that borrows heavily from Japanese and Korean beauty cues.

Twenty-Nine Capital Partners Ventures Ltd led the new round, with participation from ICMG Global Ventures II Pte. Ltd. ClayCo will use the money for product development, expansion into new categories, and working capital. That’s a sensible plan for a beauty brand. Inventory, formulation work, testing, and packaging eat cash fast, especially if you’re trying to look premium without charging imported-brand prices.

What is ClayCo skincare and how does it work?

ClayCo skincare sells a focused lineup of treatment-first products built around specific routines rather than a giant wall of SKUs. The brand’s range includes leave-on masks and exfoliation rituals. It also sells retinal treatments, eye care, sheet masks, serums, and moisturisers. The overall pitch centers on efficacy, sensorial finish, and internationally familiar actives packaged for Indian consumers.

The customer journey is straightforward. You pick the concern dullness, pigmentation, pores, texture, early ageing and the brand slots you into a simple routine. Its Rice & Sake Sleep Mask is a gel-based overnight treatment aimed at brightening and hydration. The Pore Cleansing Ritual works as a 2-step regimen with a Matcha Enzyme Scrub followed by a Detoxifying Matcha Clay Mask. And its Anti Ageing 0.15% Retinal Serum is sold as a faster, stronger alternative to classic retinol-based products.

The more interesting part is where ClayCo tries to stand apart. It has leaned into newer actives like exosomes and retinal. One of its newer hero products is a Cica Microneedling Exosome Serum that uses spicule-style microneedle tech to improve ingredient absorption. That’s not mainstream Indian mass-market skincare. It’s much closer to the language and product architecture you see in prestige Asian beauty.

And that matters because the brand isn’t selling just a cream or serum. It’s selling an experience that feels more curated than older Indian skincare aisles usually did. Before this new wave, buyers often had to choose between imported K-beauty and J-beauty, Ayurvedic legacy brands, or clinical derma products that worked but didn’t always feel aspirational. ClayCo is trying to sit in the middle science-backed, visually premium, and still local.

Who built ClayCo skincare and why now?

From Sugarbox to beauty

Niharika Jhunjhunwala didn’t come into this cold. Before ClayCo, she built Sugarbox, a subscription-commerce startup launched in 2014 that curated monthly boxes across beauty, fashion, lifestyle, and gourmet categories. She also studied economics at Lady Shri Ram College and later completed a master’s in economics and management at the London School of Economics. That mix consumer taste, curation, and business training gives her better market fit than the average founder who spots beauty as just another hot D2C category.

She seems to have understood early that Indian beauty buyers had changed. They weren’t satisfied with vague “natural” claims anymore. They wanted ingredient literacy and stronger actives. They also wanted dermatologist-adjacent credibility and products that felt nice to use. ClayCo’s whole design language limited range, prestige cues, global formulations, ritual-based storytelling comes straight out of that shift.

The numbers got investors’ attention

ClayCo’s growth has been fast enough to turn a niche skincare idea into a proper venture story. Revenue climbed from ₹5 crore in FY24 to ₹33 crore in FY25, then to ₹72 crore in FY26. That’s a 14x jump across the period. Investors will forgive a lot for that kind of curve, including a narrow assortment and a premium positioning strategy that normally takes longer to scale.

The brand has done that while keeping its range deliberately tight and largely in the ₹600 to ₹1,300 price band. That’s an important signal. Instead of flooding the market with dozens of me-too launches, ClayCo has tried to build recall around a smaller set of hero products, including the Rice & Sake Mask. In beauty, focus can be a growth hack if the product actually lands.

Where ClayCo sits against competitors

ClayCo isn’t alone. It’s entering a premium Indian skincare category that already includes ingredient-led brands such as Minimalist, Foxtale, Pilgrim, Deconstruct, and other newer D2C labels chasing educated urban consumers. Foxtale raised fresh funding in 2024, and Minimalist has already grown into one of the category’s biggest names. This is no longer an experimental niche.

But ClayCo’s differentiation is a bit different from the usual “science-backed skincare” line. It’s mixing clinical language with prestige beauty cues, Japanese and Korean inspiration, and a smaller portfolio that feels more edited than commodity-driven. The legacy alternatives are either imported skincare, older herbal and Ayurvedic brands, or doctor-led derma products. Investors are betting ClayCo can own the space between those buckets.

Why ClayCo skincare’s Series A matters

This round matters because ClayCo has moved past the “interesting brand” stage. At ₹72 crore in FY26 revenue, it now has enough demand to justify broader category expansion but beauty expansion done badly can wreck a brand fast. A Series A of this size gives ClayCo room to add products without turning into another cluttered skincare shelf.

Jhunjhunwala framed the pitch in blunt terms: “For too long, Indian consumers who wanted truly world-class skincare had to look abroad. We built ClayCo to change that to prove that you don’t have to compromise on formulation, texture, or results when buying an Indian brand.” That’s not just brand storytelling. It’s basically the entire commercial thesis behind the company.

ICMG’s angle is just as telling. CEO Gen Funahashi said the firm is backing ClayCo because it blends Japanese beauty heritage with modern science, and because the partnership fits ICMG’s co-creation model linking Japanese resources with India’s market opportunity. Translation: this isn’t only a consumer-brand bet. It’s also a cross-border formulation and brand-building thesis.

And the working-capital piece shouldn’t be overlooked. In premium beauty, cash isn’t just for ads. It’s for raw materials and packaging. It also covers regulatory work, inventory buffers, and getting timing right across launches. That sounds boring. It isn’t.

How big is India’s premium skincare market?

India’s beauty and personal care market was valued at ₹2,43,236 crore, or about $28 billion, and is expected to reach ₹2,95,358 crore, or about $34 billion, by 2028, growing at roughly 10% to 11% a year. That’s the backdrop for ClayCo’s raise. Investors aren’t just chasing one breakout brand. They’re positioning for a much larger consumer upgrade cycle in beauty.

The shift inside that market is what really matters. Buyers are getting more ingredient-aware. They’re comfortable spending more for targeted routines. And they’re discovering brands across marketplaces, beauty retail apps, and quick-commerce platforms instead of relying only on store shelves. That makes it much easier for a focused premium label to build demand without first becoming a mass-market giant.

There’s also a cultural shift here. Asian beauty rituals especially Japanese and Korean formats, textures, and actives have gone from niche internet obsession to mainstream aspiration in India. That doesn’t guarantee ClayCo wins. But it does mean the brand’s positioning is landing in a market that already understands the reference points. Timing counts.

Final take on ClayCo skincare

ClayCo skincare has raised enough money to test whether an Indian prestige skincare brand can scale without losing its edge.

The next thing to watch isn’t just revenue. It’s whether ClayCo can expand into adjacent categories, keep its product quality high, and stay distinctive while better-funded skincare brands crowd the same customer.

Read how Nava neocloud platform raises $22M with Greenoaks to scale its cloud infrastructure and next-gen computing platform.

FAQ

What funding did ClayCo raise?

ClayCo raised ₹34.59 crore, or about $4.1 million, in a Series A round. Twenty-Nine Capital Partners Ventures Ltd led the investment, and ICMG Global Ventures II Pte. Ltd also participated. The company plans to use the capital for product development, category expansion, and working capital.

How does ClayCo skincare work for customers?

ClayCo sells concern-led skincare routines rather than an endless catalog of products. A buyer typically chooses a need such as pigmentation, pores, dullness, or ageing, then uses products like the Rice & Sake Sleep Mask, Matcha pore ritual, retinal serum, or exosome-based treatments as part of a simple regimen. The brand’s pitch is that you get premium textures and stronger actives in an Indian label instead of having to shop imported alternatives.

Who is the founder of ClayCo skincare?

ClayCo was founded by Niharika Jhunjhunwala in 2023. Before launching the brand, she built Sugarbox, a consumer subscription startup founded in 2014, and studied economics and management at the London School of Economics after graduating from Lady Shri Ram College. That background helps explain why ClayCo feels sharply merchandised rather than randomly assembled.

Is ClayCo part of India’s D2C skincare market or premium beauty market?

It’s really both. ClayCo is a homegrown D2C skincare brand, but it’s positioned closer to premium beauty because of its clinical-validation messaging, ingredient choices like retinal and exosomes, and its tighter, prestige-style assortment. That’s the slice of the market investors care about as India’s beauty and personal care sector heads toward $34 billion by 2028.

Nava is building AI infrastructure for enterprises that need GPU compute, orchestration software, and data-center capacity without stitching the whole stack together themselves. The Nava neocloud platform has now raised $22 million, or about ₹204 crore, in a Series A round led by Greenoaks, with RTP Global and Unicorn India Ventures coming back in. AI adoption is running ahead of local compute supply in India and across Asia. Founded in 2025 by Abhinav Sinha, Vamshidhar Reddy, and Abhijeet Singh, the company is trying to sell a fuller answer to that bottleneck than a plain GPU rental business.

What is the Nava neocloud platform and how does it work?

The Nava neocloud platform is a full-stack AI compute system that combines infrastructure, data services, and developer tooling in one product. On its public product architecture, Nava splits that stack into three blocks: AI Factory, Data Platform, and Core Compute. In practice, an enterprise can rent compute and deploy models from one environment. It can also manage data and run inference there instead of bolting together multiple vendors.

The AI Factory layer is built for model serving and training. Nava offers inference as a service through model endpoints that auto-scale and rebalance in real time, along with platform services for deployment and monitoring. Under that sits GPU Fabric and AI-optimized data-center infrastructure. That’s the heavy plumbing that determines whether an AI workload runs fast or turns into an expensive mess.

Then there’s the data side. Nava lists parallel file systems, a lakehouse setup, model registry tools, and managed vector databases as part of its platform. AI teams usually don’t just need raw GPUs. They also need storage for large datasets and version control for models. Retrieval systems for production inference matter too. Nava’s pitch is that those pieces should be built together, not added later as afterthoughts.

Before the rebrand, the company described the product as an intent-driven AI-native private cloud where developers set performance, cost, or compliance goals and the software handles deployment and orchestration across hybrid, on-prem, edge, and sovereign environments. The newer Nava site shows that same logic, expanded into a more capital-heavy model that includes data centers and bare infrastructure. It also bakes in zero-trust controls like role-based access, GPU isolation, signed model artifacts, and encrypted vector stores. Enterprise buyers will ask about that before they sign anything.

Who founded Nava and what did they build before this?

The company started as Kluisz.ai

Nava began in 2025 as Kluisz.ai and has now rebranded as it shifts from a software-led GPU cloud product to a broader neocloud strategy. At the same time, the startup has set up Singapore as its regional headquarters so it can sit closer to APAC customers and talent. That’s not cosmetic. The company doesn’t want to stay boxed into India alone.

Why this founding team has real market fit

Abhinav Sinha, Nava’s CEO and co-founder, previously served as Global COO and CPO at OYO and earlier worked at BCG. Vamshidhar Reddy, also a co-founder, is a former McKinsey partner with AMD in his background. Abhijeet Singh, the third co-founder, previously led cloud infrastructure at Jio and earlier worked at AT&T. Between them, Nava has operations experience and direct cloud infrastructure exposure. It also has consulting and systems design depth.

Nava’s leadership page adds a bit more color. Sinha studied at Harvard and IIT Kharagpur. Reddy is listed with Stanford and IIT Kharagpur. Singh is also shown as an IIT Kharagpur alumnus. That doesn’t guarantee execution. But it reinforces the investor story here: this is a founder group built to sell hard technical infrastructure to serious buyers, not just spin up another AI wrapper.

Early traction, fundraising, and the crowded field

The product launched earlier in 2026, and Nava has begun onboarding paying customers, though it still hasn’t disclosed revenue. The company is also in advanced talks around rolling out GPU-based AI infrastructure offerings, and it plans to add senior talent across AI data-center design, GPU engineering, and go-to-market functions. In Singapore, it aims to hire 10 to 15 people by the end of the year.

On the financing side, Greenoaks led this new $22 million Series A, with RTP Global and Unicorn India Ventures participating. Nava’s total funding now stands at $31.6 million, after a $9.6 million seed round in July 2025 that RTP Global led and that also included Unicorn India Ventures, Blume Founders Fund, and Climber Capital. That seed round stood out at the time because it was one of the larger AI startup seed deals in India.

How does Nava compare with Neysa, Yotta, and E2E?

Neysa already sells a full-stack AI cloud system with GPU-as-a-service, orchestration, cost controls, and security. E2E Networks pitches itself as India’s GPU cloud for AI and machine learning. Yotta is pushing Shakti Cloud as a sovereign AI factory that covers training, fine-tuning, and deployment on Indian infrastructure. So Nava isn’t walking into an empty market. Not even close.

Nava is betting on tighter vertical integration and regional reach. Hyperscalers like AWS, Microsoft Azure, and Google Cloud are the obvious default alternatives, but they can be expensive, generic, and awkward for customers that want local deployment, lower latency, or tighter control over compliance and infrastructure choices. Nava’s pitch is that it can sit between those global clouds and old-school enterprise data-center projects. It offers more purpose-built AI features than one, and less complexity than the other. That’s a smart thesis. It’s also brutally hard to execute because infrastructure companies don’t get much room for error.

Why this Nava funding round matters now

A $22 million Series A matters here because Nava isn’t just polishing software anymore. It’s moving into the expensive part of the stack AI-optimized data centers, GPU infrastructure, orchestration, inference layers, and developer tooling. That kind of roadmap needs more than a seed-round budget. Greenoaks leading the round suggests investors think Nava could become more than a niche cloud product.

For customers, the round should mean faster product depth and more local capacity. Nava wants to build its AI compute platform for Asia, not just India, and that matters for companies training models or running inference close to end users. If the company delivers reliable GPU-as-a-service and bare-metal compute with decent developer ergonomics, it could win teams that are tired of juggling fragmented infra contracts.

For the category, this round is another sign that investors are warming up to infrastructure again but only when the story goes deeper than “we rent GPUs.” The core bet on Nava is that software alone won’t be enough, and hardware alone won’t differentiate either. The edge comes from owning both layers in markets where AI demand is rising faster than supply.

Why India needs more AI compute and neocloud capacity

India’s AI data-center push is getting big, fast. The backdrop to Nava’s raise is a broader buildout that the source article pegs at more than $200 billion in planned investment, while India’s AI market is projected to reach $126 billion by 2030 and contribute up to $1.7 trillion to GDP by 2035. That’s the opportunity side of the story. The problem is the infrastructure side still looks thin.

Sinha put the gap pretty bluntly: India has roughly 1 megawatt of compute capacity per million residents, versus more than 100 megawatts in the US. That’s a staggering difference. It helps explain why Indian AI startups and enterprises keep talking about sovereign infrastructure, not just cloud credits. Existing facilities were largely built for conventional cloud workloads. AI training and inference demand denser power, faster networking, and more specialized hardware.

This isn’t just an India story, either. JLL now expects global data-center capacity to climb from 103 GW to 200 GW by 2030, with AI workloads rising from about 25% of total capacity in 2025 to 50% by 2030. Asia-Pacific is part of that surge. So Nava’s timing makes sense.

The real test for the Nava neocloud platform

Nava has a credible founding team, returning investors, a fresh lead in Greenoaks, and a product vision that matches where enterprise AI is heading. That’s the bullish case.

But the Nava neocloud platform now has to prove it can do the ugly part too — secure capacity, ship reliably, price competitively, and stand out in a market where better-funded rivals are already moving. The next thing to watch isn’t another brand refresh. It’s customer adoption, infrastructure rollout, and whether Nava can turn a strong narrative into real compute on the ground.

Read how Astranova Mobility Raises ₹60 Cr for EV Fleet Leasing to expand its electric vehicle leasing and fleet management platform.

FAQ

What funding did Nava raise?

Nava raised $22 million in a Series A round announced in April 2026, led by Greenoaks with participation from RTP Global and Unicorn India Ventures. That brought its total disclosed funding to $31.6 million after a $9.6 million seed round in July 2025.

What does the Nava neocloud platform actually do?

It gives enterprises an integrated AI infrastructure stack instead of just raw GPU rentals. Nava combines inference services and GPU compute in one platform. It also includes Kubernetes and virtual machines, storage, vector databases, model registry tools, and enterprise security controls.

Who are the founders of Nava?

Nava was founded in 2025 by Abhinav Sinha, Vamshidhar Reddy, and Abhijeet Singh. Sinha came from OYO and BCG, Reddy from McKinsey and AMD, and Singh from Jio’s cloud infrastructure team and AT&T.

Is Nava an AI cloud startup or a data center company?

It’s trying to be both. Nava started with a software-led cloud approach under the Kluisz.ai name, and it’s now expanding into a full-stack neocloud model that includes AI-optimized data centers, GPU-as-a-service, bare-metal compute, orchestration, and inferencing software.

Astranova Mobility is a Gurugram-based EV asset management and leasing startup that helps fleet operators source, finance, maintain, refurbish, and redeploy electric vehicles. It has now raised ₹60 Cr in a Series A round led by IvyCap Ventures, with Trucks Venture Capital joining and existing backers Asian Development Bank and Advantedge Founders also participating. For commercial fleets, the messy part isn’t deciding that EVs matter it’s handling capital, uptime, servicing, and asset life after first deployment. Founded in 2023 by ex-Cars24 CEO Kunal Mundra and Grip Invest cofounder Nikhil Aggarwal, Astranova is trying to own that operational layer rather than just sell financing.

What does Astranova Mobility actually do?

Astranova Mobility runs a full-stack EV leasing model for commercial fleets. A customer starts by sharing fleet requirements, then works with Astranova to choose the right OEM and funding structure. It can then add service, energy, or refurbishment support. It’s basically a bundled workflow for fleet electrification instead of a single loan or vehicle sale.

The company’s operating scope is wider than a lot of EV finance startups. It works across electric 2-wheelers, 3-wheelers, light commercial vehicles, passenger EVs, charging infrastructure, and even batteries. On top of the lease itself, it offers customized operating leases and financing plans. It also offers annual maintenance contracts, certified refurbished vehicles with warranty, and energy packages through swapping and charging partners.

That matters because fleets usually stitch this together from too many vendors. One party supplies the vehicle. Another underwrites the loan. Somebody else handles repairs. Then the operator is left to figure out residual values and redeployment. Astranova’s pitch is that it can take care of due diligence and monitoring. It also handles refurbishment and redeployment, using its own tech and data stack to manage the asset through its lifecycle.

There’s also a financing twist here. Because of its link to Grip Invest, the model wasn’t built only for fleet buyers it was also designed to attract retail and institutional capital into leased EV assets. That gives Astranova a shot at being more than a fleet services company. It can become the asset manager sitting between OEMs, operators, and financiers.

Who founded Astranova Mobility and why now?

The founding story

Astranova was founded in 2023 by Kunal Mundra and Nikhil Aggarwal. The company started out as Electrifi Mobility and later rebranded to Astranova Mobility as it widened its ambition beyond pure EV deployment and began exploring other clean-transport technologies, including hydrogen for larger vehicles such as buses and trucks. That’s not just a name change. It suggests the founders don’t want to be trapped in one drivetrain thesis if commercial transport evolves differently by vehicle class.

Why the founders make sense for this market

Mundra brings the operating side. He previously served as CEO of Cars24 India, and he has more than 20 years across entrepreneurship, financial services, auto, and asset management. Aggarwal brings the capital markets angle. Before Astranova, he cofounded Grip Invest, the alternative investment platform that let investors co-own income-generating assets and lease them to users.

That pairing is unusually practical for EV leasing. One founder knows auto distribution and scale. The other knows structured asset finance. EV fleets need both. A lot of startups can sell vehicles or originate loans. Far fewer can think clearly about residual value, refurbishment, lender confidence, and redeployment economics in the same model.

Past execution and what they’ve already built

Grip matters more here than a casual mention would suggest. By 2023, about 30% of Grip Invest’s portfolio assets were EVs, and Grip had leased 15,000 EV assets worth more than ₹200 Cr across classes over the previous 3 years. That kind of operating history gives Astranova a useful head start in asset selection, financing relationships, and underwriting behavior.

Astranova also didn’t enter as a narrow category player. Mundra said early on that the business had already partnered with OEMs across 2-wheelers, cargo 3-wheelers, and passenger EV segments. So even before the rebrand, the company was being built as a multi-category commercial EV platform, not a single-segment loan book.

Traction, partnerships, and early proof

The startup has deployed more than 20,000 EVs so far and manages assets worth over ₹350 Cr. It has onboarded more than 15 banks, NBFCs, and lending partners, served 20 OEMs, and worked with over 50 fleet customers. Its partner list includes names such as Zypp Electric, Euler Motors, Magenta, Shadowfax, and Eveez.

There’s a real signal in where demand is coming from. Astranova has deployed vehicles across everything from 2-wheelers to LCV buses, but the strongest pull still comes from 2-wheelers especially from quick-commerce operators in big cities. That makes sense. Those vehicles rack up daily utilization fast, and that’s where the unit economics of electrification usually show up earlier.

Mundra also put a number on the near-term business outlook: “From a revenue perspective, we are now going to be at a ₹40 Cr run rate in FY26. We have always focused on profitability in the next 18 to 24 months, we want to grow at least 4X.” That’s ambitious for an asset-heavy business. Not impossible. But it raises the bar on execution.

The fundraising and the real test ahead

The new Series A round brings in ₹60 Cr, led by IvyCap Ventures, with participation from Trucks Venture Capital and existing investors Asian Development Bank and Advantedge Founders. Back in January 2024, the startup raised ₹25 Cr in seed funding, including ₹16 Cr in equity and the rest as debt capital. This fresh money will go into engineering, the data stack, AI capabilities, and team expansion.

How Astranova compares with EV leasing rivals

Its closest competition isn’t one company. It’s a cluster. Vidyut is building a full-stack EV ownership platform around battery subscription and underwriting algorithms. It also uses battery-health data. Turno focuses hard on financing and distribution for commercial electric 3-wheelers, with battery buyback and resale logic baked into the model. Revfin has leaned into lending and IoT-based monitoring. It has also pushed into leasing through battery-swapping partnerships.

Astranova’s difference is breadth. It isn’t pitching just a loan, just a marketplace, or just BaaS. It’s selling lifecycle control — sourcing, lease structuring, maintenance, refurbishment, redeployment, and energy support in one stack. The old-school alternative is still common: buy from an OEM, borrow from a lender, fix breakdowns locally, and worry about resale later. Investors are betting that fleet operators would rather outsource that headache to one specialist platform.

Why Astranova Mobility funding matters

This round matters because Astranova isn’t using the capital just to put more vehicles on the road. It wants to build engineering muscle, deepen its data stack, and add AI into the business. For a company in EV asset management, that’s not fluff. Better data can improve OEM selection and pricing. It can also improve maintenance forecasting, residual-value estimation, and decisions on whether to refurbish or redeploy an asset.

And that’s probably the real investor bet.

Leasing businesses look simple from the outside, but the good ones are really risk-calculation machines. If Astranova can use data and software to predict uptime and optimize service packages, it becomes harder to copy than a standard financier. Faster second-life asset decisions would help too. If it can’t, it risks being just another capital-heavy operator with thin differentiation.

The rebrand also gives the round extra weight. Astranova wants optionality beyond today’s EV categories and is openly looking at hydrogen for larger formats like buses and trucks. That’s still early. But it shows the company is trying to build for commercial clean mobility as a category, not only for the current 2-wheeler boom.

How big is the EV leasing market in India?

India’s policy direction is doing a lot of heavy lifting here. The country’s stated 2030 targets call for EV sales to reach 70% of commercial vehicles, 40% of buses, and 80% of 2- and 3-wheelers a shift that would put roughly 80 million EVs on Indian roads. More than 25 states have already notified EV policies, covering over 90% of the vehicle market.

But adoption is uneven, and that’s exactly why companies like Astranova exist. Recent reporting showed EVs accounted for 6.5% of two-wheeler sales and 52% of commercial three-wheeler sales, while medium and heavy freight still had almost no meaningful clean-mobility penetration at the national level. So the market isn’t moving in one neat wave. It’s moving by use case fast in last-mile and urban delivery, slow in heavier transport where product and financing models are tougher.

That split also explains Astranova’s current shape. It’s strongest where commercial utilization is high and fleet economics are easier to prove, especially in urban 2-wheeler delivery. The long-term upside is much larger than that. The near-term revenue reality isn’t.

What to watch next for Astranova Mobility

Astranova Mobility has raised enough to move from early proof to real systems-building. That’s the interesting part now. Not the headline number.

What matters next is whether it can turn EV leasing into a defensible operating platform one that keeps assets profitable across first use, service life, and second deployment. If it does, Astranova could become one of the more important infrastructure layers in India’s fleet electrification story. If not, this stays a funding headline and nothing more.

Read how RoshAi Raises ₹22 Cr for Industrial Autonomous Vehicles to scale its AI-powered autonomous solutions for industrial operations.

FAQ

What is Astranova Mobility’s latest funding round?

Astranova Mobility has raised ₹60 Cr in a Series A round. IvyCap Ventures led the financing, while Trucks Venture Capital joined and existing investors Asian Development Bank and Advantedge Founders also participated. The company plans to use the capital to build engineering capacity, strengthen its data and AI stack, and hire more people.

How does Astranova Mobility work for fleet operators?

Astranova Mobility works like a bundled EV fleet partner rather than a plain lender. A fleet first shares its requirement, then Astranova helps select the OEM and funding model. After that, it layers in services such as AMC support, refurbishment, redeployment, and energy access.

Who founded Astranova Mobility?

Astranova Mobility was founded in 2023 by Kunal Mundra and Nikhil Aggarwal. Mundra previously ran Cars24 India, while Aggarwal cofounded Grip Invest, which built experience in leasing and income-generating assets, including EVs.

Is Astranova Mobility an EV financing startup or an EV asset management company?

It’s really both, but the sharper label is EV asset management and leasing. Unlike companies focused only on loans or battery subscriptions, Astranova handles a wider lifecycle stack across sourcing, leasing, maintenance, refurbishment, and redeployment for commercial fleets.

RoshAi builds retrofit hardware and AI software that turns commercial vehicles in ports, mines, airports, and industrial yards into driverless machines. The Kochi-based startup has raised ₹22 crore, about $2.4 million, in a round led by IAN Group’s IAN Alpha Fund to push deeper into industrial autonomous vehicles. A lot of high-value industrial transport still depends on humans doing repetitive, risky driving in tightly controlled but messy environments. Founded in 2021 by Dr. Roshy John and Rajaram Moorthy, RoshAi is betting that autonomy will land first in those closed-loop settings long before fully driverless consumer cars do.

What does RoshAi do in industrial autonomous vehicles?

RoshAi sells a full-stack autonomy system for industrial fleets. In practice, that means it retrofits existing vehicles with drive-by-wire hardware. It runs an in-vehicle autonomy stack for perception, planning, and control, then connects the setup to a cloud fleet management layer. The company isn’t just building a demo car. It’s building an upgrade path for trucks, buses, and other commercial vehicles that operators already own.

The product is more specific than the usual “AI platform” label. RoshAi describes its core system as a modular, retrofit-ready Level 4 stack that works across trucks, buses, and passenger vehicles, on both ICE and EV platforms. Its General Perception Intelligence framework combines vision, LiDAR, radar, and other sensor inputs into a sensor-fusion layer. The autonomy software handles real-time decision-making, localization, navigation, and control.

That matters because a lot of autonomy projects still assume customers will buy purpose-built new vehicles or import expensive testing systems. RoshAi is trying the opposite approach. It offers drive-by-wire kits and ADAS and AV testing support. It also offers a software stack that can be dropped into different vehicle types, which cuts down the manual engineering work needed to stand up an autonomous pilot from scratch.

For operators, the before-and-after is pretty stark. Before, you’ve got human-driven fleets doing repetitive routes and relying on manual supervision. After retrofit, you get autonomous operation plus fleet-level tools for route optimization, predictive maintenance, diagnostics, real-time monitoring, manual override, and low-latency vehicle control. It’s a much more practical sales pitch than “someday robotaxis.”

Who founded RoshAi and what has it built so far?

A founder who’d already built a self-driving car years ago

Dr. Roshy John didn’t come into this category as a first-time tourist. Long before RoshAi was formally registered in 2021, he was already known in India’s robotics circles for building an autonomous Tata Nano prototype and for his work in robotics at TCS. His interest in self-driving systems goes back to a near-fatal cab ride from the airport around 2010. That became the trigger for years of experimentation in simulation, sensors, and real vehicles.

His résumé helps explain why investors took the meeting. John previously served as Global Head of Robotics at TCS and earlier worked as a senior scientist at LG Electronics. He holds a PhD in robotics from NIT Tiruchirappalli, has been credited with dozens of international patents and patent applications, and has spent years building AI and automation systems for enterprise and automotive use cases.

Why Rajaram Moorthy fits the CTO seat

Co-founder and CTO Rajaram Moorthy brings the engineering depth. RoshAi describes him as a robotics and AI veteran with 19+ years of experience, 3 international patents, and prior work as a chief architect and director of AI and robotics serving more than 100 global customers. That kind of background matters in autonomy, where the hard part usually isn’t the demo. It’s making perception, control, hardware integration, and fail-safes work together in ugly conditions.

What RoshAi has actually executed

The company’s timeline shows that the 2021 incorporation was late in the story, not the start of it. RoshAi traces its work back through simulation in 2010, early Tata Nano prototypes, a public Level 3 demo in 2018, BMW-based validation work, a cloud-hosted autonomous vehicle platform in 2022, a driverless test fleet in 2023, and OEM development partnerships by 2024. That’s not proof of commercial scale yet. But it does show this team has stayed on the problem for a long time.

The early commercial signals are decent for a deeptech company at this stage. RoshAi is already working with industrial operators across ports, mining, and logistics through pilots and early deployments. It has tier I OEM partnerships and repeat customers. It has logged more than 1 lakh km of testing with zero safety incidents, alongside a growing patent portfolio. Its model is also unusual: an “Android-for-autonomy” approach where software gets licensed to OEMs and fleet operators while hardware is deployed across existing fleets.

The round, and what the money is for

This new ₹22 crore round takes RoshAi’s total funding to about $3.4 million, including a $1 million round in 2024 led by Ev2 Ventures and Caret Capital. The fresh capital is earmarked for the core autonomy stack, perception systems, fleet management software, customer deployments, team expansion, and a push into the US and other overseas markets. Part of the round is set aside as working capital and runway for the next 9 to 12 months.

Where RoshAi sits against competitors

This isn’t an empty category. Minus Zero has shifted toward AI-powered ADAS and autopilot software for on-road OEM programs, including work with major vehicle makers. Ati Motors is building autonomous mobile robots for factories and warehouses, which puts it closer to industrial automation than vehicle retrofit. Flo Mobility has worked on vision-based autonomy and autonomous repositioning systems, while Swaayatt Robots has focused on autonomous driving in highly complex Indian traffic.

RoshAi’s wedge is narrower, and that’s probably the point. It’s targeting confined industrial environments where regulations are easier, operational design domains are more controlled, and retrofitting old fleets is cheaper than replacing them. Legacy alternatives here aren’t fancy software companies. They’re human drivers, manually managed yards, and custom one-off automation projects that don’t scale cleanly from one site to the next.

Why are investors backing RoshAi’s industrial autonomous vehicles?

Because the company is trying to sell something customers can adopt now, not after India rewrites road laws for robotaxis.

IAN’s thesis is pretty clear from the deal: retrofit-first autonomy is easier to buy than a full fleet replacement. That matters a lot in industrial settings, where operators care less about brand-new vehicles and more about uptime, safety, and predictable operating costs. RoshAi also isn’t spending this round on vague category-building. It’s spending on the exact pieces that turn pilots into contracts — autonomy software, perception, deployments, sales coverage, and hiring.

There’s a geographic signal here too. Expanding into the US and other international markets this early sounds ambitious, maybe even a little aggressive, but it makes strategic sense if RoshAi wants to sell a platform rather than stay a services-heavy local integrator. Deeptech companies can get stuck if they only prove the tech and never build the go-to-market muscle. This round is meant to stop that.

How big is the India market for industrial autonomous vehicles?

The narrow opportunity RoshAi is chasing is getting real traction because controlled environments are simply easier. Ports, mining sites, warehouses, airports, and industrial campuses don’t have the same regulatory and road-chaos burden as open consumer driving. That’s why most early Indian autonomy work is clustering there.

One estimate in the source article puts India’s autonomous mobility market at $1.3 billion by 2033, expanding at a 20% CAGR, with off-road, industrial, and logistics use cases doing a lot of the heavy lifting. The broader market is moving fast too. IMARC pegs the overall India autonomous vehicle market at $3.23 billion in 2025 and projects it to hit $23.91 billion by 2034, with software and services holding a 55% share, Level 4 systems at 45%, and transportation and logistics accounting for 70% of the market in 2025.

That breakdown lines up with RoshAi’s strategy almost perfectly. Software-heavy deployments in controlled domains. High-automation levels where routes are known. Logistics and industrial transport first. Because that’s where the economics work first in India.

Can RoshAi turn industrial autonomy into exports?

RoshAi still has a lot to prove. Pilots need to become repeatable deployments. International expansion needs more than a slide deck. And industrial autonomy is unforgiving — one safety failure can wreck years of trust.

Still, this is one of the more grounded industrial autonomous vehicles stories in India right now. The company has founders with real history in robotics and a retrofit-first product that matches how industrial buyers spend. It also has a fresh round sized for execution rather than hype. The next thing to watch is whether RoshAi can convert its early OEM ties and pilot work into steady, scaled fleet rollouts over the next 12 months.

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FAQ

What is the latest funding raised by RoshAi?

RoshAi has raised ₹22 crore, or about $2.4 million, in a round led by IAN Group through IAN Alpha Fund. With that raise, the startup’s total funding is now about $3.4 million, including a $1 million round completed in 2024.

How does RoshAi’s product work?

RoshAi combines retrofit drive-by-wire hardware, an in-vehicle autonomy stack, and cloud fleet software to make existing commercial vehicles operate without drivers in controlled environments. The system handles perception, planning, control, and fleet monitoring. That means operators can upgrade working vehicles instead of buying a brand-new autonomous fleet.

Who are the founders of RoshAi?

RoshAi was founded in 2021 by Dr. Roshy John and Rajaram Moorthy. John previously led robotics work at TCS and worked at LG Electronics, while Moorthy came in with nearly 2 decades of robotics and AI experience and multiple patents tied to autonomy and intelligent systems.

What market is RoshAi operating in?

RoshAi sits in the industrial autonomy and autonomous mobility category, with a focus on commercial vehicles used in ports, mining sites, airports, and industrial yards. That’s a very different market from consumer self-driving cars, because controlled environments usually allow faster deployment, clearer ROI, and fewer regulatory roadblocks.