Category: Startup Funding News

Orbio builds AI agents that recruit, onboard, and support frontline workers for large employers. The company has raised a $21 million Series A led by Dawn Capital. The funding is a strong vote of confidence in a segment of enterprise software that still relies on spreadsheets, phone calls, and disconnected tools.

Orbio was founded in 2025 by Sergi Bastardas, Nacho Travesí, and Antonio Melé. The startup believes deskless workers need software that reaches them directly instead of relying on HR dashboards that frontline employees rarely use.

What does Orbio AI actually do?

Orbio AI is an AI-native HR system for enterprises with large frontline workforces. In plain English, it tries to run the messy parts of hiring and workforce management through specialized agents that sit across the employer’s existing stack, from job posting to onboarding to retention insights. Those agents work with tools such as Workday, Lever, Salesforce, BambooHR, Bullhorn, and Teamtailor rather than forcing customers to rip everything out and start over.

María is the recruiting side of the system. She can generate job descriptions and push them across job boards. She also centralizes incoming applications, screens candidates through phone, voice, and WhatsApp interviews, and then schedules qualified people directly with hiring managers. That matters because high-volume hiring usually dies in the handoff between sourcing, screening, and scheduling. Drop-off piles up fast.

Daniel handles onboarding. He collects documents and checks compliance. He also builds role-specific onboarding checklists, answers new-hire questions through chat, voice, and WhatsApp, and stays active through the employee’s first few days. Orbio’s retention agent, spelled Clare on the company’s own site, keeps the loop going with daily engagement conversations and dashboards for managers. It also handles follow-up actions and AI-led exit interviews designed to spot why people leave before the next wave does.

And the product pitch isn’t just “AI assistant” fluff. Orbio is trying to own the connective tissue between recruiting, onboarding, and retention by turning conversations, documents, and feedback into structured data that feeds the next step in the workflow. It also leans hard on enterprise-readiness: GDPR alignment, EU AI Act alignment, ISO 27001 certification, and SOC 2 Type II auditing. Those boxes matter.

Who founded Orbio AI and what’s the backstory?

The founding story

This one didn’t come out of a lab. It came out of operations pain. Bastardas spent about a decade at Amazon and then co-founded Colvin, where he saw how badly frontline-heavy businesses still handled the human side of operations. That experience pushed him to start Orbio in 2025 with Travesí and Melé, aiming at the parts of HR that are repetitive, fragmented, and still weirdly manual for companies that hire at scale.

Why this team fits the problem

Bastardas brings the operating background. He helped launch Amazon in Spain and later co-founded and led Colvin, scaling it into a fast-growing tech business that raised more than €90 million and tripled revenue annually. That’s not HR pedigree in the traditional sense, but it is the kind of large-scale execution background you’d want if your customers are running logistics, healthcare, hospitality, or retail teams across many locations.

Travesí is the closest thing here to a category insider. Before Orbio, he co-founded Cobee and helped scale the employee-benefits platform internationally until its sale to Pluxee. Melé brings the technical startup record: he previously founded and served as CTO of Nucoro, a B2B wealth-management software company that was later acquired by Backbase. Put those three resumes together and Orbio starts to make more sense. Operator, HR-tech builder, product architect.

Early traction and fundraising

Orbio isn’t a concept-stage company anymore. Its software is already being used by customers including Poke and YUM! Brands, and Bastardas has said some buyers are moving from pilot programs into full deployments. One of the clearest proof points so far is The Stepping Stones Group, where Orbio now runs the company’s full U.S. operation and has helped lift the share of candidates who make it through to hiring by 20%.

On the funding side, the headline is the new Series A. Dawn Capital led the $21 million round, and Orbio has now raised $26 million in total, with earlier backing from investors including Visionaries and 2100 Ventures. The fresh money is earmarked for hiring and building more AI agents. That suggests the wedge is working and the next job is product expansion, not survival.

How Orbio AI compares with Paradox, WorkJam, and old-school HR ops

Orbio has real startup competition, but it isn’t going after the market in exactly the same way. Paradox is best known for conversational hiring automation and its ATS product for high-volume employers. WorkJam is broader frontline operations software, covering areas like communication, task management, scheduling, learning, and employee self-service. Orbio’s pitch is narrower than WorkJam’s on day-to-day workforce operations, but broader than a recruiting-only tool because it wants one agent layer to cover hiring, onboarding, engagement, and exit feedback.

Bastardas is probably right that the biggest rival is still the old way of doing things. In frontline-heavy sectors, HR teams often juggle disconnected ATS tools, phone screens, spreadsheets, compliance paperwork, store managers, and messaging apps all at once. Orbio’s differentiation is that it tries to turn that chaos into a continuous workflow, with agents passing signals between stages instead of leaving HR to manually stitch the story together. That’s ambitious. And honestly, a little risky. Enterprise buyers love the vision, but they only keep paying if the automation survives contact with labor operations.

Why Orbio AI’s Series A matters

This round matters because Orbio seems to be past the “nice demo” phase. When a customer is willing to let a young vendor run a full U.S. operation, the question changes from “does the product work?” to “can the company scale delivery, integrations, governance, and trust fast enough?” That’s a much better problem for a startup to have.

The product roadmap lines up with that shift. Orbio already has recruiting, onboarding, and retention agents in market, so using new capital to build more agents suggests it wants to become a broader system for frontline HR operations rather than a single-use automation tool. You can also read Dawn Capital’s lead role as a bet that enterprise customers are now ready to buy agentic HR software, not just experiment with it, as long as it plugs into existing systems and meets compliance requirements. That last part is boring. It also matters a lot.

How big is the market for AI HR software?

The timing isn’t random. BCG puts deskless workers at 70% to 80% of the global workforce, about 2.7 billion people, and those are exactly the employees most likely to be underserved by corporate HR systems built around email, desktop software, and centralized admin teams. If you’re building for healthcare, retail, logistics, and hospitality, that’s the opportunity. It’s huge.

Software spend is catching up. Grand View Research estimated the global AI-in-HR market at about $3.25 billion in 2023 and projects it to reach roughly $15.24 billion by 2030, a 24.7% compound annual growth rate. Gartner has gone even more bluntly directional, predicting that by 2030, half of HR’s work will be done by AI. That doesn’t mean every HR team wants autonomous agents tomorrow. It does mean the window for companies like Orbio is open right now, especially if they can serve the messy frontline layer most incumbents still treat as an afterthought.

What to watch from Orbio AI next

Orbio AI is chasing a real problem with a team that’s credible enough to get a serious hearing. The thing to watch now isn’t whether AI can help with frontline hiring. That part’s already getting proven. It’s whether Orbio can turn three useful agents into durable enterprise infrastructure, and whether customers let it move from workflow assistant to actual operating layer. Bastardas calls this “their AI moment.”

Read how Theker raised an $85M Series A led by CRV to build AI-powered industrial robots that can be reconfigured across warehouse and factory operations, helping manufacturers automate changing workflows without relying on fixed automation systems.

FAQ

• What funding did Orbio AI raise? Orbio AI raised a $21 million Series A announced on June 15, 2026, and Dawn Capital led the round. The company has raised $26 million in total so far, with previous backing from investors including Visionaries and 2100 Ventures.
• How does Orbio AI work for frontline hiring and onboarding? Orbio AI uses specialized agents to run different stages of the employee lifecycle. María handles job creation and screening. She also manages interviews and scheduling. Daniel manages onboarding documents, compliance, and first-day support, while Clare focuses on engagement, retention signals, and exit interviews across channels like WhatsApp, voice, chat, and email.
• Who founded Orbio AI? Orbio AI was founded in 2025 by Sergi Bastardas, Nacho Travesí, and Antonio Melé. Bastardas previously helped launch Amazon in Spain and co-founded Colvin, Travesí co-founded Cobee before its sale to Pluxee, and Melé previously founded Nucoro, which was acquired by Backbase.
• Is Orbio AI a recruiting tool or HR software? It’s closer to AI-native HR software for large frontline workforces than a single recruiting app. That matters because the addressable market sits at the intersection of a 2.7 billion-person deskless workforce and an AI-in-HR software category projected to grow to about $15.24 billion by 2030.

Theker builds AI-powered industrial robots that can be reconfigured for different warehouse and factory jobs. The Barcelona startup has raised an $85 million Series A led by CRV. The funding reflects growing demand for flexible industrial automation.

Factories and logistics operators still face labor shortages. Many automation systems struggle when products, packaging, or workflows change.

Founded by engineers Carla Gómez Cano and Jiaqiang Ye Zhu, Theker is building adaptable AI-powered robots. The company focuses on flexibility instead of fixed machines or humanoid-style demonstrations.

What is Theker robotics and how does it work?

Theker sells general-purpose industrial robotics for environments where no 2 shifts look exactly alike. Its systems combine reconfigurable hardware with AI-driven perception, so a customer can swap or resize hands, arms, and parts of the robot’s form factor depending on the job—sorting parcels, packing garments, or handling bottles and cans—without starting from scratch every time. The company’s pitch is that its robots can be deployed in days and keep learning while they operate. They can work through mixed product references and irregular shapes without constant manual reprogramming.

That matters because Theker isn’t selling a single fixed robot. It’s selling a stack. The perception layer uses deep-learning-based object detection that can identify items of different shapes, sizes, and categories, while its systems are built to work in very different settings—from cleanrooms to rougher industrial sites. The business model is Robot as a Service. It lowers the upfront hit for customers that want automation but don’t want to buy, integrate, and maintain a bespoke machine from day 1.

A customer experience with Theker looks a lot more like configuring a platform than buying a one-off robot cell. The company customizes the robot to a specific workflow and integrates the AI models that handle perception and autonomy. Then it keeps improving the system over time. That shows up in its hiring too. Theker is recruiting deep learning engineers for computer vision, natural language processing, and decision-making systems that run in real time on robotic hardware.

Before that kind of setup, a warehouse or factory team often has humans covering edge cases because standard automation can’t cope with variation. Afterward, the goal is steadier throughput on jobs that used to fall back to manual handling whenever object types, packaging, or layouts changed. That’s the real promise here: not humanoid theater, but less operational fragility.

Who founded Theker robotics and why are investors betting on it?

From student robotics to a Barcelona startup

Theker came out of years of hands-on robotics work by Carla Gómez Cano and Jiaqiang Ye Zhu before the company itself became a funded startup. EIT Manufacturing says the pair grew up building and competing with robots and later created PUCRA, the robotics association at the Polytechnic University of Catalonia, or UPC. Gómez Cano has said that community convinced them Barcelona had the talent to build a global robotics company instead of just producing talent for someone else’s.

That origin story matters because it doesn’t read like founders jumping on an AI trend late. It reads like robotics people who finally got better software tools—and better timing. UOC profiled Gómez Cano in 2023 as a cofounder working in robotics, AI, and computer vision, right when generative AI was making the broader market pay attention to embodied systems again.

Why the founders fit this market

Theker’s founder-market fit is pretty direct. Both founders are engineers with a robotics background, and Jiaqiang Ye Zhu also appears as a co-author with Gómez Cano on a 2024 paper about in-context learning for robotics control with feedback loops. That doesn’t prove commercial execution on its own. But it does show the team is operating close to the technical frontier on robot control, not just packaging off-the-shelf hardware with a slick sales deck.

Gómez Cano has also been public for a while about 1 stubborn robotics problem: cost and usability. In earlier interviews, she pointed to Robot-as-a-Service as one way to get past the capital barrier that stops many companies from adopting robots in the first place. That thread runs straight into Theker’s current model.

Traction, team growth, and the new round

Theker’s early signals are stronger than the average robotics startup boasting about pilots. Inditex, Zara’s parent, backed the company early and validated its tech in high-variability logistics work. EIT Manufacturing said Theker had already raised a €21 million seed round and employed just over 20 specialists, with plans to grow 4x to 5x over the following year. In the newer financing, Theker said it had received 15,000 job applications and could expand from a team in the dozens to as many as 120 people by the end of 2026.

CRV led the new $85 million Series A, with backing that includes Samsung and Aglaé Ventures, the investment vehicle linked to LVMH chairman Bernard Arnault. Samsung isn’t a customer yet, but Gómez Cano said the companies are in advanced discussions. That’s a pretty important detail. A robotics startup getting the same company as supplier, customer, and investor would have real industrial credibility, not just cap-table sparkle.

How Theker robotics compares with rivals

Theker’s competitive position sits between 2 established camps. On one side, you’ve got traditional industrial automation vendors that are great when the task is stable and predictable—same item, same box, same movement. On the other, you’ve got humanoid-robot companies pushing a fixed body plan into factories and warehouses. Theker is arguing that both approaches can struggle when workflows change a lot and product variability is high.

That’s why the modular hardware matters so much. Theker isn’t insisting the human form is always the right answer. It’s treating the robot more like configurable infrastructure. That’s a less cinematic story than a humanoid walking across a demo floor. It may also be a more useful one for operators who care about uptime, deployment speed, and whether a robot can switch from garments to parcels without becoming an engineering project again.

Why does Theker robotics raising $85M matter?

Because Theker says it “didn’t build” the company to run endless pilots, and this round gives it the money to prove that point. The startup already has a showroom in central Barcelona and plans to open more as it expands across Europe, the U.S., and Asia. It also plans to hire across engineering and deployment. Sales too.

The round also says something about investor appetite. Hardware investors usually want signs that a robotics company can survive real-world complexity, not just lab demos. Inditex’s involvement, Samsung talks, and the focus on operations departments rather than innovation teams all point to a company trying to sell into budget owners with immediate problems. That’s a harder path. It’s also the only one that really counts.

There’s also a local angle. Gómez Cano has been blunt that Barcelona hasn’t slowed the company down, and Theker is keeping its HQ there after raising more than double its original target. For Europe’s deep-tech scene, that’s not a trivial signal. A lot of founders still assume serious robotics has to migrate early. Theker is betting the opposite.

How big is the market for Theker robotics?

The timing isn’t random. The International Federation of Robotics says U.S. factories had 393,700 industrial robots in operation in 2024, while annual installations reached 34,200 units. IFR also points to labor scarcity and reshoring as long-term drivers for more automation, even if near-term conditions stay uneven. That’s a big installed base already. It also shows how much room there is for systems that can do more than one fixed task.

Warehouse automation is growing even faster. Grand View Research estimates the global warehouse robotics market could reach $17.29 billion by 2030, up from $4.31 billion in 2022, with a 19.6% CAGR. That lines up neatly with Theker’s first targets in logistics and retail, where mixed inventory, packaging changes, and labor shortages make rigid automation especially annoying.

The broader trend is that AI is finally making robotics less brittle. Better perception and better control loops help. Better software tooling does too. More operators are willing to try automation in workflows that used to be dismissed as “too variable.” That doesn’t mean every generalist robot startup wins. It does mean the category is no longer theoretical.

Can Theker robotics become Europe’s factory robot contender?

Maybe. But the next test won’t be fundraising.

It’ll be deployment density, customer retention, and whether Theker Robotics can turn a very ambitious modular-robot story into repeat business across warehouses and factories on 3 continents. The money is there now. So is the attention.

Read how Prometheus raised $12B from Jeff Bezos, JPMorgan Chase, Goldman Sachs, and BlackRock to build AI-powered engineering software that could help companies design, test, and manufacture complex physical products faster.

FAQ

• What funding did Theker raise? Theker raised $85 million in a Series A announced in June 2026, and CRV led the round. Samsung and Aglaé Ventures were among the backers, and the company described it as the largest robotics Series A raised in Europe.
• How does Theker robotics work? Theker robotics combines modular robot hardware with AI-based perception so customers can adapt a system to different jobs instead of buying a robot built for only 1 task. Its stack includes deep-learning object detection and customizable robot configurations. It also uses a Robot-as-a-Service model that makes deployment easier for logistics and industrial customers.
• Who are Theker’s founders? The company was founded by Carla Gómez Cano and Jiaqiang Ye Zhu, 2 engineers with roots in student robotics at UPC. They helped build the PUCRA robotics association, and Zhu later co-authored robotics-control research with Gómez Cano, which helps explain why investors take the team seriously as technical builders.
• Is Theker a warehouse robotics company or a factory robotics company? It’s both, at least by design. Theker started with logistics and retail use cases such as sorting and packing, but it’s aiming to push deeper into manufacturing, food and beverage, waste, and other industrial settings where variable manual tasks still dominate.

Prometheus builds AI software for designing and manufacturing complex physical products. The startup has now raised $12 billion at a $41 billion valuation from Jeff Bezos, JPMorgan Chase, Goldman Sachs, BlackRock, and other backers. It’s chasing a brutal bottleneck in industry: turning an idea into a manufacturable machine, device, or compound still takes years, giant teams, and a lot of expensive iteration. Bezos and Vik Bajaj launched the company in November 2025, betting that AI can compress that cycle in a way older engineering tools never could.

What does Prometheus physical AI actually build?

Prometheus is building what Bezos calls an “artificial general engineer” — software meant to help companies design physical systems and move them from concept toward manufacturable reality. Bezos has described it as a “very, very modern version” of CAD, but that undersells the ambition. The idea isn’t just drawing parts on a screen. It uses AI across design and prototyping. Testing, calibration, and pre-production work are part of it too.

That distinction matters. Bezos has been explicit that Prometheus isn’t a robotics company and isn’t mainly about automating factory floors. He’s framed it instead as software for the messy stage before mass production — where engineering teams cycle through requirements, models, experiments, and revisions until something finally works at scale. Axios described the focus as optimizing pre-production machinery and processes, including prototyping.

The pitch is that a customer could shorten the “dream-build” loop. A jet engine maker trying to squeeze out 10% more thrust, for example, might not need to grind through a decade-long program just to explore the design space. The same logic could apply beyond aerospace. Think medical devices, electronics, or even drug compounds. Bezos has already pointed to Blue Origin as the kind of customer that could benefit, though he says Prometheus stands on its own rather than sitting inside Blue or Amazon.

Who founded Prometheus physical AI and why now?

The founding story

Prometheus emerged from stealth in November 2025 with an eye-watering $6.2 billion initial raise, then followed that with this much larger Series B in June 2026. Bezos said he got so excited about the idea that he became co-CEO — his first operational role at a tech company since stepping down as Amazon CEO in 2021. The company’s core thesis is simple to say and very hard to execute: the pace of physical creation has lagged way behind the pace of software, and AI might finally close that gap.

Why Bezos and Bajaj make sense together

Bezos brings obvious industrial scar tissue. He built Amazon into a giant logistics and computing machine, still serves as executive chairman, and has spent years pushing Blue Origin through the painfully slow realities of designing hardware that can’t fail. That matters here. Prometheus isn’t trying to automate spreadsheets. It’s going after engineering work where errors are expensive and timelines can stretch into the next decade.

Bajaj is less famous, but he’s arguably the more natural fit for the technical thesis. He co-founded Google Life Sciences, later renamed Verily, and served as its chief scientific officer. After that, he became chief scientific officer at Grail before moving into Foresite Capital and then helping build Foresite Labs. That mix — hard science, applied research, and company-building — is the sort of résumé you’d want for a startup trying to merge AI with engineering workflows.

Early signals from the company

Prometheus is still unusually secretive for a startup carrying this much capital. What’s public is enough to show scale, though: the company has about 150 employees and offices in San Francisco, London, and Zurich. Bezos has also said a large share of the money will go toward compute. That tells you this isn’t a lightweight SaaS play. It’s a capital-intensive R&D effort that expects very big models and very costly experimentation.

The financing behind the bet

This new round values Prometheus at $41 billion and includes money from Bezos, JPMorgan Chase, Goldman Sachs, BlackRock, DST Global, Arch Venture Partners, and others. It follows the $6.2 billion launch financing from late 2025 and is widely described as a Series B. That’s an absurd amount of money for a company still keeping most product details hidden. Investors aren’t backing traction here so much as a theory of where engineering software goes next.

Where Prometheus sits against competitors

Prometheus overlaps with physical AI, but not in the same way as robotics-first labs. Physical Intelligence has raised $600 million at a $5.6 billion valuation to build AI software for robots, while Skild AI raised $1.4 billion at a $14 billion valuation around its general-purpose robot intelligence system. Prometheus is aiming earlier in the chain. Its real fight is with the fragmented stack of CAD and simulation tools. Prototyping and engineering review still depend heavily on human iteration and institutional memory.

Why are investors backing Prometheus physical AI?

Because this kind of company can’t be built on a normal startup budget.

A product that touches design and testing, then reaches into manufacturing, has to earn trust in ways a chatbot never does. It needs compute, domain specialists and time. Bezos has already said compute is a major use for the new capital, and that lines up with the broader thesis: if Prometheus works, it could become core infrastructure for industries where one better engineering decision can save years and billions.

There’s also a labor argument behind the round, and it’s a controversial one. Bezos has pushed back on the idea that AI automatically means mass white-collar unemployment, saying instead that AI-driven productivity could create “labor scarcity.” He put it bluntly: “Significant productivity in the economy is going to raise the standard of living.” You can disagree with that. A lot of people do. But it helps explain why Prometheus isn’t pitching itself as a cost-cutting side tool. It’s pitching itself as a way to let smaller teams take on much bigger engineering problems.

How big is the market for AI in manufacturing and engineering?

The money chasing this category isn’t random. Grand View Research forecasts the global AI in manufacturing market will reach $47.88 billion by 2030, with a 46.5% compound annual growth rate from 2025 through 2030. That’s the kind of curve investors love, especially when the buyers are large industrial companies that can spend heavily once the software proves itself.

The timing makes sense too. Manufacturers are trying to connect design, automation, analytics, and production data instead of running them as separate islands. PwC has argued that industrial winners by 2030 will be the companies that can orchestrate AI, automation, analytics, and engineering on shared workflows rather than just buying isolated tools. Prometheus is basically a giant wager that engineering itself is ready for that same integration.

Is Prometheus physical AI the next big industrial AI bet?

Maybe. But it hasn’t earned that label yet.

The valuation is massive. The secrecy is real. The product still sounds more like a thesis than a finished platform. Even so, Prometheus physical AI is trying to solve a problem that matters: the painfully slow path from idea to manufacturable thing. What matters next is whether Bezos and Bajaj can show named customers, real workflow gains, and proof that their software beats the old engineering stack on speed, cost, or both.

Read how 4baseCare raised ₹128 Cr in Series B funding to expand its genomics lab network and scale OncoTwin, an AI-powered oncology platform designed to improve cancer treatment decisions across emerging markets.

FAQ

• What funding did Prometheus announce? Prometheus announced a $12 billion Series B at a $41 billion valuation in June 2026. The round included Jeff Bezos, JPMorgan Chase, Goldman Sachs, BlackRock, DST Global, and Arch Venture Partners, following the company’s earlier $6.2 billion launch financing in November 2025.
• How does Prometheus work? Prometheus is building AI software that helps engineers move from concept through design and prototyping. It also covers testing, calibration, and pre-production planning. Bezos has described it as a next-generation CAD-like system, but the point is broader: cut down the years of manual iteration that slow hardware and industrial development.
• Who founded Prometheus? Prometheus was co-founded by Jeff Bezos and Vik Bajaj. Bajaj previously co-founded Google Life Sciences, later called Verily, served as Grail’s chief scientific officer, and went on to help lead Foresite Labs — a background that gives him credibility in complex science-heavy systems.
• Is Prometheus part of the physical AI market? Yes, but it sits in a specific corner of that market. Prometheus is part of the physical AI push because it targets engineering and manufacturing, yet Bezos has said it’s not a robotics company; the focus is the design and pre-production layer rather than training robots to act in the world.

4baseCare builds genomics-based cancer diagnostics and AI-led treatment support tools, and its latest 4baseCare funding update is a ₹128 crore Series B haul aimed at taking that model across more emerging markets. Cancer care still has a brutal data gap, especially for patients outside the datasets that shaped most precision oncology tools in the West. That’s the bet here. Founded in 2019 by Hitesh Goswami and Kshitij Rishi, the Bengaluru startup will use the fresh money to expand its genomics lab network and push harder on its oncology intelligence platform, OncoTwin.

What does 4baseCare actually sell?

4baseCare runs genomic tests that help oncologists match a cancer patient to more targeted treatment options. The workflow is direct: a hospital or doctor sends a tumor tissue sample, blood sample, or both. 4baseCare sequences the relevant material. Its platform identifies actionable mutations and biomarkers. It also flags resistance signals and related evidence. Then the oncologist gets a report built for treatment decisions rather than a raw genomics dump.

The product stack is wider than the source article suggests. Its TARGT First panel covers 92 commonly mutated genes for faster frontline decisions. TARGT Indiegene goes much deeper with a 1,212-gene panel. It includes markers such as tumor mutational burden, microsatellite instability, homologous recombination deficiency, gene fusions, and rare variants. At the top end, TARGT Absolute uses whole-exome profiling across roughly 20,000 genes.

Then there’s SoLiQ. Instead of treating tissue biopsy, liquid biopsy, and germline testing as separate jobs with separate reports, 4baseCare combines them into one analysis. That matters because cancer changes over time, tissue can be limited or stale, and blood-based ctDNA can reveal evolution or resistance that a single archived sample might miss. In plain English: fewer blind spots. Less manual stitching together of three different reports.

OncoTwin sits on top of that diagnostics layer. It doesn’t just show mutations, it tries to find “digital twins” — patients with similar clinical and genomic profiles — then summarizes treatment journeys, outcomes, match scores, and evidence quality for doctors in real time. It also supports molecular tumor board review with an integrated case workspace. That makes this more than a testing company.

Who built 4baseCare and why did investors back it?

The founding story

4baseCare was started by Hitesh Goswami and Kshitij Rishi with a specific thesis: cancer biology isn’t uniform across populations, but a lot of precision oncology still leans on datasets that underrepresent patients from India and other emerging markets. That’s not a branding line. It’s the company’s operating logic — build local genomics infrastructure, generate more relevant clinico-genomic data, and use that data to improve treatment decisions.

Why the founders fit this market

Goswami looks like the domain founder in the pair. He has more than 20 years across pharma drug discovery, genomics, and precision medicine, and earlier worked in high-throughput screening at Piramal Life Sciences. Before 4baseCare, he also co-founded Bionivid Technology, a genomics IT company built around data processing, storage, analytics, and training. That background matters because 4baseCare is a blend of diagnostics, data engineering, and oncology workflow.

Rishi brings the operator side. He’s an XLRI alumnus who held leadership roles at Deloitte and GE, and at 4baseCare he has handled partnerships and operations. He’s also led patient engagement and hospital network building. That’s useful because genomics startups don’t scale on software alone. They scale through logistics, clinician trust, lab operations, and repeat institutional relationships.

What they’ve already built

Bionivid is the clearest prior execution signal here because it shows Goswami was already working on genomics infrastructure problems before 4baseCare existed. Rishi’s earlier entrepreneurial experience plus large-company operating roles help explain why 4baseCare has pushed into cross-border lab expansion instead of staying a single-city testing outfit. It’s not flashy. But it’s credible.

Traction, labs, and hospital links

This isn’t a pre-product story. 4baseCare is already live and runs laboratories in India, Dubai, Nepal, and the Philippines. It wants to enter 8 to 10 more countries over the next 12 to 18 months. It’s also extending an in-hospital genomics lab model through partnerships with institutions including AIIMS Jammu, Max Healthcare, and Shankara Hospital. The company currently processes about 1,500 genomic tests a month. It expects that figure to rise to 8,000 to 10,000 a month as the network expands.

The 4baseCare funding details

The fresh tranche is ₹38 crore. growX Ventures and Infosys led it, and it takes total capital raised in this Series B cycle to ₹128 crore, or about $13.3 million. Earlier, 4baseCare had announced a ₹90 crore first close of the round from Ashish Kacholia, Lashit Sanghvi, and Yali Capital. The company will use the new money for global genomics lab expansion and deeper investment in OncoTwin.

There’s also some round-history context that helps. 4baseCare had previously raised ₹50 crore in Series A with Yali Capital and Infosys participating, and before that it disclosed a $2 million pre-Series A round backed by investors including growX Ventures. So this isn’t a sudden investor discovery. It’s a follow-on conviction story.

Competition and market positioning

Here’s the honest read: 4baseCare’s competition isn’t just other cancer startups raising money in India. Its real competition is fragmented pathology workflows, tissue-only testing, overseas genomic labs, and oncology teams still piecing together decisions from separate reports and literature searches. That’s slow and expensive. It’s often not designed for underrepresented populations.

Among newer players, OneCell Diagnostics is another Indian precision oncology name with a genomics-heavy approach, while startups like Everhope Oncology, MOC Cancer Care, and Oncare are attracting capital on the care-delivery side of the oncology stack rather than the diagnostics-and-decision-support layer where 4baseCare sits. That distinction matters. 4baseCare is trying to own the data and interpretation layer inside cancer treatment workflows, not build a chain of chemotherapy centers.

What does 4baseCare funding change next?

This 4baseCare funding round matters because genomics expansion is capital-heavy in a way pure SaaS isn’t. Labs, sequencing capability, validation, hospital integrations, and clinician adoption all take time and money. A ₹38 crore top-up isn’t just runway. It’s a sign investors were willing to keep financing the infrastructure piece after the initial Series B close.

It also sharpens the company’s strategy. 4baseCare isn’t trying to win by becoming another general AI healthtech story. It’s pairing local testing infrastructure with a data moat built around population-specific clinico-genomic evidence. Then it uses OncoTwin to make that evidence usable for oncologists. That combination — lab network plus decision support — likely appealed to Infosys and growX as repeat backers.

There’s an operational upside for hospitals too. If 4baseCare can push more in-hospital genomics labs instead of forcing samples into faraway reference-lab chains, turnaround times and physician engagement could improve a lot. That’s ambitious, yes. But it’s a more defensible plan than being just another interpretation software vendor.

Why is 4baseCare funding landing in a hot oncology market?

India alone recorded about 1.41 million new cancer cases and 916,827 cancer deaths in 2022, with 5-year prevalence above 3.25 million people. That scale explains why precision oncology isn’t some niche luxury category anymore. The need is already here, especially when treatment decisions increasingly depend on biomarkers, mutation profiles, and resistance patterns rather than organ-of-origin labels alone.

The commercial side is big too. Grand View Research estimates the global precision oncology market at $115.8 billion in 2024 and projects it to reach about $201.96 billion by 2030. IMARC pegs India’s genomics market at $2.5 billion in 2025 and forecasts it at $9.5 billion by 2034. Those are big numbers, sure. But the more useful takeaway is that diagnostics, sequencing, and personalized medicine are attracting real infrastructure capital, not just research interest.

And demand won’t ease. Global cancer cases are projected to rise to 35 million a year by 2050, up from around 20 million in 2022. That’s why investors keep circling oncology even when broader startup sentiment gets shaky. The clinical need is rising, and the data layer in cancer care is still badly underbuilt outside rich healthcare systems.

Final take on 4baseCare funding

The interesting thing about 4baseCare funding isn’t just the ₹128 crore total. It’s that investors are backing a harder model — one that mixes wet-lab execution, hospital partnerships, and AI-driven oncology software in markets that global precision medicine companies have often underserved. The next thing to watch is simple: can 4baseCare turn its current lab footprint and 1,500-test monthly base into a much larger, cross-border genomics network without losing clinical depth or speed?

Read how Coram AI raised a $35M Series B co-led by Ansa Capital and Battery Ventures to turn existing cameras, access systems, and visitor logs into an AI-powered physical security platform that helps teams investigate incidents in plain English.

FAQ

• What is the latest 4baseCare funding round?
  4baseCare has closed its Series B at ₹128 crore, including a fresh ₹38 crore top-up led by growX Ventures and Infosys. That came after a ₹90 crore first close backed by Ashish Kacholia, Lashit Sanghvi, and Yali Capital. This round was built in stages rather than in one headline cheque.
• How does 4baseCare’s cancer platform work?
  It starts with genomic testing from tumor tissue, blood, or both, and then turns that sequencing data into treatment-oriented reports for oncologists. Its SoLiQ approach combines tissue DNA, ctDNA from blood, and germline DNA in one analysis. OncoTwin adds a second layer by matching a patient to similar clinico-genomic cases and summarizing treatment outcomes.
• Who founded 4baseCare?
  4baseCare was founded by Hitesh Goswami and Kshitij Rishi. Goswami came in with deep genomics and drug-discovery experience, including work at Piramal Life Sciences and a prior genomics IT venture called Bionivid Technology, while Rishi brought strategy, operations, and partnership-building experience from roles at Deloitte and GE.
• What market is 4baseCare operating in?
  It sits in precision oncology, with one foot in cancer genomics diagnostics and the other in AI-based clinical decision support. That puts it inside two fast-growing categories at once: the global precision oncology market, estimated at $115.8 billion in 2024, and India’s genomics market, estimated at $2.5 billion in 2025.

Coram AI builds software that turns existing cameras, doors, and visitor logs into one AI-powered physical security system.

That makes Coram AI funding worth paying attention to. The California-headquartered startup has raised a $35 million Series B co-led by Ansa Capital and Battery Ventures. The company wants to move security teams beyond after-the-fact video reviews and toward AI-led investigations.

Co-founder and CEO Ashesh Jain, an IIT Delhi alumnus, started Coram around 2022. He describes the company’s core belief simply: most security tools “just record what happened.”

The new funding will support AI product development, go-to-market expansion, and customer success efforts. Coram also plans to expand its engineering footprint in Bengaluru and hire across AI, software engineering, and product development.

What does Coram AI actually sell?

At the product level, Coram is now much more than an AI video search tool. The company sells a unified physical security platform that combines video security and access control. It also covers emergency management and guest management in one system. A customer can keep existing IP cameras, connect them to Coram, and then search footage or site activity in plain English instead of scrubbing timelines by hand. More than 1,500 locations already run on that platform, and Coram’s architecture works without a costly camera rip-and-replace.

The newest piece is Deep Investigation, which is basically Coram’s pitch for autonomous security work. A user asks a multi-step question in natural language — across cameras, access events, locations, and time windows — and the system reconstructs what happened. It builds a synthesized timeline and returns an evidence-backed answer or shareable report. That’s a lot closer to an analyst workflow than a traditional video management system.

Coram’s other modules fill in the rest of the building. Its access-control product links door events to live and recorded video, so admins can pull up the clip tied to a badge event from the same dashboard. Guest management runs on iPads at entry points, captures visitor info, and can verify a driver’s license in under 2 seconds. It prints badges and alerts hosts automatically. Tailgating alerts tie access-control events to camera footage, so the system can flag when more people passed a door than the valid credential events would suggest.

There’s also a technical angle here that investors clearly like. Coram’s edge devices run current AI models on NVIDIA GPUs where the data is generated, which lets customers use more advanced AI without sending sensitive video to the cloud. That won’t solve every privacy debate around workplace and school surveillance. But it gives Coram a cleaner answer than vendors that ask buyers to replace hardware first and sort the architecture questions later.

Who founded Coram AI and how has the company grown?

The founding story

Coram was started about 4 years before this June 2026 fundraise, which puts the company’s founding around 2022. Jain’s argument is simple: physical security is still reactive, and teams waste hours reviewing footage, access logs, and disconnected systems after something has already gone wrong. Coram’s answer is to centralize those workflows in one AI-native platform instead of making security staff bounce between siloed tools.

That origin story also shows up in Jain’s own wording. He said, “Most security systems just record what happened,” and only later might someone find the incident after a manual search. That’s not just marketing copy. It’s the core complaint behind the product.

Why Ashesh Jain fits this market

The clearest window into Jain’s background comes from his own explanation of why Coram exists. He said Coram’s team spent years building AI that helped cars “read a scene and act before someone gets hurt,” and now they’re applying that same logic to schools, hospitals, and workplaces. So the founder-market fit here isn’t traditional security-industry pedigree. It’s perception AI, computer vision, and decision systems brought into physical infrastructure.

He also looks deeply involved in the product side, not just the fundraise side. Since 2023, Jain has authored technical posts tied to Coram’s AI-first roadmap, including work around GPT-powered support and intelligent video search. He’s also written about authenticity checks for AI-generated media. That matters because Coram isn’t trying to win on camera hardware alone. It’s trying to win on how intelligence sits on top of that hardware.

Traction and fundraising details

Coram has posted a 4x jump in revenue and tripled its customer base since raising its $13.8 million Series A last year. Since that earlier round, it has expanded from a video-security product into a broader platform spanning video, access control, emergency management, and visitor management. The June 2026 Series B lifts total funding to $66 million.

Ansa Capital and Battery Ventures co-led this new round, with participation from UP.Partners, 8VC, and Mosaic Ventures. Coram will put the capital toward AI product work, bigger go-to-market and customer-success teams, and a larger engineering base in India. The Bengaluru office is a big part of that plan, with hiring slated across AI, software engineering, and product development.

How does Coram AI compare with Verkada, Ambient.ai, and older systems?

Coram isn’t alone here. Verkada sells a cloud-managed physical security stack that combines enterprise video security and access control, and it has built AI directly into its own cameras. Ambient.ai is pitching “agentic physical security,” with real-time monitoring and advanced forensics. It also focuses on threat detection and alarm correlation between video and access systems. Those are serious, well-defined competitors.

Coram’s differentiation is less about being the only AI story and more about deployment posture. It keeps hammering on compatibility with existing IP cameras, unified workflows across video and doors, and a model architecture that runs locally on edge hardware. In one customer case, Coram beat alternatives including Spot AI because buyers preferred its open platform and scalability. Against old-school incumbents, the pitch is even clearer: fewer portals, less manual review, and no need for someone to sit on-site just to make the system useful.

Why does Coram AI funding matter right now?

A lot of startup rounds are just runway extensions with a nicer press release. This one feels more tied to an actual product inflection.

Since the Series A, Coram has already stretched from AI video search into access control, emergency workflows, guest management, and now Deep Investigation. So the Series B isn’t about proving there’s a wedge. It’s about seeing whether that wedge becomes a full operating layer for physical security teams. The use of funds lines up with that: more product depth, more customer-facing muscle, and more engineering capacity in India to ship faster.

Investor language tells you what they think they’re buying. Allan Jean-Baptiste of Ansa Capital called physical security “one of the largest industries yet to be transformed by modern AI,” and said Coram’s founders combine frontier AI expertise with conviction about where the market is headed. Strip away the VC polish, and the thesis is pretty clear: buyers don’t just want better cameras anymore. They want systems that can interpret, summarize, and escalate without a human doing every step.

Is the market big enough for Coram AI?

Yes. And it’s not a niche bet.

Grand View Research estimates the global video surveillance market was worth $83.48 billion in 2025 and projects it will reach $204.68 billion by 2033, an 11.7% CAGR from 2026 to 2033. Its separate forecast for AI in video surveillance is even steeper: $6.51 billion in 2024, rising to $28.76 billion by 2030 at a 30.6% CAGR. That gap matters. It suggests the overall market is huge, but the AI-heavy slice is where spending is accelerating fastest.

The tech shift also favors companies like Coram. IP video systems already held more than 55.7% of market revenue in 2025, and cloud-based surveillance adoption is rising because buyers want remote access and less infrastructure drag. They also want scalability. That doesn’t guarantee Coram wins. But it does explain why a startup promising AI on top of existing hardware, instead of a painful rebuild, can get attention now.

What to watch after Coram AI funding

The real test after Coram AI funding isn’t whether the company can add one more AI feature. It’s whether Deep Investigation, access control, guest management, and emergency response start behaving like one sticky operating system for buildings instead of a bundle of adjacent tools.

Watch 3 things next. First, whether Bengaluru hiring speeds up product releases. Second, whether Coram keeps landing bigger multi-site customers without forcing hardware replacement. Third, whether this “autonomous security agent” story translates into durable revenue growth, not just sharp demo moments.

Read how Equal AI raised a $30M Series B led by Prosus Ventures and Tomales Bay Capital to turn call screening into an AI-powered assistant that answers unknown calls, understands intent, and helps users manage conversations before they pick up.

FAQ

• What is Coram AI funding and who backed the round? Coram AI funding refers to the startup’s new $35 million Series B round announced in June 2026. Ansa Capital and Battery Ventures co-led the round, and it brings Coram’s total funding to $66 million.
• How does Coram AI work for physical security teams? Coram AI works by connecting existing IP cameras, door systems, alerts, and visitor activity into one platform. Teams can search video in plain English, investigate incidents across time and locations with Deep Investigation, and tie access or guest events directly to video evidence.
• Who founded Coram AI? Ashesh Jain co-founded Coram AI and serves as CEO. He’s an IIT Delhi alumnus, and his own description of the company points to a background in AI systems that helped cars interpret scenes and respond before harm occurred, which carries straight into Coram’s security-first product design.
• Is Coram AI a video surveillance company or a broader security platform? It’s broader now. Coram started from AI-enhanced video security, but by June 2026 it had expanded into access control, emergency management, and guest management, which puts it in the wider physical security platform category rather than a single-purpose surveillance tool.