Author: Woodenscale AI

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.

Equal AI Call Assistant is an Android app that answers unknown calls for you, works out why the person is calling, and shows you the reason before you decide what to do. In a market like India — where spam, scam, delivery, hiring, and financial sales calls can pile up fast — that’s a more useful promise than plain caller ID. The startup has now raised a $30 million Series B led by Prosus Ventures and Tomales Bay Capital, a sign investors think call screening is turning into a real consumer product category, not just a phone feature. Equal was founded in 2022 by Keshav Reddy, and this new round pushes its total funding past $42 million.

What is Equal AI call assistant and how does it work?

Equal AI Call Assistant sits between you and an unknown caller. When a call comes in, the app can answer on your behalf, ask the caller why they’re calling, and show you that interaction live. A caller card appears. You can monitor the conversation through real-time transcription, and the full record later lives inside the caller screen with a summary, recording, and conversation history.

That makes the experience less like old-school spam blocking and more like having a tiny receptionist on your phone. You can jump in if the call matters. Or stay hands-off if it doesn’t. If you want to reply without speaking, the app offers preset messages and custom typed responses that the AI reads back to the caller. That covers practical moments, like asking a delivery person to leave a package at the door or hand it to a neighbor.

Equal has also built a demo flow instead of forcing users to wait for a real call. The app includes “Talk to Your Assistant” and “Call Now,” which let users test how the assistant behaves before trusting it with live callers. It runs in the background and notifies you when it has stepped in. It also depends on proper permissions, a decent connection, and the right SIM setup to work cleanly. Reliability matters.

Under the hood, Equal uses a mix of speech recognition, automatic speech recognition, speech generation models, and its own orchestration layer. The tricky bit in India isn’t just English. It’s code-mixing — callers jumping between English and local languages in the same breath — and Equal supports more than 10 languages with that behavior in mind.

Who founded Equal AI and why did it start with calls?

From identity infrastructure to consumer calls

Equal was founded in 2022 by Keshav Reddy, with Rajeev Ranjan and Krishna Prasad Atluri as co-founders. The company didn’t begin as a consumer calling app. It started as a data-sharing and identity infrastructure business for financial services, and it still sells tools tied to financial analysis and KYC verification. It also handles fraud checks and employer use cases. Earlier in its B2B life, Equal built around identity rails and large API connectivity for regulated industries.

Reddy’s explanation for the pivot is pretty direct. “We always wanted to be a customer-facing company,” he told TechCrunch, and calls were the first obvious wedge because Indians get bombarded by outreach tied to loans, insurance, jobs, and deliveries. He used one sharp example: if you’re buying car insurance, you might get “20 calls over a week.” That’s not a discovery problem. It’s a fatigue problem.

Why Keshav Reddy had a shot at building it

Reddy comes from the family behind GVK, the Indian conglomerate with interests across infrastructure, energy, and healthcare. He’s a University of Michigan graduate with an MBA from MIT, worked in airport and pharmaceutical businesses before starting Equal, and has also invested in more than 30 software companies through the Reddy Family Office. That doesn’t automatically make someone a great consumer founder. But it does explain why he’d be comfortable building in regulated, trust-heavy categories like identity and communications.

The wider founding team fills in the product gap. Rajeev Ranjan previously served as director of engineering at Swiggy, and Equal describes Krishna Prasad Atluri as having helped build one of India’s largest financial services platforms. That mix matters. Consumer calling products need hard engineering, not just a clever AI wrapper.

Traction, fundraising, and where the company sits against rivals

The app launched last year on Android and has already crossed 1 million monthly active users and 300,000 daily active users. That’s real traction for a behavior-change product. Most people don’t casually hand phone access to a startup unless the pain is obvious.

On the financing side, Equal has raised $30 million in Series B funding led by Prosus Ventures and Tomales Bay Capital, with Think Investments and Valiant Fund also participating. Individual backers in the round include PhonePe founder Sameer Nigam, Zubin Bharti Mittal from Airtel Family Office, Skyflow AI co-founder Anshu Sharma, Meta India and Southeast Asia VP Sandhya Devanathan, and CtrlS Datacenters chairman Sridhar Pinnapureddy. The company says total funding now tops $42 million.

This round has an unusual setup. It’s split into 3 tranches, and the valuation changes depending on whether Equal hits preset targets. That structure isn’t common. It also creates an odd headline advantage: a startup can talk about the highest valuation reached even if most of the equity sold earlier at a lower price. Equal declined to disclose the actual valuations.

Competition is stiff, and the lines are blurry. Google’s Phone app can use Call Screen to ask why someone is calling. It can also generate AI replies and save transcripts of screened calls. Apple now offers call screening for unknown callers on iPhone and can ask for the caller’s name and reason before the phone rings. Truecaller, the incumbent in caller ID and spam protection, averaged 433 million monthly active users in late 2024 and has been adding AI call recording and transcription in India. In the U.S., Cloaked has also pushed into screening. Equal’s difference is that it’s building for India-first behavior — multilingual, code-mixed, delivery-heavy, finance-heavy — and trying to be useful after the name lookup, not just before it.

Why does this Equal AI funding round matter?

Because this round is about expanding the product from filter to agent.

Right now, Equal screens unknown calls. Next, it wants to screen known numbers too, send consented texts like your address to a delivery person, make outbound calls to book appointments, launch on iOS, and introduce a paid tier with more features. If it pulls that off, the app stops being a defensive spam tool. It starts looking more like a voice-based personal assistant with very narrow, very practical jobs.

That’s also why Prosus is here. Thiago Viana, the firm’s global co-head, argued that Equal’s local context gives it an edge. That isn’t just VC flattery. A call assistant in India has to handle mixed languages, informal phrasing, business verification, scam suspicion, and the fact that many important calls still come from unknown numbers. Generic U.S. call-screening logic won’t cover all of that.

There’s a second angle too. Prosus has also backed local-market assistant players like Luzia in Spain and Zapia in Latin America. Both got caught in Meta’s crackdown on third-party AI bots on WhatsApp. Equal is trying to avoid that trap by building around phone calls inside its own app rather than depending on a messaging platform it doesn’t control.

Why is India ripe for AI call screening apps?

India had about 1.22 billion total telephone subscribers as of July 31, 2025. That’s the base layer here. Even a niche utility can become huge when the addressable communications surface is that large. And smartphone-based services keep absorbing more daily tasks — banking, deliveries, hiring, insurance, healthcare — which means the phone number is still a frontline identity channel, not some relic.

The spam side is brutal. Truecaller’s India Insights Report 2025 said it identified 4,168 crore spam calls in India during the year and blocked 1,189 crore of them. It also estimated that users avoided about 21.7 lakh hours every day speaking with spammers. The scale is enormous.

There’s also a business trust problem hiding inside all this. Truecaller has said as much as 70% of business calls are rejected or missed when people don’t recognize the number. That creates an opening for a product that can do more than label a caller. It can broker the first 10 seconds of trust. And that’s why the category is shifting from caller ID toward AI call handling.

Can Equal AI call assistant outgrow caller ID?

It might.

Equal isn’t trying to win by telling you who’s calling. Tons of apps already do that, and the government’s CNAP effort pushes in the same direction. The harder bet is that people want software to deal with the call itself — to ask questions, screen intent, summarize, reply, and eventually act. If Equal AI Call Assistant gets that right on iOS, on known callers, and in outbound tasks, it stops being a neat Android feature and starts becoming a habit.

Read how Endurance Energy raised a $54M Series A led by Founders Fund to develop subsea geothermal power plants that could deliver always-on clean electricity from seafloor heat near coastal demand centers.

FAQ

• What funding did Equal AI raise?
  Equal AI raised $30 million in a Series B round announced on June 11, 2026. Prosus Ventures and Tomales Bay Capital led the deal, and the company said the new financing brought its total capital raised to more than $42 million.
• How does the Equal AI call assistant work?
  It answers unknown calls on your behalf, asks the caller why they’re calling, and shows you the interaction through live transcription and a post-call summary. Users can also send preset or custom typed responses for the AI to speak, and the app keeps the call recording and conversation history inside the caller screen.
• Who is Keshav Reddy, the founder of Equal AI?
  Keshav Reddy is a third-generation entrepreneur from the GVK business family and founded Equal in 2022 after working in airports and pharmaceuticals. He studied at the University of Michigan, earned an MBA from MIT, and had already built an investing track record through the Reddy Family Office before pushing Equal from identity infrastructure into consumer software.
• What market is Equal AI actually in?
  Equal AI sits in the overlap of AI call screening, caller ID, and consumer communications software. Its closest reference points are Google’s Call Screen, Apple’s call screening tools, and Truecaller’s spam and caller-identification products, but Equal is leaning harder into India-specific voice workflows, multilingual conversations, and action after the call starts.

Endurance Energy is building subsea geothermal power plants that aim to turn seafloor heat into always-on electricity. The Seattle startup has raised a $54 million Series A led by Founders Fund, betting that clean power demand is rising faster than conventional projects can be built. The problem is simple: heavy industry, EV charging, and AI infrastructure all want round-the-clock electricity, but a lot of low-carbon options are either intermittent or painfully slow to deploy. Founder and CEO Andrew Redd, a former SpaceX engineer who worked on Dragon and Starship, started Endurance in 2025 after deciding renewable power needed a more radical hardware approach.

What is Endurance Energy’s subsea geothermal system?

Endurance’s subsea geothermal plan is to place power hardware on or near hot seafloor hydrothermal systems along tectonic spreading zones, then send that electricity back to shore through submarine cables. Its seafloor hydrothermal generators could deliver gigawatts of baseload power from Ring of Fire regions where volcanic heat sits much closer to the surface than it does at most land-based sites.

Here’s the practical workflow. Endurance identifies offshore geothermal resources and deploys drilling and prototype hardware from vessels. It then mates a wellhead system to a vent or drilled well and pumps hydrothermal fluid through equipment that measures temperature and flow. In one field update, the company said its first wellhead prototype was already operating on a submarine volcano in Tonga’s Lau Basin, where vent fluids exceed 300°C. All pumping, sensing, and communications were powered onboard by solid-state thermoelectric generators.

That matters because it shifts a lot of ugly manual work away from the land-based geothermal playbook. Instead of chasing scarce surface leases in the best-known geothermal corridors and drilling deeper inland, Endurance is trying to use hotter offshore resources and standard offshore engineering habits. That includes vessels and subsea handling. Also robotic operations, corrosion-resistant hardware, and cable economics. Redd has said site choice comes down to an optimization algorithm balancing cable cost, resource size, and demand onshore, while avoiding sensitive habitats near hydrothermal vents.

Who founded the subsea geothermal startup Endurance Energy?

The founding story

Redd’s pitch starts with a hard filter. He wanted an energy source that’s renewable or at least non-polluting, available 24/7, and scalable into the tens or hundreds of gigawatts. In his words, that clean-power requirement was “my non-negotiable,” and it pushed him past nuclear, wind, solar, and hydropower toward geothermal, which he called “the only real deployable, baseload renewable.”

The origin story is unusually direct for climate tech. Redd grew up in the Pacific Northwest and has linked that experience, especially the region’s heat waves and fires, to his decision to work on energy. More important, he left SpaceX convinced that an incremental fix wouldn’t cut it, and that the next company had to be built from first principles.

Why Andrew Redd looks like a believable builder

Redd isn’t a classic geothermal founder. That’s both the appeal and the risk. His background is in brutal hardware execution, not years of utility permitting or conventional reservoir engineering. His public profile shows Princeton University as his alma mater before stints at SpaceX sites in Hawthorne, California, and Starbase, Texas.

Still, the fit isn’t random. Endurance is trying to fuse space-style iteration with subsea industrial systems. That favors teams comfortable with hard environments, rapid prototyping, and custom machinery. The company has leaned into that identity: its Seattle waterfront base lets it load seafloor drills and prototype generators directly onto vessels. Very different from a software-heavy climate startup.

Traction, team, and early signals

For a company founded in 2025, Endurance has moved fast. TechCrunch reported that the startup had grown to 25 employees by June 11, 2026, including 12 alumni from SpaceX, and that its vice president of engineering came from fusion startup Helion Energy. Company posts show it had already completed multiple deep-sea missions, entered a pilot collaboration with Tonga, and tested hardware in both shallow water near Puget Sound and at mile-plus ocean depths in the South Pacific.

The Tonga work is one of the clearest signs this isn’t just slideware. On February 27, 2026, Tonga’s prime minister signed an MOU with Endurance to explore subsea geothermal development, and by late spring the company said it had completed its first deep-sea deployment in Tongan waters. That doesn’t mean commercial power is close. It does mean Endurance has crossed from concept art into field operations. That’s a big line in any deeptech company.

The $54M round and who joined it

The new financing is a $54 million Series A led by Founders Fund, with Ascend, Construct Capital, Felicis Ventures, First Round Capital, Point72 Ventures, Riot Ventures, and Voyager Ventures also participating. Redd has said the money will go toward developing Endurance’s power-plant plans as electricity demand climbs from AI data centers, EVs, and heavy industry.

Competition and positioning

The obvious comparison set is Fervo Energy, XGS Energy, and Sage Geosystems. But those companies are still mostly attacking geothermal from land: deeper drilling, engineered reservoirs, closed-loop heat harvesting, or pressure-based systems. Fervo closed a $462 million Series E in December 2025, XGS announced a 150 MW geothermal project with Meta in New Mexico in June 2025, and Sage disclosed a Series B of more than $97 million in January 2026. Endurance’s angle is that the hottest, shallowest resources near plate boundaries may be offshore and closer to major coastal demand centers than inland geothermal fields.

That differentiation is real. So is the difficulty. Saltwater corrosion, deep-ocean robotics, subsea maintenance, and cable economics could wreck the model if the engineering or costs go sideways. Redd’s own defense is blunt: if something fails, the environmental downside looks a lot more like hot water release than an oil spill. Investors are buying the upside case that offshore oil-and-gas know-how can be repurposed into offshore geothermal.

Why are investors betting on subsea geothermal now?

This round matters because Endurance isn’t raising seed money to tinker in a garage anymore. A $54 million Series A gives the company room to expand field testing, manufacturing, environmental work, and subsea systems development, but it’s still small compared with the capital needed to build utility-scale infrastructure. That tells you what investors think this round is for: de-risking the platform, not proving out a mature power business yet.

There’s a sharper thesis underneath it. If Endurance can show that offshore geothermal can be drilled, controlled, and cabled back economically, it opens a route to clean firm power near coastal load centers instead of far inland. That’s why this is interesting. Not because it’s safe, but because the prize is huge if the hardware works.

How big is the geothermal power market?

Geothermal is still tiny in the U.S. mix. EIA says U.S. geothermal plants in 7 states produced about 16 billion kilowatt-hours in 2025 — roughly 0.4% of utility-scale electricity generation. DOE says the U.S. currently has just over 4 GW of geothermal generating capacity, but advances in next-generation geothermal could lift that to at least 90 GW by 2050.

The demand backdrop is getting tighter, not easier. IEA forecasts global electricity demand growing 3.6% a year from 2026 through 2030, with data centers among the drivers, and it has said that in the U.S. data centers are on course to account for almost half of electricity-demand growth by 2030. That’s the sort of setup that makes investors care a lot more about clean firm power than they did a few years ago.

There’s also a broader geothermal tailwind here. IEA’s 2024 geothermal analysis said geothermal has the second-largest technical potential for electricity generation after solar PV, ahead of onshore and offshore wind on that measure. Endurance’s own pitch pushes the argument even further offshore: Redd estimates about 6 TW could be developed around the Ring of Fire within 5 to 10 years, versus roughly 20 TW of average global energy use today. That estimate is aggressive. But it shows why venture firms are willing to take a swing.

What to watch next for Endurance Energy

Endurance hasn’t proven commercial subsea geothermal yet. Nobody has. But it has already done something a lot of climate hardware startups never manage: it turned a wild idea into repeated field operations in a very nasty environment.

The next test is simple to state and hard to clear. Endurance needs to move from prototypes and pilot deployments to a reliable multi-megawatt system that utilities or islands will actually buy power from. If that answer starts turning into yes, Endurance won’t look like a weird ex-SpaceX side quest anymore. It’ll look early.

Read how Manam Chocolate raised a $9M Series A led by Omnivore to expand its farm-to-retail craft chocolate business and bring its experience-led premium cacao brand to Delhi NCR.

FAQ

• What funding did Endurance Energy raise? Endurance Energy raised a $54 million Series A announced on June 11, 2026. Founders Fund led the round, and the syndicate included Ascend, Construct Capital, Felicis Ventures, First Round Capital, Point72 Ventures, Riot Ventures, and Voyager Ventures.
• How does Endurance Energy’s subsea geothermal technology work? Endurance is developing seafloor systems that capture hydrothermal heat near tectonic plate boundaries and convert it into 24/7 electricity sent ashore by submarine cable. In Tonga, the company said a prototype wellhead had already attached to a vent, pumped hydrothermal fluid, and powered onboard sensing and communications with solid-state thermoelectric generators.
• Who is Andrew Redd? Andrew Redd is Endurance Energy’s founder and CEO, and he previously worked as an engineer on SpaceX’s Dragon and Starship programs. His public background also shows Princeton University and work across SpaceX locations in Hawthorne and Starbase before he launched Endurance in 2025.
• Is Endurance Energy a geothermal or offshore energy company? It’s both, really — Endurance sits in next-generation geothermal but applies offshore industrial methods to reach heat sources under the ocean. That’s what separates it from rivals like Fervo, XGS, and Sage, which have focused on land-based geothermal systems even as the whole category gains momentum.