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What Startups Are Selling For The Person Who Has Everything
In recent years, we’ve witnessed continued shrinkage in investment to consumer-facing product startups, particularly those selling gadgets and giftable goods. While there’s no single explanation for the decline, it doesn’t help that this has been a tough area for returns. It’s a widespread trend, as we’ve chronicled, affecting areas including fashion, consumer electronics and the once-burgeoning direct-to-consumer space. All this is to say that, if one were perusing recently funded startups to find gift ideas, the options aren’t as broad as they used to be. No more $700 juicers or smart party coolers this holiday season. That said, there are still intriguing options in the mix, particularly around wellness, customization and apparel. To illustrate, we used Crunchbase data to assemble a list of 24 companies funded this year with products on the market, ranging from libido-lifting kits to 3-D printers. We also dig into some of the more transparent trends. Wellness is one of the top trends Wellness was the standout focus area this year for consumer gadgets and products startups. This includes the most heavily funded and best-known name on the list — Oura, maker of smart rings that track over 20 biometrics to deliver wearers personalized and timely health metrics. The starting price is around $500. We could all use a better night’s sleep, and startups are tackling this area as well. The top fundraiser here is Eight Sleep, which picked up a $100 million Series D in August. It sells connected bed gear that can adjust to provide optimal temperature and support. Getting older also brings its share of wellness needs, and startups are on this too. This includes San Francisco-based OneSkin, which sells anti-aging skincare products and raised a $20 million round this summer. And for menopausal or post-menopausal women, there’s a Black Friday sale at Womaness, which sells “menopause survival,” libido-lifting and healthy aging kits, along with skincare and sexual wellness products. Customization Personalized gifts are also a popular offering, with several recently funded startups focused on customized products. For artistic types, Arcade offers an AI-enabled platform for designing jewelry and home decor goods. The startup then works with a team of “verified makers” to turn the design into a finished product. On the manicure front, Blank Beauty is mixing up custom nail polish based on customer-submitted photos. The Tennessee startup snagged a $6 million Series A in May. For those seeking a pricey item to pre-order, meanwhile, crowdsource-backed EufyMake wants to let you make your own custom creations with its personal, 3-D texture UV printer. One can currently pre-order a printer for $2,300. Fashion’s still in style (somewhat) We’re also still seeing some fashion startups raising good-sized rounds, although it’s admittedly not the most action-packed sector. The biggest startup fundraiser in this niche for 2025 was Kim Kardashian’s Skims. The shapewear and clothing brand closed on $225 million in a Series D this month. Vivrelle, a subscription offering for luxury accessories, was another investor favorite, picking up a $62 million round this summer. It’s also running a Black Friday sale for those targeting fans of designer handbags. Fun vs. fundable Overall, there’s a lot of stuff to buy, even if VCs haven’t been heavy patrons of the consumer space. This is pretty typical. As startup categories go, consumer products has always been one of the more fun ones to research. Offerings tend to be clever, quirky and nice-to-have, if not essential. But while the category may be a startup reporter favorite, it’s not always venture investors’ top pick. This was apparent for 2025, as VCs poured record sums into AI deals and mostly ignored market-ready consumer products and gadget startups. Still, I wouldn’t count this sector out. For one, while we didn’t see many market-ready consumer products unicorns, investors did put considerable cash into a number of robotics startups working on consumer products. Bots for housework look particularly compelling. Two-year-old The Bot Co. has raised $300 million to date to develop a robot for doing housework. And Sunday, a Benchmark-backed startup building a household robot capable of doing everyday chores, introduced its first bot, Memo, last week. Several others in the heavily funded space are working on both consumer and more specialized workplace bot offerings. Perhaps these will be the hot holiday item in a couple years. If they work as well as early buzz hints, they might even be able to wrap themselves. Related Crunchbase list: Related reading: Illustration: Dom Guzman
Why AI’s Next Phase Belongs To Infrastructure
By Laura Connell and Andreas Cleve The artificial intelligence wave is entering its most valuable phase. Even in conservative scenarios where AI capabilities plateau at current-generation models, analysts project tens of trillions in value creation as companies integrate AI into their operations. In more ambitious outlooks, the impact could rival the industrial revolution itself. The question isn’t whether this transformation will happen, but where value will accrue as the market matures from frontier research to broad deployment — and over what timeframe. Investment dispersion reflects genuine uncertainty about AI’s trajectory, but infrastructure value compounds regardless of which scenario unfolds. Why this time is differentLaura Connell and Andreas Cleve Every tech cycle attracts skeptics who compare it to past bubbles. The data tells a different story. During the dot-com boom, 97% of fiber capacity sat unused. In 2025, the opposite is true: Every unit of compute is active, utilization rates remain high, and returns on AI infrastructure are already positive. Global investment in generative AI reached $49 billion in the first half of the year, driven by hyperscalers reinvesting profits, rather than speculation. The first wave of AI value went to the foundation model builders — OpenAI, Anthropic and others — whose breakthroughs triggered an explosion of experimentation at the application layer. That wave proved what was possible. Now, as investment scales and adoption spreads into regulated sectors, the challenge has shifted downstream. The frontier is no longer building larger models, but getting AI live — safely, reliably and within real-world constraints. Deployment at this stage depends on more than compute access or APIs; it requires embedded teams who understand the domain, the workflows and the regulations that shape how AI performs. That blend of infrastructure and expertise is becoming the new differentiator — the layer that turns potential into production. Physical capacity is tightening. Data centers now consume up to 10 gigawatts per site. But the greater bottleneck is operational: compliance frameworks growing more complex, orchestration challenges in global deployment, and the gap between proof-of-concept and production. When AI systems stall in pilots, even the most advanced infrastructure struggles to deliver returns. The deployment gap Across sectors, between 80% and 95% of AI projects fail, not only because of inaccuracies but because compliance and validation are treated as afterthoughts. In healthcare, U.S. hospitals spend an estimated $39 billion annually on compliance and administrative oversight. Similar dynamics exist across financial services, energy and any domain where AI must operate within regulatory boundaries. Developers are asking new questions: How can models remain auditable as they evolve? How can performance stay consistent across jurisdictions with different data rules? How can costs be contained as usage grows unpredictably? In healthcare, this means API platforms that handle medical-grade data securely, automate audit trails for regulators, and enable deployment in weeks instead of months. Building that capability from scratch delays product launches and drains engineering resources most teams don’t have. The next decade of value lies in the infrastructure making AI both compliant and scalable — the layer that allows innovation to move from impressive demos to mainstream deployment. The rise of vertical infrastructure The next evolution of infrastructure will be vertical. General-purpose compute makes AI possible, but domain-specific infrastructure makes it usable. The highest stakes industries — healthcare, energy, finance, precision manufacturing — depend on systems that understand their regulations, workflows and risk thresholds. That’s where the next generation of durable value will form. The demand signal is clear. To secure long-term success, developers need to build on AI infrastructure that makes their solutions fully deployable. Accuracy is necessary but not sufficient. Deployment is the bottleneck. Corti’s 1 experience illustrates how this is playing out. Health systems need AI they can deploy and trust, not just test. By embedding validation, compliance and audit directly into its APIs, Corti enables developers to integrate clinical-grade AI in weeks rather than months. What began as a healthcare challenge is becoming a broader design pattern — an infrastructure model that abstracts away friction between innovation and safe adoption at scale. Europe’s structural advantage Europe’s early emphasis on interoperability, privacy and safety once seemed like a constraint. As the market shifts from experimentation to widespread deployment, those principles have become a competitive advantage. This is playing out in real procurement decisions. When a top-three global healthtech provider evaluated infrastructure for their clinical AI deployments, they chose Corti over Microsoft, OpenAI and Anthropic. What began as months of technical due diligence became a landmark agreement — a signal that global buyers now prioritize compliance architecture and deployment readiness alongside model capability. Companies that embedded regulatory principles from day one are structurally better positioned for this phase. European builders have been designing for this complexity from the start, treating compliance as a core product requirement rather than a go-to-market barrier. Every transformative technology becomes more efficient over time, and AI is no exception. Model efficiency improves by an order of magnitude annually, while infrastructure-led automation removes friction across regulated sectors. This is not a bubble deflating; it is a market maturing from frontier research to scaled production. The next era belongs to the builders who recognized early that deployment, not just capability, would define who wins. The hype will fade, as it always does. What remains is infrastructure purpose-built for the hardest problems, allowing thousands of companies to turn AI’s transformational potential into measurable reality. Laura Connell is a senior partner at Atomico, the founder-built European venture capital firm. Connell leads growth-stage investing at Atomico, where she focuses on AI infrastructure and applications. Andreas Cleve is the co-founder and CEO of Corti, a pioneering AI company building infrastructure and foundation models for healthcare developers. After nearly a decade as a multientrepreneur in artificial intelligence, Cleve founded Corti with Lars Maaløe to help healthcare developers make clinical workflows faster, smarter and more efficient. Today, Corti’s trusted AI powers real-time consultations across the U.S. and Europe — eliminating administrative burdens and bringing expert-level reasoning to every corner of healthcare. Illustration: Dom Guzman
Innovation Policies: The Australian Lesson Focusing Startup Investments On A Few Strategic Sectors
By Alberto Onetti For some time now, I’ve been pointing out how Australia has been making significant progress, positioning itself as a strong competitor among the innovation ecosystems of the Asia-Pacific region. The Scaleup Summit Australia, which Mind the Bridge organizes every October with the support of Investment NSW, offers a good opportunity to take stock — also thanks to the data and analysis from the “Tech Scaleup Australia 2025” report, published with the support of Crunchbase and Acciona. The numbers Australia is home to 1,582 scaleups — almost six scaleups per 100,000 inhabitants, a remarkable number considering the country’s relatively small population — that have collectively raised more than $36 billion in capital (around 2% of national GDP). Aside from the major Asian economies (the 2 billion-plus-people nations of China and India, which play in a different league with 12,403 and 4,112 scaleups respectively), Australia’s numbers are not far behind South Korea (2,127) and Japan (2,268), roughly on par with Singapore (1,660), and 3x larger than emerging ecosystems like the UAE (503). Notably, Australia also stands out as fertile ground for large tech companies — what we call scalers. We identified 71 Australian scaleups that have each raised over $100 million, a number comparable to Japan (86) and South Korea (96). This can be explained by the relative isolation of the Australian ecosystem, which has encouraged the creation of national champions in strategic sectors for the continent such as construction, mining and energy — collectively referred to as “infratech.” Infratech: Australia’s house specialty A closer look at the infratech landscape shows steady growth over the past five years, with venture capital investments rising from $100 million in 2020 to nearly $500 million in 2025. Among Australian scaleups, about 1 in 10 (107) operate in this vertical, covering the entire value chain: from critical resources (21%) to construction (57%) and energy systems (22%). Australia’s dominance in mining AI As highlighted in the “Unlocking the Future of Mining” report developed by Mind the Bridge with support from BHP, Austmine and Hub de Innovación Minera del Perú, and based on dozens of interviews with mining industry experts, large infrastructure projects promoted by local and international corporations (including ACCIONA, BHP and Rio Tinto) are increasingly integrating new technologies such as AI, advanced robotics, computer vision and digital twins. Therefore, it doesn’t come as a surprise that Australia leads globally, accounting for nearly three quarters of all investments in AI for mining, far ahead of China (12%) and the United States (9%). Australia’s dominance in mining AI reflects structural strengths that are tough to match. The country combines large-scale mining operations, supportive regulations and a mature ecosystem of mining tech vendors and talent — which made it the global hub for mining innovation. For companies abroad, this means access to mining AI capabilities now largely depends on partnering with Australian platforms and experts. Investment landscape: industrial roots, corporate muscle Investors are also moving into tech areas like space tech, UAVs, drones and autonomous mobility, especially when these intersect with construction and mining applications. Although Australia’s investor landscape is broad — with 491 active VCs and CVCs currently managing around $32 billion of dry powder available for local scaleup investments (see map on MTB Ecosystem) — it remains largely focused on seed- and early-stage funding (the majority of funds — 73% — are under $50 million). However, what’s particularly interesting — and consistent with the industrial drive of the Australian ecosystem — is that most of the mega funds (over $1 billion) are corporate venture capital vehicles. Leading examples include Rio Tinto, as well as banks like Macquarie Group, ANZ, National Australia Bank and Commonwealth Bank of Australia. Specialization attracts international players, dispersion does not The Australian strong specialization in specific verticals is attracting the interest of global corporates: 26 large international companies have set up innovation outposts Down Under (map available on MTB Ecosystem). This sends a powerful message in today’s innovation world, where the concentration of investments in a handful of major ecosystems has effectively reduced the visibility of nearly all others, pushing them toward marginalization and irrelevance. Specialization, whether in technological domains or industrial applications, can help some of these ecosystems stand out and claim their space on the global innovation map. For more insights on startup and scaleup ecosystems, see Mind the Bridge’s reports (available for free download here). Alberto Onetti, Mind The Bridge Alberto Onetti is chairman of Mind the Bridge and a professor at University of Insubria. He is a serial entrepreneur who has started three startups in his career, the last of which is Funambol, among the five Italian scaleups that have raised the largest amount of capital. He is recognized among the leading international experts in open innovation and has wide experience in setting up and managing open innovation projects — venture clients, venture builders, intrapreneurship, CVCs — with large multinational companies, as well as advising and training on this subject. Onetti has a column on Sifted (Financial Times) and several other tech blogs. Related reading: Illustration: Li-Anne Dias
Edtech-Specific Startup Funding Stays Low
Funding to startups specifically focused on education technology remains at depressed levels relative to a few years ago. However, the tallies — which exclude general-purpose AI platforms popular with educators, students and investors alike — may understate enthusiasm at the intersection of tech and education. So far this year, global edtech-focused startups have raised around $2.8 billion in seed- through growth-stage funding, per Crunchbase data. That’s roughly flat with 2024 levels, pointing to stabilizing investment, albeit at a fraction of the peak a few years ago. In the U.S., this year’s funding numbers are a bit stronger relative to 2024, with $1.2 billion invested in edtech startups so far. While still far off of the pandemic-era highs, 2025’s funding figures puts this year roughly on par with 2023. What’s in and what’s out Edtech is a vast space, covering everything from preschool lesson-planning to corporate upskilling. Given this, it’s not uncommon to see a downturn in one subcategory while another remains a funding favorite. If we were to generalize trends looking at this year’s larger rounds and exits, it appears investors are particularly keen on opportunities in healthcare education and training. At the K-12 level, VCs are also backing startups deploying AI tools to customize lessons for individuals and free up teachers from routine, repetitive tasks. As for what’s not hot, we’ve seen a movement away from coding academies and teaching platforms, with the rise of coding automation tools. We’re also seeing a paucity of jumbo-sized funding rounds and not a lot of deals that look like pre-IPO financings. What the biggest rounds tell us So who is getting funded? Amboss, a Berlin-based startup offering a tool to learn about and research medical information, raised the largest round, securing nearly $260 million in a March financing. The company started with a focus on medical students but now also markets to practitioners. Lingokids, a provider of content and online learning activities for young children, secured the next-biggest funding, a $120 million September round led by Bullhound Capital. Other larger rounds this year include an $80 million financing for EdSights, developer of a chatbot to help students navigate college life and boost retention, and a $45 million Series B for MagicSchool AI, a provider of AI-enabled time-saving and productivity-enhancing tools for educators. For a slightly broader view, below we put together a list of eight of the larger funding recipients in the education sector this year. Buyers too Edtech is also seeing some exit activity. This is coming in the form of M&A, as the IPO market has been quiet this year. Most recently, CareAcademy, a platform for healthcare workers to learn new skills and obtain certifications, sold to Activated Insights, a software platform for senior living and home care providers, for an undisclosed sum. Founded in 2013, Cambridge, Massachusetts-based Care Academy previously raised at least $33 million in known venture funding. The company, founded and led by Harvard-trained educator Helen Adeosun, carved out a niche offering upskilling opportunities to health workers like home care aides and nursing home staffers, opening a path to advancement for what are typically lower-paid positions. Also in the health sphere, OnlineMedEd, an Austin startup focused on online learning tools for medical students and educators, sold this spring to exam prep provider Archer Review, a portfolio company of private equity firm Leeds Equity Partners. Previously, 11-year-old OnlineMedEd had raised at least $30 million in venture funding. And in the post-secondary education space, Modern Campus, a Toronto-based provider of software tools for colleges to attract and retain students, sold a majority stake to PE firm Providence Equity Partners in August. The optimist case Looking ahead, the optimist case is that founders, investors and acquirers alike will find plenty of appealing opportunities in ed tech. Longtime education startup investor Owl Ventures considers the education and training market to be one of the fastest-growing sectors in the global economy. In its 2025 report, the firm projects the global education market is on track to surpass $10 trillion by 2030. In terms of growth, Owl unsurprisingly points to AI as the largest ed tech driver. In recent years, the report notes, AI in the classroom has moved beyond the experimentation stage and is already proving vital in saving educators hours of work, providing personalized tutoring to students, and helping craft compelling lesson plans. Eventually, it’s likely we’ll see the impact of AI innovation in edtech also showing in the form of more funding for startups in the space. Related Crunchbase queries: Related reading: Illustration: Dom Guzman
The Week’s 10 Biggest Funding Rounds: A Lot Of Really Big Deals
Want to keep track of the largest startup funding deals in 2025 with our curated list of $100 million-plus venture deals to U.S.-based companies? Check out The Crunchbase Megadeals Board. This is a weekly feature that runs down the week’s top 10 announced funding rounds in the U.S. Check out last week’s biggest funding rounds here. Perhaps venture investors wanted to get their term sheets squared away in advance of the holiday season. Or maybe AI, despite all the bubble talk, is still heating up in a big way in the startup sphere. Whatever the reason, this past week was an exceptionally busy one for very large startup financings. Not surprisingly, an AI company led the rankings. Lambda, a provider of AI cloud infrastructure, closed on $1.5 billion in Series E funding. Next up was predictions market Kalshi, which picked up $1 billion. More AI, plus crypto, fintech and biotech rounded out the list. 1. Lambda, $1.5B, AI cloud infrastructure: San Francisco-based Lambda, a provider of AI cloud infrastructure, says it raised over $1.5 billion in Series E funding led by TWG Global, a holding company led by Thomas Tull and Mark Walter. Founded in 2012, Lambda has raised $3.2 billion in equity and debt funding to date, per Crunchbase data. 2. Kalshi, $1B, predictions market: Kalshi, a marketplace for wagering predictions on future events, reportedly picked up $1 billion in a new round led by Sequoia Capital and CapitalG. The financing is said to set an $11 billion valuation for the 7-year-old, New York-based company. 3. Luma AI, $900M, multimedia AI: Silicon Valley-based Luma AI, a startup focused on AI-generated video and imagery, announced that it secured $900 million in a Series C round led by Humain that included significant participation from AMD. 4. Kraken, $800M, cryptocurrency: Cryptocurrency exchange Kraken closed on $800 million in new investment across two tranches. Investors including Jane Street Capital, DRW Venture Capital, HSG, Oppenheimer and Tribe Capital led the primary tranche. Citadel Securities also made a $200 million strategic investment at a $20 billion valuation, per Kraken. 5. Physical Intelligence, $600M, robotics AI: San Francisco-based Physical Intelligence, a developer of AI software to power robots, raised $600 million in a funding round led by CapitalG that reportedly sets a $5.6 valuation for the company. Other investors in the round include Lux Capital, Thrive Capital, Jeff Bezos, Index Ventures and T. Rowe Price. 6. Ramp, $300M, fintech: Expense management provider Ramp landed $300 million in fresh funding led by Lightspeed Venture Partners at a $32 billion valuation. The financing marks the fourth raise for New York-based Ramp in 2025 alone, and brings its total equity raised since its 2019 inception to $2.3 billion, per the company. 7. Function Health, $298M, longevity: Austin-based Function Health, a startup offering lab tests, scans and health data with an eye to extending lifespans, secured $298 million in Series B financing. Redpoint led the round, which set a $2.5 billion valuation for the 3-year-old company. 8. Genspark, $275M, agentic AI: Palo Alto, California-based Genspark, a platform for building agentic AI tools for knowledge workers, announced that it closed on $275 million in a Series B 1 round at a $1.25 billion valuation. The company also said it exceeded $50 million in annualized revenue run rate within five months. 9. Suno, $250M, AI music creation: Suno, an AI platform for producing songs and music, raised $250 million in a Series C financing at a $2.45 billion valuation. Menlo Ventures led the financing for the 3-year-old, Cambridge, Massachusetts-based company. 10. Solve Therapeutics, $120M, biotech: San Diego-based Solve Therapeutics, a developer of therapeutics and diagnostics for treating solid tumor malignancies, raised $120 million in a round led by cancer-focused venture investor Yosemite. Methodology We tracked the largest announced rounds in the Crunchbase database that were raised by U.S.-based companies for the period of Nov. 15-21. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week. Illustration: Dom Guzman
Exclusive: Founded By Ex-Nvidia Researchers, Flexion Lands $50M To Build The ‘Brain’ for Humanoid Robots
Flexion, a startup that’s “building the brain for humanoid and human-capable robots,” has raised $50 million in funding, it tells Crunchbase News exclusively. Founded in January by former Nvidia researchers, CEO Nikita Rudin and CTO David Hoeller, along with Julian Nubert and Fabian Tischhauser, Zurich-based Flexion has now raised a total of $57.35 million in funding. The company plans to use its new capital in part to open a U.S. headquarters in the Bay Area. DST Global Partners, NVentures, Redalpine, Prosus Ventures and Moonfire Ventures participated in the Series A financing. Flexion co-founders Fabian Tischhauser, Nikita Rudin, David Hoeller and Julian Nubert Flexion was born out of years of research at ETH Zurich and Rudin’s work at Nvidia. The executive argues that today, most robots rely on scripts, tele-operation or brittle task-specific code. “That doesn’t scale,” he said. Flexion’s platform, according to Rudin, replaces that with a full autonomy stack including language-level reasoning, vision-language-action motion generation, and transformer-based whole-body control “so robots can understand instructions, move through the world, and adapt to new situations with minimal human involvement.” He added: “Unlike companies focused on a single robot form factor or on narrow behaviors, our system is built to work across morphologies and tasks, making it a true general-purpose intelligence layer for robotics.” Put simply, Flexion’s mission is to build the AI foundation “for the next era of robotics” by giving humanoid robots “the intelligence they need to transform industries and daily life, making them safe, capable, and indispensable partners to humans.” Its funding comes amid what looks like a robust year for robotics-related venture investment overall, with more than $10.7 billion invested globally as of Nov. 19, per Crunchbase data, already topping every full year since 2021. All kinds of robots, all kinds of tasks Flexion differs from other efforts around foundational models for robotics in a couple of ways, Rudin said. First, it does not rely on hand-engineered behaviors or teleoperation, in which a human operator controls and trains a robot remotely. Rather, Flexion primarily uses synthetic data generated from high-performance physics simulations to train its models. Second, it claims to leverage reinforcement learning techniques with the goal of delivering software “that is robust to the high diversity of the real world.” This gives Flexion an advantage, claims Rudin, in that its data generation is not constrained by human labor, and its models can “generalize and perform beyond the limits” of teleoperation setups. The startup is initially focused on humanoid and human-capable robots, because, as Rudin puts it, they represent the highest-value opportunity, performing “useful work” in applications across industrial settings, logistics, manufacturing, and eventually, areas like disaster response and planetary exploration. Because Flexion’s platform is morphology-agnostic, the company also sees opportunity in wheeled platforms, multiarm systems, and other complex robotic forms. Over time, Flexion’s goal is to be relevant anywhere robots need to perform long-horizon tasks autonomously. Expansion plans Presently, Flexion has 31 employees. Besides expanding to the U.S., the company plans to use its new capital to expand its Zurich-based R&D team, scale compute and robot fleets, and accelerate commercialization of its autonomy stack. Rudin says the startup is already working with major OEM partners and the funding will help it to scale those partnerships globally. Flexion licenses its software with an annual per-robot software licensing model. “There is a clear appetite for a software-only intelligence layer that can generalize across robot bodies,” Rudin told Crunchbase News. But its priority for now, he said, is to stay focused on core technology development. Working at Nvidia gave Rudin “a deep appreciation” for the compute and data flywheel that enabled the leap in large language models. “Working on the fundamental tools for robot learning training provided me a window into the challenges robotics companies were facing: They are rebuilding the same components, going through the same learnings, and fighting the same challenges,” he said. ‘The toughest and most defensible part of the stack’ Philip Kneis, an investor at Redalpine, told Crunchbase News via email that after looking into the robotics space for years, Flexion stood out because in his firm’s view, it is focused on “the toughest and most defensible part of the stack: building a shared brain for robots.” “They’ve already put robots to work in the real world …” he said. “That ability to turn cutting-edge research into robust, field-tested autonomy is a big part of why we invested.” Sandeep Bakshi, head of Europe investments for Prosus Ventures, said the startup’s simulation-first approach was compelling because it hadn’t seen any other robotic model developers building with that approach. “Most players in the market today are taking teleoperations-heavy approaches, which requires hundreds of thousands of hours of manual human demonstrations — an approach we believe is fundamentally unscalable in the long run,” he added. “Robotic foundation model developers will eventually need to heavily leverage simulation-based training, and the Flexion team is best suited to win with this approach.” Related Crunchbase queries: Related reading: Illustration: Dom Guzman
Exclusive: With Customers Like Okta And Coinbase, Coverbase Raises $16M To Grow AI-Powered Procurement Platform
Coverbase, an AI-powered procurement platform, has raised $16 million in a Series A round led by Canapi Ventures, the startup tells Crunchbase News exclusively. Founded in 2024, San Francisco-based Coverbase aims to reinvent how enterprises vet and manage vendors in a “security-first” manner. Specifically, it uses artificial intelligence to automate and secure how large, regulated companies onboard new vendors and suppliers, such as software providers, consultants, contractors and service firms. Coverbase counts Nationwide, Coinbase, Okta and the Navy Federal Credit Union among its customer base. Coverbase co-founders Clarence Chio and Kao Zi Chong. Courtesy photo. Co-founders CEO Clarence Chio and CTO Kao Zi Chong have impressive backgrounds. Chio also co-founded Unit21, a startup that helps businesses monitor fraudulent activities with its no-code software that has raised $92 million from the likes of Tiger Global Management. Chong is a former engineering manager at fintech giant Stripe. The pair started Coverbase to automate the procurement process “by weaving risk, security, and compliance decision-making directly into every stage of intake, due diligence, contracting, and ongoing monitoring.” “What makes us different is that our AI agents don’t just manage workflows, they actually perform the work,” Chio, who currently teaches AI and cybersecurity at UC Berkeley, told Crunchbase News. Most competitors, he claims, build tools that help humans move through manual approval steps. (Competitors include the likes of Zip, Coupa, Ariba and Archer.) By being “AI-agents-first” instead of “workflow-first,” Coverbase allows customers to onboard vendors faster, with less friction and stronger security outcomes, according to Chio. According to Verizon’s 1 Data Breach Investigations report, breaches involving third parties reached 30% this year, up 2x compared to 2024, “driven in part by vulnerability exploitation and business interruptions.” Investors appear to be pouring more money into AI-powered procurement startups lately. Examples include: Fast growth Existing and new investors including Fika Ventures, TTV Capital, Pear VC, Valley Bank and Founders You Should Know also participated in Coverbase’s Series A round. To date, the company has raised about $20 million. While Chio declined to reveal its valuation, he said the Series A represents “a roughly 4x increase” compared to its previously undisclosed $3.5 million seed round led by Fika. Chio also declined to reveal hard revenue figures, noting that Coverbase’s customer base has grown 10x since the start of 2025. Overall, the startup has about 35 customers and operates on a usage-based SaaS pricing model that scales with the number of suppliers and risk assessments performed on its platform. Its customers range in size, with some having as few as 50 suppliers, and others being larger companies with over 50,000 suppliers. Besides those mentioned above, customers include General Bank of Canada, Live Oak Bank, Coastal Bank, Thread Bank, LiveView Technologies, Guardant Health, Rubrik, Alteryx and Bill. Coverbase is currently active in industries such as financial services, insurance, healthcare, pharmaceuticals and technology because they “demand high levels of compliance and security,” Chio noted. But the company is also expanding into telecommunications and critical infrastructure, and sees future opportunities in other highly regulated sectors such as energy and utilities, defense contracting, government, aerospace and aviation, medical devices and biotech, and payments and logistics. The company plans to use its new capital to expand into contract management and continuous security monitoring. It’s also planning to quadruple its sales force to meet “accelerating enterprise demand.” Presently, Coverbase has 12 employees. ‘A real and persistent pain point’ Walker Forehand, president and general partner at Canapi, said his firm was drawn to Coverbase because it believes the company is “solving a real and persistent pain point in enterprise procurement—particularly in highly regulated, security-conscious sectors.” “Vendor onboarding is typically slow, fragmented, and risk-prone,” he wrote via email. “Coverbase is flipping that dynamic by using AI to make procurement faster, more secure, and strategically valuable.” Forehand also believes that what sets Coverbase apart is its AI-native approach. “This gives enterprises a faster, smarter, and more secure way to adopt innovation without adding operational burden,” he said. “It’s not just about efficiency — it’s about turning procurement into a competitive advantage, which is a fundamentally different mindset from traditional solutions.” Related reading: Illustration: Dom Guzman
Unicorns Pick Up For The Second Month In A Row, Adding Close To $45B To The Board
A total of 20 companies joined The Crunchbase Unicorn Board in October, adding $44.5 billion in value. This was the highest valuation amount added to the unicorn board for a new cohort in the past three years. The number of new monthly entrants has picked up in recent months. The top 20 companies on the board have also been reshuffled and we’ve seen a marked increase in new decacorn-valued companies. Of the 20 companies that joined in October, 11 came from the U.S. China added three new unicorns and Sweden contributed two. Europe, the U.K., Germany and Ukraine each minted one new unicorn, as did India. Among the new entrants, New York-based open model developer Reflection and Austin-based residential battery operator Base Power each raised billion-dollar rounds that valued them as unicorns for the first time. The highest valued among the new unicorns were Reflection, which was valued at $8 billion, and San Francisco-based payments blockchain Tempo, valued at $5 billion. Exits A pair of companies from the unicorn board were acquired in October: Passwordless authentication company Stytch was acquired by Twilio, and Nexthink, an IT employee experience platform was acquired by Vista Equity Partners. In another October exit, data management tooling company dbt Labs merged with Fivetran in an all-stock deal. Three companies also went public: Silicon Valley-based travel and expense management company Navan, Shanghai-based e-commerce software platform Jushuitan Network Technology, and Beijing-based silicon wafer production company Eswin Materials. New unicorns Here are October’s 20 newly minted unicorns across multiple sections. AI led with four companies, transportation with three, and healthcare and financial services followed, each with two companies. AI Transportation Healthcare and biotech Financial services Web3 Energy Aerospace Professional services E-commerce Sales and marketing Defense tech Beauty Semiconductor Related Crunchbase unicorn lists: Related reading: Methodology The Crunchbase Unicorn Board is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on Crunchbase data. New companies are added to the Unicorn Board as they reach the $1 billion valuation mark as part of a funding round. The unicorn board does not reflect internal company valuations — such as those set via a 409a process for employee stock options — as these differ from, and are more likely to be lower than, a priced funding round. We also do not adjust valuations based on investor writedowns, which change quarterly, as different investors will not value the same company consistently within the same quarter. Funding to unicorn companies includes all private financings to companies that are tagged as unicorns, as well as those that have since graduated to The Exited Unicorn Board. Exits analyzed here only include the first time a company exits. Please note that all funding values are given in U.S. dollars unless otherwise noted. Crunchbase converts foreign currencies to U.S. dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs and other financial events are reported. Even if those events were added to Crunchbase long after the event was announced, foreign currency transactions are converted at the historic spot price. Illustration: Dom Guzman Clarification: This story has changed since its original publication to correct an error in the Exits section.