The raise brings the Swedish company鈥檚 recent Series D funding round total to $600 million. At the time of the first close in March, Legora was valued at $5.5 billion.

, and also participated in the extension, according to CNBC.

The valuation was a big jump from the $1.8 billion Legora achieved just last October, when it raised The company has now raised a total of $866 million since being founded in 2023 by , and .

Nvidia has been an active startup investor, backing over three dozen companies so far in 2026, according to SA国际传媒 data. The chip giant has a stake in several of the most valuable AI companies, including , , and . It also has a number of power-generation-related investments sprinkled in, indicating ongoing concern and interest in how we are going to feed all those power-hungry AI bots.

Record legal tech funding

Meanwhile, venture funding for legal tech startups reached a record high in 2025, driven by investor enthusiasm for AI鈥檚 potential to automate the legal profession. Per SA国际传媒 , companies in the legal and legal technology sectors raised $4.08 billion in seed- through growth-stage funding in 2025. That鈥檚 an impressive 77.4% increase from the $2.3 billion raised by legal tech startups in 2024.

So far this year, legal tech startups have already raised more than $1.3 billion, .

Other startups in the industry that have closed on sizable fundings over the past year include:

• : A provider of legal practice management software, Filevine announced in September that it had closed on two previously undisclosed rounds totaling $400 million. led the first round and, interestingly, joined and to co-lead the second.
• : San Francisco-based Harvey, a provider of AI tools for legal professionals, closed on four separate funding rounds in 2025, including two rounds of $300 million each. To date, the 3-year-old company has raised more than $1 billion.
• : Toronto-based Blue J, developer of a GenAI tax research platform that counts legal professionals among its core users, raised $122 million in an August Series D financing led by and .
• : Palo Alto, California-based Eudia, which develops an intelligence platform for Fortune 500 legal teams, landed up to $105 million in a Series A financing led by .

Related SA国际传媒 query:

Related reading:

• Swedish Legal Tech Startup Legora Triples Valuation To $5.55B With $550M Series D Led By Accel
• Legal Tech Investment Hits All-Time High With Filevine Funding

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Austin-based Dreambase has developed AI-native data agents to do the analytical work that data teams have historically done, according to CEO and co-founder .

After a decade in the Austin tech scene as a “first product hire鈥� working alongside early-stage founders, Keil spent three years as head of product at . By August 2024, he had teamed up with to launch Dreambase. After months of prototyping and proving concepts, the company started offering early access in April 2025.

The startup describes itself as -native and not just Supabase-compatible. Supabase is the developer of an open-source relational database for AI app development that competes with 鈥檚 database.

Dreambase users can connect their Supabase database and get a dashboard 鈥渋n seconds,鈥� according to Keil.

鈥淥ur AI data agents handle the rest,鈥� he told SA国际传媒 News. 鈥淭hey build dashboards, run analysis and surface insights 24/7. Whether you’re a solo founder shipping your first product or an enterprise AI innovation team running mission-critical analytics, you get a full virtual data team without the headcount.鈥�

Interestingly, Supabase鈥檚 team was so impressed with Dreambase鈥檚 products, three of its executives participated in its funding round, including its CFO, CTO and COO. 1聽led the raise, which also included participation from , , , , and . Angel investors from companies including , and QuotaPath also participated.

鈥楾he analytics layer鈥� for every Postgres company

While Dreambase鈥檚 technology is “Postgres native” 鈥� meaning it functions with any Postgres database 鈥� the company saw a massive opportunity within Supabase鈥檚 community of 7 million developers.

For the unacquainted, (often just called “Postgres”) is a popular open-source relational database where a company stores all its most important data such as user profiles, transaction histories, app settings and product logs.

Keil is ambitious about Dreambase鈥檚 potential to help companies at any stage move faster.

鈥淲e plan to be the analytics layer for every company running on Postgres in the world,鈥� he said.

Historically, companies have waited until later stages to hire a data team. But Dreambase says its value proposition is that it goes straight to the database. By providing a “context layer” to LLMs, it allows anyone to ask product questions in natural language.

鈥淥ur AI data agents aren’t a chatbot wrapped around a chart. 鈥� Your Supabase database is the source of truth, and the experience is fast and intuitive whether you’re a founder, engineer, operator or analyst,鈥� Keil said.

Currently a five-person team, Dreambase aims to double its headcount over the next few months by hiring across engineering and enterprise go-to-market.

A 鈥榝undamentally different starting point鈥�

Felicis General Partner said her firm was impressed with the fact that Dreambase鈥檚 founding team had 鈥渓ived the problem鈥� the company is trying to solve 鈥渇rom both sides鈥� 鈥� Keil as head of product at QuotaPath and Ledbetter leading design at , and .

鈥淭hey’ve watched the same broken sequence play out for years: someone asks a simple question about the product, and the answer takes weeks. What’s different now is that AI can finally do the work a data team used to do, and the Supabase platform they鈥檙e building on is 7 million+ developers and accelerating,鈥� she wrote via email.

Supabase CFO said he invested as an angel into Dreambase because he believes the company 鈥渨ill win the analytics layer for the next generation of Postgres-native companies.鈥�

鈥淢ost analytics tools treat Postgres as just another data source to extract from. Dreambase treats it as home,鈥� he wrote via email. 鈥淭hat’s a fundamentally different starting point, and it shows up everywhere in the product, from how fast teams get to their first dashboard to how the AI agents reason about real Supabase schemas. The Postgres ecosystem has been waiting for this analytics layer, and Dreambase is the team building it.

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She began on the consumer side at early-stage companies including and pioneered the early push of CPG e-commerce at long before the acquisition. After a pivot into tech M&A at and a partnership at GGV Capital (now ), she joined NEA roughly three years ago.

Today, Luck鈥檚 thesis centers on the AI application layer, betting on vertical AI and the “last mile” of automation to bridge the gap between horizontal model potential and tangible enterprise ROI.

We recently spoke with Luck about the increasing relevance of vertical AI, how startups can carve out durable advantages in a world dominated by platform giants and more.

The interview has been edited for brevity and clarity.

SA国际传媒 News: You鈥檝e seen the evolution of commerce from the early days of Amazon Fresh to the current AI boom. How does that background influence how you view the “friction” of AI adoption today?

Luck: I see incredible parallels. At Amazon, I spent my days convincing CPG manufacturers that e-commerce was the inevitable future. Back then, there was immense friction 鈥� technological, logistical and mental. Today, 500 companies are in a similar spot with AI.

While the potential is obvious, most organizations are still struggling to integrate it into their daily workflows. We are moving from a world where AI is a “shiny object” to one where it must solve a mechanical problem, but getting there requires overcoming that initial resistance.

You鈥檝e talked about the “ question” 鈥� the fear that frontier models will eventually swallow the application layer. How can vertical startups build a durable defense?

It鈥檚 the primary concern for founders right now. Most horizontal tools, like Claude, currently act as research co-pilots. They are excellent at taking a user from 0% to 80%, but they don’t handle the “last mile.” For 99% of people, AI isn’t yet running in the background while their hands are off the keyboard.

Moats are being built by solving the specific hardships of that last mile. Take financial planning and analysis: You can plug data into a general model and ask questions, but the model won’t automatically re-forecast, flag specific trade-offs between burn rate and growth, or create a unified data layer across disparate sources.

Startups that build these purpose-built product flywheels 鈥� and use forward-deployed engineers to sit alongside users and identify workflow holes 鈥� build a moat that general models can’t easily replicate through scale alone.

Why is owning the end-to-end workflow becoming more valuable than the underlying model differentiation?

Because it removes the mental friction of “What do I do with this?” If a company can deliver a finished work product 鈥� an artifact 鈥� the ROI is undeniable.

Our portfolio company does this for legal due diligence. Another, , does this for equity research reports. When the output is a discrete document that looks exactly like (or better than) what a team of analysts would produce, the enterprise doesn’t care which model is under the hood. They care about the hours saved and the accuracy of the result.

You mentioned the idea of the “operating system” changing. How should startups think about partnering versus competing with platforms like Claude or ?

We haven’t truly seen our way of working change yet; we鈥檙e still using the same UIs. But I expect a shift in which a model becomes your de facto operating system 鈥� a command center from which you “call” other specialized applications.

Think back to five years ago, when startups used as their primary interface. You might see a future where a specialized tool like Samaya is integrated directly into a horizontal model鈥檚 UI.

The specialized knowledge graph and proprietary data remain with the startup, but execution occurs within the user’s primary “operating system.” Interoperability will be the next big frontier at the application layer.

In regulated industries, what is the “make or break” factor for an enterprise buyer right now?

It鈥檚 a mix of accuracy, auditability and cybersecurity. Enterprises are terrified of “data provenance” issues 鈥� they need to be able to audit the trail of every number.

I鈥檓 closely following (Artificial Intelligence Underwriting Co.). They are essentially building a “惭辞辞诲测鈥檚 for AI agents.” They鈥檝e assembled a group of over 100 CISOs to create a for-profit certification standard. If a company like or can certify its agents against this standard, it gives the enterprise a layer of trust that a standard SOC 2 just doesn’t cover yet.

With AI-fueled hackers creating new attack vectors, this kind of authentication is becoming a necessity.

What does the “next frontier” look like for you as an investor?

We are in the “pre-mobile-native” era. We鈥檝e moved the web to the phone, but we haven’t seen the apps that only AI can enable. I鈥檓 waiting for the “ moment” 鈥� the transition from a co-pilot, where you’re still “driving,” to a truly autonomous, agentic workflow. Whether it鈥檚 through voice-first interfaces or agent-to-agent interaction, the next 12 months will likely reveal the first truly novel ways of working that feel fundamentally different from the laptop-and-keyboard era.

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Goldman Sachs Alternatives has committed up to $50 million to the round, including an initial $25 million investment and an additional $25 million to be funded in the coming months, subject to undisclosed conditions.

Existing backers and also participated in the financing, which brings New York-based Kashable鈥檚 total equity and debt raised to more than $450 million since its 2013 inception. The company declined to reveal its valuation saying only it had tripled since its January 2024 Series B raise.

The premise behind Kashable is that, since its loans are facilitated through an employer, the service can often offer better rates than a traditional bank might 鈥� making it a more appealing alternative to high-interest credit cards or payday loans.

In addition to low-cost loans, Kashable partners with employers to provide employees with a suite of financial wellness services, including credit monitoring, financial coaching and affordable credit. Employers offer the services as employee benefits integrated with their HR and payroll systems.

Big growth

Kashable has grown more than 40% year over year so far in 2026, according to co-CEO and co-founder , an computer scientist and former derivatives trader. Its revenue model is driven by interest and fees paid on loans and administrative fees from employers for customized programs.

To date, the company has funded nearly $2 billion in loans and expects to surpass $500 million in volume in 2026 alone. Co-founder and co-CEO told SA国际传媒 News that Kashable is profitable, and has been 鈥渇or several years.鈥�

鈥淭imely repayments [of loans] through payroll reduce default rates, giving Kashable better unit economics that it can then pass on to its customers as lower-cost loans,鈥� Kumar told SA国际传媒 News.

Kashable鈥檚 platform is available to over 4 million employees across more than 600 employers. Its customers include governments, large nonprofits such as universities and hospitals, school districts, and large companies. They include , , , , , , , the , and San Mateo County in California.

This isn鈥檛 the first company that Kumar and Steklov have started together. The pair also founded , a commercial finance company, in 2008.

Investor POV: 鈥楨ssential liquidity鈥� on fair terms

, partner and head of inclusive growth at Goldman Sachs Alternatives, told SA国际传媒 News via email that his firm鈥檚 investment in Kashable was driven by its mission 鈥渢o support innovative solutions that help working Americans lead more secure financial lives.鈥�

鈥淭he American workforce is facing a significant squeeze as job security and wage growth has struggled to keep pace with inflation, eroding personal savings and the ability to absorb unexpected financial pressures,鈥� he said. 鈥淲e recognized Kashable鈥檚 model and mission as differentiated, providing essential liquidity on fair, transparent terms, in a way that is substantial enough to offer true long-term relief rather than a short-term, expensive Band-Aid. Kashable鈥檚 platform offers a necessary financial bridge that helps users navigate personal liquidity needs without falling into predatory debt cycles.鈥�

Shell also believes that Kashable stands out due to its 鈥渦nique鈥� structural advantage. Its integration with employer鈥檚 payroll systems gives it the ability to get a more accurate picture of creditworthiness, he said.

鈥淐onsequently, Kashable sustains meaningfully lower loss rates than its competitors,鈥� Shell added, 鈥渁nd can pass the resulting savings directly to its borrowers in the form of lower interest rates鈥�

Fintech startups have benefited from increased investment in recent quarters. Total global funding to VC-backed financial technology startups totaled $53.8 billion in 2025, per SA国际传媒 . That鈥檚 a more than 29% increase from 2024鈥檚 total of $41.6 billion raised.

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• Fintech Startups Globally Raise More Money In Far Fewer Deals In Q1 2026

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led the raise, which included participation from , , , and St. Elmo Venture Capital, the investment arm of customer . It brings the Raleigh, North Carolina-based startup鈥檚 total raised to $5.35 million since its 2023 inception.

The idea for Cloneable traces back to a bottleneck its founders encountered years earlier while working in the field.

In 2019, as wildfires ravaged California, co-founders , and 鈥� founding employees at drone company 鈥� were deployed to help inspect critical infrastructure. Their team sent out 150 drone pilots to survey thousands of miles of transmission lines.

But reviewing that data proved far less scalable.

When Reich visited a utility command center weeks later, she saw hundreds of workers manually scrubbing through video footage, while only a handful of experts knew what to look for.

“It was an ‘aha’ moment,” she recalled. 鈥淲e realized this cannot be the way. If we know what the expert is looking for, why can’t we just clone that expertise?鈥�

The startup鈥檚 founders realized that heavy industries 鈥� energy, oil and gas and agriculture 鈥� face a 鈥渒nowledge crisis鈥� as experienced workers retire faster than they can be replaced.

鈥淔or every young worker entering the energy workforce, 2.4 experienced ones are walking out the door toward retirement. And it’s happening right as energy demand is set to double by 2050,鈥� Reich, the company鈥檚 CEO, told SA国际传媒 News.

Cloneable aims to capture and preserve that kind of institutional knowledge.

In February 2025, it launched Cloneable Field for automated infrastructure inspection targeting the energy sector.聽 Alongside the fundraise, the company is now launching an agentic product that codifies expert knowledge and deploys it as scalable AI agents.

The funding will also support expansion into infrastructure-heavy industries such as public utilities, vegetation management, construction, rail, mining, agriculture and manufacturing.

鈥淭hese are markets chronically underserved by point solutions,鈥� Reich said. 鈥淣o one has combined in-field data collection with agentic automation at the scale these industries require.鈥�

That includes workers鈥� judgment and institutional knowledge not captured in documentation or general AI models, according to Reich. 鈥淐loneable automates workflows that have traditionally been considered too complex for automation,鈥� she said.

The company claims that a process that typically takes a human engineer eight hours, such as structural calculations for a project where a firm is going to replace, upgrade or install 25 utility poles can be completed by a Cloneable agent in under two minutes.

鈥淎 single engineer can process roughly 4,500 to 5,500 poles a year before they hit a capacity ceiling,鈥� Reich said. 鈥淥ur agent runs at 2 million to 3 million poles a year. For a mid-size engineering firm with five to 10 people spending half their time on this work, that’s $115,000 to $312,000 a year in labor that’s not being redirected to higher-value work.

She added: 鈥淭his could be the difference in entire towns being connected to fiber or not over the next 12 months.鈥�

From the field to the back office

The startup says it grew ARR 100x between February and the end of 2025. It has dozens of customers, including , , , and , as well as , which is expanding the 鈥渆xpert cloning鈥� model to livestock and food supply.

Unlike generic AI that requires coding or clean data, Cloneable鈥檚 platform 鈥渟hadows鈥� experts. AI watches an expert perform a workflow, such as a complex utility-pole design. It then captures audio and documentation from the expert in real time. Next, it turns that contextual experience into an AI agent capable of executing the same task.

鈥淥ur differentiation is a decade of lived experience in how these industries actually operate, and the proprietary data and workflows we’ve captured from being inside these companies,鈥� Reich said, adding that everything is highly specific 鈥� from tools to how they鈥檙e configured per customer.

Large foundation model companies focus on the model itself, she said.

鈥淲e’re focusing on a framework that leverages different model types, including small, specific ones,鈥� she said. 鈥淲e clone our customers鈥� knowledge and experience into a small model, which makes it extremely cost-effective to do their work. We’ve built it so all the agent needs to know is: my company, my rules, my industry, my tools.鈥�

Cloneable makes money from its field offering through seat-based licenses per field collection device. With its new agent, charges are per-token and usage-based.

Solving for both data and the agents to act on it

, a partner at Congruent Ventures, said her firm鈥檚 investment in Cloneable was the culmination of many conversations with founders about AI adoption in legacy industries.

鈥淲e’ve seen companies focus either on data capture with complex, expensive, purpose-built hardware 鈥� or on agentic AI for the back office where they struggle to get the high-fidelity data needed to power those agents,鈥� she wrote. 鈥淐loneable has solved both.鈥�

She said the firm is betting on Cloneable鈥檚 team to bring AI to industries where horizontal solutions 鈥渁ren鈥檛 deep enough.鈥�

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was founded in 2023 on the idea that winding down a startup shouldn鈥檛 be chaotic, opaque or a total loss for founders and investors. Since then, it raised just over $20 million from investors including,,, and

Founder and CEO came up with the concept while building his previous company, after a board member asked him to produce a 鈥渟hutdown analysis.鈥� The process proved so complex and time-consuming that it sparked the idea for a software platform to automate and streamline company closures.

Three years later, Los Angeles-based SimpleClosure is expanding that vision with the launch of Asset Hub, a marketplace designed to help founders recover value from what they鈥檝e built 鈥� from source code and operational data to domain names and equipment 鈥� rather than letting those assets disappear in the shutdown process.

SA国际传媒 News spoke with Yona via email about the new product, the rise in startup shutdowns, and how attitudes around failure are beginning to shift.

This interview has been edited for brevity and clarity.

SA国际传媒 News: Are we seeing more shutdowns than in the past?

Yona: We’ve seen strong year-over-year growth in startup shutdown activity over the past few years, and that trend has continued into 2026 so far. Based on our data, in Q1 2026 we saw 2.6x more companies close compared to Q1 2025.

Tell us more about the Asset Hub and why you launched it.

This offering was part of our original vision for SimpleClosure and has continued to be reinforced by feedback from the thousands of founders we have worked with over the past three years.

In our early days, we kept hearing the same thing: Selling off assets was one of the most frustrating and opaque parts of winding down. Companies had spent years building real things: source code, internal tools, operational data, but when it came time to shut down, there was no straightforward way to find buyers or capture any of that value. It just evaporated.

We’re launching the Asset Hub with two initial offerings: Source Code and Workplace Data (beta), but it doesn’t stop there. We’re talking about physical equipment like laptops, domain names, IP 鈥� all the things a company accumulates over its lifetime that still hold value but often get written off or forgotten during the chaos of a shutdown.

SimpleClosure has always been about making the shutdown process more efficient, more compliant and less painful. Asset Hub is the natural next step, moving beyond the paperwork and filings to actually help founders walk away with something tangible from the work they put in.

With the industry now actively seeking real-world codebase and workplace data to train the next generation of models and agents, the timing couldn’t be better. What used to be abandoned assets now have a real market, and we’re in a unique position to facilitate that connection on behalf of our customers.

We’re seeing more capital-intensive sectors like biotech and climate tech beginning to use SimpleClosure. How does the dissolution process change when you’re dealing with physical assets and complex IP rather than just code and a cap table?

The underlying dissolution framework is the same. You still need to wind up the company by settling liabilities, handling assets, and distributing any remaining proceeds in the correct priority order.

What changes with capital-intensive industries like biotech or climate tech is the nature of the assets and obligations, which introduces more complexity in a few key areas: Physical assets require real-world disposition. Instead of just transferring code or shutting down software, you’re dealing with lab equipment, inventory or hardware. These need to be inventoried, stored, sold, or disposed of, often with logistics, costs, and timelines involved. We have some liquidation partners who assist in this area.

IP is more complex and often more valuable. Rather than a codebase, you may have patents, filings or licensed technology. That means more formal valuation considerations, potential buyers or licensing opportunities and additional documentation to properly transfer or assign rights.

The process isn’t fundamentally different, but it’s more hands-on, more document-heavy, and requires tighter coordination across legal, financial, and operational workstreams to ensure everything is properly closed out.

As we move into 2026, 鈥渟imple鈥� rule-based automation is being replaced by adaptive AI. How is SimpleClosure moving toward 鈥淐ognitive Partnering鈥� to help founders make nuanced decisions about creditor negotiations rather than just filing paperwork?

At a baseline, dissolution still requires structured, rule-based steps (filings, notices, sequencing). But where founders really need help is in the gray areas, especially around creditor strategy, tradeoffs, and timing.

That’s where cognitive partnering would come in.

Founders often ask questions such as: Am I allowed to settle this vendor at 50%? Can I pay this vendor first? What happens if I don’t respond?

Rather than giving a single answer, we help frame what’s required in terms of creditor priority, where there’s flexibility, and what risks are introduced by each path.

We’re also able to see pattern recognition across hundreds of shutdowns, and are starting to leverage what we’ve seen across many wind-ups, such as where delays create real risk versus just noise and what 鈥済ood鈥� vs. 鈥減roblematic鈥� outcomes look like.

We also keep humans in the loop for judgment calls. We’re not trying to fully automate these decisions 鈥� they’re too nuanced. The goal is that AI surfaces context, options, and risks while humans make the final call.

We’re moving from: 鈥淗ere’s what to file next鈥� to 鈥淗ere’s how to think about this decision, what your options are, and what the consequences look like.鈥�

Is there a future where your platform provides 鈥渉ealth monitoring鈥� tools to help founders recognize months in advance when a pivot or a clean shutdown is their best fiduciary path?

Potentially. It is definitely something that comes up with founders from time to time.

Making the decision to shut down however, is typically based on more than just company health. It is often an emotional decision for the founder that marks the end of a multi-year journey.

We will never push the founder into a dissolution. The decision has to come from them.

Is it accurate that your company has helped return more than $150 million to investors that might have otherwise been trapped in 鈥渮ombie鈥� companies? Do you see a shift in VC sentiment where a 鈥渃lean failure鈥� is now viewed as a more positive signal for a founder’s next raise than a slow, three-year bleed-out?

Yes! That’s accurate, and as of this week we’ve actually helped return over $200 million to stakeholders.

What we’re seeing from both founders and investors is a real shift in mindset. A few years ago, there was more tolerance for companies lingering, trying to figure it out over long periods. Now, there’s a growing recognition that a clean, well-executed shutdown is often the more responsible outcome.

From a founder perspective, running a thoughtful wind-down (prioritizing creditors appropriately, returning remaining capital and closing things out cleanly) demonstrates strong judgment and integrity. That absolutely carries weight in future fundraising conversations.

From the investor side, capital efficiency matters more than ever. Getting capital back, even partially, and seeing that a founder handled a difficult situation responsibly is often viewed more positively than a prolonged burn with no clear path forward.

鈥淐lean failure鈥� is about communication and trust. Investors keep telling us what stresses them out isn’t a company that’s struggling, it’s a founder who goes dark when things get hard. Investors want the hard truth.

They don’t just want to hear from the founder when things are good. They want to hear when they’re stuck, because that’s when they can actually do something like brainstorm a pivot, restructure, raise a bridge round, or explore an exit or soft landing. Shutting down is almost never the first option they reach for. But they can’t help if they don’t know.

I recently chatted with a founder whose investor wants to back them a third time. Their first company exited. The second was a shutdown. By the third, the investor was writing a blank check because they’d seen this founder handle both the highs and the lows out in the open. The founders who handle the downside cleanly are the ones who get the next yes.

So yes, I would say that we’re increasingly seeing that a clean failure is not only more accepted, but in many cases preferred, because it preserves both capital and credibility.

After exited the shutdown space to back you, how has that partnership changed the way cap table data integrates into the dissolution process? Is the goal a 鈥渙ne-click鈥� shutdown?

Carta has been very helpful. We’ve been working closely with them, and it’s been valuable not just for improving the product (cap table side of things) and user experience, but also for strengthening the brand and reputation venture-backed founders expect.

That said, the reality is that cap table data, even when coming from a system like Carta, still requires validation and context. Companies evolve over time, and by the time they’re shutting down, there are often nuances that need to be reconciled. So while integration helps streamline things, it doesn’t eliminate the need for thoughtful review.

On the idea of a one-click shutdown: We think about that a bit differently. There are definitely parts of the process that should feel close to one-click, such as pulling in cap-table data, generating documents and calculating distributions

But a shutdown is the unwinding of everything that went into building a company 鈥� contracts, obligations, stakeholders, and decisions made over years. There are some decisions that can and should be made by the founder themselves such as how to communicate with employees, investors and vendors. That’s not something that can, or should, be reduced to a single action.

You’ve often said that automating shutdowns helps break the taboo of failure. In 2026, are you seeing founders talk about their SimpleClosure experience as openly as they talk about their seed rounds?

We’re definitely seeing a shift. More founders are talking about their SimpleClosure experience in the same breath as their fundraising story, because for many of them, shutting down is part of the story. I can point to any of our customer testimonials to demonstrate that founders are increasingly willing to share their experience.

But I don’t want to overstate it. The stigma hasn’t disappeared. There’s still a version of founder culture where talking about closing your company feels like admitting defeat. What we are seeing is that SimpleClosure gives founders a way to close with their heads held high 鈥� the process is clean, documented and professional, and that changes both how they talk about it afterward and their relationships with investors, employees, vendors and other stakeholders moving forward.

A significant portion of your users are already working on their next company. What is the most common lesson learned you hear from founders who used your platform to close their first venture?

1. A lot of founders say some version of 鈥淚 knew earlier than I acted.鈥� They reflect that they held on just a bit too long, hoping for a turnaround, a fundraise, or a breakthrough. In doing so, they burned additional time, money, and energy that could have been preserved, as well as saving themselves some extra heartbreak. They could have started their next company already.
2. It’s not just your company or your money. You have obligations to vendors, employees, investors. And those obligations don’t go away just because things didn’t work out. The founders who go through a structured wind-down come out with a much clearer understanding of responsibility, and that shows up very differently in how they operate the next time.
3. How much unnecessary complexity founders take on without a downside plan. Debt, grants, multistate operations, vendor contracts 鈥� all of it feels manageable when things are going well. But during a shutdown, that complexity becomes friction and liability quickly. We’ve seen second-time founders are much more intentional in all fronts, from limiting vendors to debating whether to bootstrap for as long as possible. Indeed, these are many of the same lessons I have learned myself over my career as a founder.

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led the financing, which included participation from , , and . It brings Boulder, Colorado-based Schematic鈥檚 total funding since its 2023 inception to $12 million.

Schematic builds entitlements and enforcement infrastructure for SaaS and AI companies. Put more simply, it serves as a digital gatekeeper for software and AI companies. For example, if a company鈥檚 sales team wants to give a major client a special discount or extra storage, they have to ask an engineer to go in and 鈥渕ove the walls.鈥� The process can be slow, expensive and tedious.

That鈥檚 where Schematic comes in. It essentially acts like a universal remote control for a company鈥檚 features.

Instead of burying those rules in the code, a company can plug Schematic into its product. Then, if it, for example, wants to launch a new “AI Tier” or change how many users a client can have, a person in marketing or sales can flip a switch in a simple dashboard.

鈥淲hen a software company sells you a plan, something inside their product has to enforce what you can do and access based on what you paid for,鈥� said CEO and co-founder . 鈥淢ost companies build that enforcement infra themselves, often badly, and it becomes the thing that slows down every future monetization change. Schematic is the infrastructure that handles it, so engineering doesn’t have to.鈥�

In addition to the fundraise, Schematic is also announcing that payment giant has tapped it 鈥渢o solve entitlements as a first-class primitive: decoupled from code, enforced at runtime, on top of Stripe Billing.鈥�

Schematic will be launching its new Stripe app publicly on stage next week at Stripe Sessions.

Systems like Stripe currently handle the money, sending invoices and charging credit cards. But Stripe doesn’t actually sit inside the app to block or allow a user from clicking a button. Schematic claims it will now serve as the “muscle” that actually enforces the rules that a platform like Stripe sets.

鈥楢n emergent crisis鈥�

By using Schematic, Glover said that companies like went from taking weeks to change their pricing to just 10 minutes. The startup鈥檚 other customers include , and .

AI has made entitlements an emergent crisis, in Glover鈥檚 view.

鈥淣either underlying costs nor customer value are predictable, and both accrue at runtime,鈥� he told SA国际传媒 News. 鈥淭his is why we describe what we’re building as runtime monetization infrastructure: Value is now accruing nondeterministically at runtime, and as a result, pricing and packaging have to be enforced at runtime. A shadow enforcement system catching webhooks from a billing platform cannot support this inflection.鈥�

, general partner at S3 Ventures, said his firm was drawn to invest in Schematic for a few reasons.

鈥淎s operators and through our portfolio companies, we’ve seen firsthand how often pricing changes get delayed or deprioritized because entitlement logic is buried in application code. On top of that, AI is accelerating a structural shift away from seat-based pricing; hybrid and consumption-based models now represent 38% of SaaS companies and that number is rising as companies hone their AI pricing strategies, putting real pressure on legacy monetization architectures,鈥� he wrote via email. 鈥淔inally, Fynn, Ben, and Gio have worked together for nearly a decade, and each of them encountered this specific problem while running pricing and packaging at growth-stage SaaS companies.鈥�

Fintech startups have benefited from increased investment in recent quarters. Total global funding to VC-backed financial technology startups totaled $53.8 billion in 2025, per SA国际传媒 . That鈥檚 a more than 29% increase from 2024鈥檚 total of $41.6 billion raised.

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Several multibillion-dollar megadeals drove the spike in first-quarter investment. That signals investors aren’t just paying for research anymore, but betting on companies that are ready to scale up and put their AI technology into actual cars people can buy or hail.

So far in 2026, we鈥檝e seen a shift to a small number of autonomous vehicle companies capturing a disproportionate share of global capital, with a handful of giants, including ,听 and , getting the lion鈥檚 share of funding.

The broad trend: It appears that investors are no longer spreading small bets across dozens of startups. Instead, they are pouring billions into the three or four players they believe will own the market. And while North America remains the largest hub for overall funding volume, the Asia-Pacific region 鈥� specifically China 鈥� is seeing the fastest growth in deployment. Chinese startups are also raising some of the largest rounds in the space.

The numbers: Autonomous vehicle startups raised a record $21.4 billion across 34 deals through April 15, . That鈥檚 up a staggering 262.17% compared to the $5.9 billion raised across 99 investments globally in all of 2025. It鈥檚 also about 77% higher than the $12.1 billion raised across 127 deals in 2024.

Noteworthy deals

Exactly three-fourths of the $21.4 billion raised in 2026 thus far is attributable to Mountain View, California-based Waymo鈥檚 raised in February. , , and co-led the financing, which was raised at a staggering $126 billion valuation.

But it wasn鈥檛 the only outsized round.

San Diego-based Shield AI landed a $2 billion Series G round co-led by and . Its valuation jumped to $12.7 billion.

And London-based Wayve raised a $1.3 billion Series D round co-led by , and , achieving an $8.6 billion valuation.

Interestingly in 2025, three of the four largest autonomous vehicle rounds were raised by Chinese companies: an $897.7 million Series C by ; a $600 million Series D by , and $527.8 million by .

IPO outlook

We didn鈥檛 see any IPOs in the space in 2025, but some are on the horizon for this year.

Beijing-based confidentially in March. Backed by , and , it is seeking a valuation above $14 billion.

secured $24.7 million in pre-IPO funding in March and is as it expands its AI-led logistics projects.

Because Waymo is a subsidiary of Alphabet (), it doesn’t need to go public for cash, but industry observers are increasingly discussing a potential spinoff. With a $126 billion valuation, it would instantly become one of the most valuable transportation companies in the world if it hit the public market.

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The company described the financing as a 鈥渟eed II鈥� round. It builds upon a $2.75 million 鈥渟eed I鈥� round the company raised in February 2025, bringing its total raised to about $8 million. led its latest financing, which also included participation from and . Existing backers , and also wrote checks into the round.

GetWhys has built and maintains a proprietary 鈥� and growing 鈥� library of in-depth interviews with B2B software buyers. It makes the analysis searchable and usable day to day. Naturally, that dataset only compounds over time. Companies can also connect their own internal intelligence 鈥� such as sales call transcripts 鈥� into its offering.

The Boise, Idaho-based startup then turns that buyer research into 鈥済o-to-market-ready鈥� intelligence that can be used for drafting messaging, content and competitive materials for revenue teams. By using AI, GetWhys says it’s able to automate the most tedious parts of the research process, summarizing hundreds of hours of transcripts or videos.

The interviews give teams 鈥渁ccess to insights that usually do not exist publicly or in their internal docs,鈥� said CEO and co-founder . 鈥淚f a customer hits a gap, they can request net-new research, and we run and add those interviews back into the platform.鈥�

Interestingly, Boutros notes, GetWhys uses humans to gather the information and foundational large language models to do the analysis.

Impressively, the startup鈥檚 customers include the likes of , , , (CDW) and .

鈥楢 new model for a research-based business鈥�

When Boutros teamed up with college friends and former Intel software engineers and to found GetWhys three years ago, he admits that it was 鈥渁 rather different company.鈥�

鈥淲e thought we were building a knowledge base for B2B buyers,鈥� Boutros recalls. But they soon realized there was potential for much more.

While Phan and Honsinger were at Intel, Boutros was working at a small market research consulting firm, an opportunity he was grateful for.

鈥淗ad I worked at a larger firm, I wouldn’t have had such firsthand experience managing customers such as , and , or being taught how to sell,鈥� he told SA国际传媒 News. 鈥淚鈥檇 never have been able to really sink my teeth into what actually happens with research after the presentation.鈥�

Those were the formative years that led the trio to start GetWhys.

And then ChatGPT came out.

鈥淓very person out there seemed to be starting some sort of large language model-based startup, including dozens in market research,鈥� Boutros said. 鈥淎ll of them seem to be building some sort of tooling improvement, and we didn鈥檛 want to do that.鈥�

He added: 鈥淲e thought, fundamentally, if LLMs are good at creating text, there are only two gold mines they鈥檇 ever be able to derive it from 鈥� the text on the internet or within a company鈥檚 proprietary data from sales calls.鈥�

Boutros and Honsinger had just spent eight years doing 鈥渕any millions of dollars worth of qualitative market research鈥� where they would interview people to discover information that was previously undocumented, analyze it and report back to customers.

鈥淲e realized that we could build a research-based business with a new business model,鈥� he said. 鈥淚nstead of collecting the same information time and time again for each customer, we could collect it once, and people could build off of that.鈥�

鈥業n the trenches of B2B market research鈥�

While GetWhys declined to reveal revenue figures, Boutros told SA国际传媒 News that the startup grew revenue more than 10x last year, and has 鈥渄ozens鈥� of customers. Its customers pay a flat annual platform fee upfront, and get unlimited access to its products and the dataset it鈥檚 built on.

鈥淥ne of the things we need to figure out this year is how to provide an offering for smaller organizations, since most of our customers tend to be large enterprises,鈥� he said.

, principal at Epic Ventures, said he was drawn to invest in GetWhys in part because its founders 鈥渟pent a decade in the trenches of B2B market research.鈥�

鈥淭hey understood that the bottleneck wasn’t the lack of data, but the speed and cost of turning that data into action,鈥� he wrote via email. 鈥淲hen we saw them scale ARR 10x in nine months while displacing high-priced alternatives like and , it was clear they had captured lightning in a bottle. They aren’t just building a tool; they’re building a new operating system for GTM teams.鈥�

, a partner at Next Frontier Capital, said her firm doubled down on GetWhys because the startup is demonstrating 鈥渁 rare combination of speed, capital efficiency, and product depth early in its lifecycle.鈥�

She added via email: 鈥淲hat sets GetWhys apart is its proprietary, continuously compounding dataset of verified buyer insights, combined with workflows that turn those insights directly into GTM outputs. Most AI tools rely on generic or public data 鈥� GetWhys is grounded in real customer conversations, which leads to more accurate and actionable outputs.鈥�

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“We have this saying between the two of us,” explains CEO , referring to CTO and co-founder . “If there鈥檚 anything he can鈥檛 do, I can try to figure it out. And if there鈥檚 something I know I can鈥檛 do, I know he can do it.”

That combination 鈥� a blend of deep technical tradecraft and business intuition 鈥� first bore fruit in 2020 with the launch of a digital onboarding startup called . They went on to scale that venture (raising just 1.5 million EUR) before it to the Canadian identity verification company in under two years for more than $50 million.

After the sale, the pair took a brief hiatus before “getting the band back together” to start Spektr in the summer of 2023. This time, they brought along key team members from their previous journey 鈥� CPO and CRO 鈥� to tackle a persistent, expensive problem: the manual drudgery of financial compliance.

Put simply, their new venture, Spektr, provides infrastructure for compliance teams in financial services. It combines configurable workflows with AI agents that execute tasks such as document reviews, ownership mapping and risk analysis 鈥� work that has traditionally been manual and time-intensive.

Today, Copenhagen-based Spektr has raised $20 million in a Series A funding round led by , the company tells SA国际传媒 News exclusively.

Existing backers , and Tech also participated in the financing, which brought Spektr鈥檚 total raised to just under $26 million. The company declined to reveal its current valuation, saying only that it is a significant step up from its February 2024 seed round.

Building bridges

Compliance remains a stubbornly manual field. Analysts spend countless hours cross-referencing documents, researching registries and manually assessing risk. Spektr鈥檚 founders saw a massive misalignment between this sort of rule-based labor and what modern AI was suddenly capable of achieving.

The startup aims to serve as a bridge between the two worlds through its layer of agentic structures that sit atop old-school processes for onboarding, risk assessment and monitoring sanctions lists.

鈥淢ost compliance tools help you manage workflows,鈥� Skarnager said. 鈥淪pektr actually executes the work inside those workflows.鈥�

Its AI agents don鈥檛 just assist, he added. They perform specific compliance tasks end-to-end while maintaining 鈥渇ull transparency鈥� and a human-in-the-loop configuration, so teams 鈥渟tay in control.鈥�

Unlike legacy platforms that offer incremental improvements, such as better data organization, Spektr鈥檚 agents actually perform the analysis, said Skarnager.

“It鈥檚 not just about gathering data,” the CEO told SA国际传媒 News in an interview. “It鈥檚 about making the determination so the human can make the final decision.”

Momentum and market fit

Since launching “spektr 2.0” last August, which fully integrated these agent capabilities, the company has seen a boom in customer adoption.

“Clients really relate to that way of thinking,鈥� Skarnager said. “They鈥檙e used to building an onboarding journey, but now, in the same tool, they have the ability to create agents inside that same structure.”

Companies operating in this space include , and .

These systems play an important role in managing workflows, cases and data across the compliance lifecycle, Skarnager noted.

But, he said, Spektr sits one layer deeper.

鈥淲e automate the underlying execution of compliance work itself through specialized AI agents,鈥� he said.

Scaling the vision

With a headcount of 45 and growing, Spektr is currently focused on the heavy lifting required to serve banks and Tier 1 financial institutions. While the company is rooted in Copenhagen, its footprint is increasingly global.

Its new capital is earmarked for expansion. Plans include building out its engineering team to manage the complex needs of large banks and fintechs. The company also plans to open offices in London and New York to better serve a client base that already includes clients such as , Santander Leasing, , , and 鈥渕ajor鈥� U.S. marketplaces.

NEA partner believes that in a market where AI can mass-produce functionality, Spektr wins through “taste” and deep domain expertise.

The co-founders possess a “rare level of cohesion” and “operate at an instance speed,” allowing them to forgo slides for live demos that directly address use cases, according to Pappas.

Spektr鈥檚 product is architected for a shift where “software screens everything continuously” and experts “handle the exceptions,鈥� he said.

鈥淭his end-to-end automation leads to better decision making and error reduction,” he wrote via email.

Rather than forcing a total replacement of legacy tech at once, Spektr is “the only system that can coexist with existing solutions,” Pappas believes, providing the orchestration needed until “buyers can easily just switch over to spektr to handle everything in one place.”

Fintech startups, particularly those that apply AI to traditionally manual or burdensome processes, have benefited from increased investment in recent quarters. Total global funding to

VC-backed financial technology startups totaled $53.8 billion in 2025, per SA国际传媒 . That鈥檚 a more than 29% increase from 2024鈥檚 total of $41.6 billion raised.

Related reading:

• Fintech Startups Globally Raise More Money In Far Fewer Deals In Q1 2026

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