and co-led the round, with participation from insurance industry veterans including , who will join Slingshot鈥檚 board. Rally is also a co-founder of the Minneapolis-based startup, as part of the firm鈥檚 broader approach. So far, it has co-founded roughly eight portfolio companies that have either launched or remain in stealth.

Slingshot, which is led by first-time founder and CEO , focuses on small insurance policies that consumers can buy as part of another purchase, such as coverage when booking a flight. Known as embedded insurance, the model puts coverage in front of customers when they might need it.

The startup鈥檚 software helps businesses offer those policies by connecting them with insurers that back the coverage. It uses AI to process and share data between the two sides, aiming to reduce the administrative work that can make inexpensive policies costly to offer.

Its first customer and design partner is , which serves more than 750,000 customers at an average premium of $25, according to Slingshot. Genteman told 小蓝视频色情网页版 News that Slingshot鈥檚 first program will launch this month.

For now, Slingshot earns revenue as a technology provider working with outside insurers. But the six-person company, which formally began operating July 1, is also pursuing an acquisition that would allow it to back policies itself.

鈥淲e’re not a carrier yet, but we are rapidly closing on our carrier,鈥� Genteman said in an interview.

Making the math work on a $25 policy

One product Slingshot is working on would let parents buy insurance when signing a child up for youth sports, Genteman said. A parent spending $2,000 on a volleyball league, for example, might be offered a $25 policy covering cancellation or an injury during the season.

Each policy includes paperwork and coordination with an insurer, and can bring potential claims. But when policies cost so little, managing them can quickly eat into the money they bring in, noted Genteman.

He believes that as vertical SaaS companies increasingly look beyond traditional subscription fees for growth, embedded insurance is 鈥渁 largely untapped opportunity鈥� to generate incremental revenue from transactions already happening on their platforms while delivering additional value to customers.

鈥淭he challenge is that the insurance and regulatory landscape is incredibly complex, and most SaaS companies don’t have the expertise, industry relationships or understanding of where to begin,鈥� he said.

Slingshot aims to solve this by providing a white-glove service for those vertical SaaS companies, helping them go from an insurance product idea to 鈥渁 fully operational offering.鈥�

He offered a hypothetical example: A program collecting $500,000 in premiums with a 10% margin would have just $50,000 in margin.

鈥淭hat’s not a viable program for the way incumbent carriers have set themselves up operationally,鈥� he said.

Slingshot鈥檚 goal is to replace the spreadsheets, emails and disconnected systems used to manage that work with software that connects policy distributors and insurers directly. The company says its AI automates data-heavy tasks while keeping a record of decisions that the businesses involved can review and check.

It charges a 5% technology fee to administer programs through its platform. Acquiring an insurer would let it provide both the software and the financial backing, according to Genteman.

A customer helped shape the business

The difficulties facing Slingshot鈥檚 first customer helped bring the startup together.

Rally had already helped start Vertical Insure, which uses software platforms to sell insurance to its customers. According to Genteman, that business had opportunities to introduce more products but struggled to find insurers whose technology and processes could keep up.

鈥淲e can design the products our customers need almost overnight, but the industry is highly regulated, and we need delegated authority just to write and distribute what we鈥檝e built,鈥� Vertical Insure co-founder and CEO said in a statement. 鈥淭he antiquated approval process on the carrier-side slows everything down.鈥�

Even after finding an insurer willing to back a product, connecting the two companies鈥� systems could create more delays, Genteman said.

Slingshot was built to address those problems, with Vertical Insure helping shape its product as a design partner. Noland is also a Slingshot co-founder.

The opportunity in vertical SaaS

Genteman sees a broader opening among vertical SaaS companies: businesses that sell software tailored to a particular industry.

As vertical SaaS companies look for ways to grow beyond subscription fees, insurance could give them another avenue to make money from purchases customers already make through their platforms, argues Genteman. They already know their customers and what they鈥檙e buying, putting them in a position to offer coverage that fits the purchase.

A babysitting platform, for example, could offer coverage to help parents pay for replacement childcare if a sitter cancels at the last minute. Software used by pet boarding facilities could offer coverage for unexpected vet bills if a pet is injured during a stay. An equipment rental platform could let customers add protection at checkout against accidental damage or theft.

Those are examples of what could be developed, Genteman said. Slingshot aims to help vertical SaaS companies take ideas like those and turn them into insurance they can sell, providing both the software and help navigating the insurance industry and its regulatory requirements.

鈥淚nsurance becomes another product that can be offered naturally within the existing customer experience,鈥� he said.

For , managing director at Rally Ventures, that opportunity is familiar. Before joining Rally, he ran , where insurance sold through its software became a key revenue source.

鈥淭hat experience gave me firsthand insight into the opportunity for software companies to generate revenue through embedded financial products,鈥� he wrote via email.

Kaufenberg sees similar potential across Rally鈥檚 portfolio, where most companies combine AI products with financial services. He said founders increasingly want insurance partners that can help them introduce coverage quickly.

Rally co-founded Slingshot to bring insurance backing, underwriting, reporting, claims handling and distribution together in one business.

鈥淭o get the carrier, the reinsurance, the underwriting platform, the data flow engine, the reporting capabilities, the claims handling, the embedded distribution engine, and every other part of the offering from a single vendor is unheard of,鈥� Kaufenberg said.

From insurance executive to founder

Before starting Slingshot, Genteman was chief actuarial officer at publicly traded , where he also led data. He joined in 2019 and helped build its actuarial and data team as the company expanded beyond earthquake insurance into other coverage types.

That work led him to explore whether newer technology could reduce the costs of running insurance programs. About a year ago, he began coding and building prototypes in his basement. He started talking with Rally toward the end of 2025 and left Palomar on July 1 to run Slingshot.

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The partner began his career as a high school math teacher with after studying astrophysics at . He later worked in college access and Yale admissions, and earned graduate degrees in business and education from before joining Reach. As a teacher, he was an early user of , an experience he describes as 鈥済ame-changing.鈥�

His investment firm, Reach Capital, has also evolved. Founded in 2015 after spinning out of , the San Francisco-based firm initially focused on technology for K-12 schools and districts. Its mandate has since expanded to learning, health and work, with investments in companies including ClassDojo, , and .

In August, Reach announced its fifth fund, a $265 million vehicle that brought its assets under management to nearly $1 billion. The firm, which has backed more than 180 companies, plans to invest in approximately 50 additional teams through the new fund, targeting pre-seed, seed and Series A rounds.

小蓝视频色情网页版 News spoke with Kim about how Reach鈥檚 education roots inform its broader strategy, what it saw in AI detection company , and why some of its best investments initially divided its own team. We also discussed how ownership stakes and secondary transactions have shaped the firm鈥檚 approach to returning capital.

This interview has been edited for length and clarity.

小蓝视频色情网页版 News: You started your career as a high school math teacher. How did that shape your view of education technology?

Kim: I taught ninth through 12th grade, mostly Algebra I and geometry. I was very early to ClassDojo. Back then, it was a very simple classroom management tool, and it was game-changing for me. It has since evolved into a sprawling suite, but I already knew technology could move the needle.

Reach began as an education investor. How did the firm鈥檚 focus evolve into learning, health and work?

Kim: Reach started in 2015, and our original focus was exclusively on learning and education technology 鈥� more specifically, technology sold into schools and districts.

Our founding partners were seeing the growth of devices in schools, one-to-one device ratios and broadband penetration. The belief was that you would start to see a proliferation of technology serving students, teachers and administrators and moving the needle on learning outcomes. That is what ended up playing out. Some of our early bets are now household names in edtech, such as , and ClassDojo.

Reach spun out of NewSchools Venture Fund, where three of our four founding partners had started doing for-profit seed investing before 2015. Over time, we branched out from K-12 into higher education, and then into consumer learning.

We also started getting pulled toward healthcare and work. Health and wellbeing affect a learner鈥檚 readiness for the classroom, so we were being pulled toward mental health, behavioral health and developmental health. On the work side, formal learning typically ends in college, but you still need the bridge to a job. Once you鈥檙e on the job, you need to continue learning, especially in the current environment.

A few years ago, we officially put a stake in the ground and said we invest across learning, health and work. The common thread is that these are the most important systems underlying human development and flourishing.

So far, the breakdown has been roughly 50% learning, 25% health and 25% work.

These sectors have different buyers, regulations and business models. What did investing in education teach you that translates to healthcare and work?

Kim: Education is highly regulated, and institutional buying is complex. Many stakeholders must provide input, and decisions often require board-level approval. We were familiar with and comfortable with regulated products and complex go-to-market motions.

Selling to a university or a K-12 district, in terms of the complexity and the need to intersect with regulation and policy, is quite comparable to selling to a health system, hospital or insurance carrier.

Cartwheel is a good example of where our experience gave us an advantage. It provides mental health teletherapy, but the customer is the school district and the care setting is in schools.

While we were relatively new to behavioral health when we made that investment, we were very familiar with selling to school districts and implementing an intervention within a school setting. That gave us an advantage both in winning the investment opportunity and supporting the company afterward.

Reach talks about 鈥渆levating human potential.鈥� What makes an investment fit that mandate?

Kim: We look for products and services that increase access to quality education, quality care or quality earnings, or some combination of those. We really have to see the mechanism for that happening.

It鈥檚 not good enough to be pitched a SaaS or AI product and have the founders say, 鈥淭his will make people more productive at work, and therefore maybe they will earn more or enjoy their jobs more.鈥� That鈥檚 plausible, but it isn鈥檛 a good enough theory of how the product actually increases access to quality earnings.

Several teammates, including myself, are former educators. We care about meaningful, needle-moving impact.

We address access from both the demand side and the supply side. On the demand side, we build products that create delightful, impactful experiences for consumers: , Stepful鈥檚 allied healthcare training, and Replit for what its founder calls 鈥渃itizen developers.鈥�

On the supply side, it鈥檚 helping schools, health systems and companies function more efficiently and effectively. , for example, helps logistics and hospitality companies hire hourly workers better and treat them better. We think about the problem from both sides of the market.

Reach backed GPTZero, which agreed to join . Was that an investment in solving a problem created by AI?

Kim: That wasn鈥檛 actually our investment thesis. In my opinion, they have the best AI detector on the market. But their thesis was that AI use in education should not be banned. It shouldn鈥檛 be punitive or become a cat-and-mouse game between the educator and the learner.

Since all of us as professionals use AI in our jobs, students should learn how to use it responsibly and thoughtfully. It was the first AI detector company I had seen with that positioning, as opposed to helping schools, districts and universities clamp down on AI usage.

That鈥檚 why I liked it. I don鈥檛 see it as a problem for students to use AI for certain purposes, such as brainstorming, organizing thoughts and editing. GPTZero helps educators work with students and teach them how to use AI responsibly.

Do you see other opportunities to build companies around the problems AI creates?

Kim: Yes, most notably in AI governance and evaluations.

Going back to regulated categories, you need to make sure AI is not irresponsibly using personally identifiable information in education or healthcare. Those categories have very strict regulations, and infrastructure companies are emerging to help ensure AI products interact with that data responsibly.

I do think there are opportunities for large, successful, profitable companies to be created in response to the problems AI creates.

What does GPTZero鈥檚 sale tell you about which AI products can become durable standalone companies and which belong within larger platforms?

Kim: I don鈥檛 know whether GPTZero is an example of either. They had been fielding acquisition offers from pretty early on. This particular opportunity arose earlier this year, and there was a compelling synergy story with Superhuman, formerly .

The founders and the board agreed that it made sense to combine the companies. But they could just as easily have remained standalone. There is another AI detection company called that鈥檚 quite good and is still a standalone, venture-backed company.

It鈥檚 not clear to me that this category can鈥檛 exist on its own. We have to be very case-by-case. We don鈥檛 know until we see the trajectory, financial sustainability and evolution of the market, whether it makes sense to explore a strategic acquisition or grow independently and perhaps one day become a publicly traded company.

Reach has backed more than 180 companies. What patterns have you noticed among those that exceeded expectations?

Kim: The reality of venture is that a very small share of investments yields the overwhelming majority of returns. Given the outlier nature of those successes, it鈥檚 hard to identify unifying principles that always predict a successful exit. If we could, we would only make those investments.

But there are a few things we think are somewhat common across our wins. One is a very smart, gritty, genuinely mission-driven founder or founding team, often with a strong personal connection to the problem.

Second is some sort of nonconsensus insight about the market. That often comes from experiencing the problem themselves, sometimes as practitioners in the industry 鈥� a former educator, for example.

Third is a defensible and scalable business model.

The nonconsensus insight is funny because it鈥檚 what makes these companies look weird and maybe uninvestable in the beginning. Even within our team, they鈥檙e often the most polarizing opportunities. You look at the votes, and a bunch of people vote no, but it gets just enough yeses to pass. Then, 10 years later, it鈥檚 one of our best investments.

People lose sight of this in the current environment, but the best opportunities are often the most nonconsensus. Those can be the most creative and interesting founders, and you can get in at reasonable valuations early and help them become consensus bets.

Reach says its first two funds have returned investors鈥� capital. What produced those returns?

Kim: We鈥檙e no exception to the power-law distribution in venture. Our first fund has been returned 2.2x by literally two investments. Our second fund has been returned 1.2x by one investment. These are cash distributions, by the way.

Especially in the second fund, there鈥檚 still remaining value, so we鈥檒l see how high that number gets. One fund-returning exit would be great; two or three would be amazing. That鈥檚 what we鈥檙e striving for.

How has that experience changed your approach to ownership and follow-on investing?

Kim: We鈥檝e had some great exits where, given how prominent the company is, you might be surprised that our share of the returns wasn鈥檛 that large. That鈥檚 purely an ownership calculation.

From our first fund to today, we鈥檝e meaningfully increased our initial ownership target. We now try to get as close to 15% ownership upfront as possible.

That means writing slightly bigger checks and reserving relatively less for follow-on investments and more for the initial check. It鈥檚 important for the exit math and distribution math.

What role do secondary transactions play in getting liquidity?

We鈥檝e become very proactive and conscientious about liquidity opportunities. Much of our distributions to investors come from secondary sales 鈥� selling our shares in a subsequent round, before an actual exit.

Whenever there鈥檚 an opportunity, we evaluate it. We consider how long we鈥檝e been in the company, how much we can get out, and how much upside we think remains.

As companies stay private longer, early-stage investors are achieving liquidity much more often through secondary transactions. I like to think we were early to that trend, but it鈥檚 certainly mainstream now.

Sometimes, when we see enough upside, we鈥檒l buy secondary shares. GPTZero was an example: After our Series A investment, we bought secondary shares, and I鈥檓 really glad we did because it worked out well. For us, we sell more often, but occasionally we buy.

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• 鈥楴obody Wanted to Give A Former Principal Money鈥�: How An Educator Built An Edtech AI Startup With $63M From VCs

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All told, fintech startups raised $10.6 billion globally in the third quarter of 2026, down 28.4% from $14.9 billion in Q2 2026 and 21.4% from $13.5 billion in Q3 2025.

The pullback comes as investors draw sharper distinctions between companies attracting capital and those struggling to raise, according to partner .

鈥淭he market remains bifurcated 鈥� AI vs. not and market leaders vs. not,鈥� she said.

founder similarly described capital concentrating in fewer, larger rounds, with infrastructure, payments and stablecoin rails attracting investor interest, supported by greater regulatory clarity.

Deal count drops

Notably, deal count in Q3 was the lowest in the quarters covered by our analysis, dating back to the first quarter of 2018. Just 687 fintech funding rounds were recorded globally in the third quarter, compared with 789 in the previous quarter and 995 in the same period a year earlier. That represents declines of 12.9% quarter over quarter and 31% year over year.

Investors are demanding stronger evidence of performance before writing checks, noted Green. For Series A and B rounds, 鈥渆vidence counts for more than narrative,鈥� she said.

In emerging categories, actual usage and momentum can matter more than revenue multiples. Elsewhere, investors want efficient growth, a credible path to profitability and stronger evidence of lasting customer demand before committing to a large Series B, Green said. More startups are also combining debt and equity to limit dilution.

Pullback in large rounds

The quarter-over-quarter funding decline was largely driven by a pullback in large rounds. Funding from rounds of at least $100 million fell 43.3%, from $9.07 billion in Q2 to $5.15 billion in Q3, accounting for approximately 93% of the overall dollar decline. Funding from rounds below $100 million declined just 5.1% sequentially.

Compared with Q3 2025, funding from rounds of at least $100 million decreased 7%, while funding from smaller rounds fell 31.4%. Large transactions remained more resilient than smaller-round funding year over year.

That divide also extends to pricing. Green said a small group of companies with clear momentum command aggressive valuations, while others face greater discipline.

Bock pointed to and as similarly sized businesses with widely different valuations, attributing Ramp鈥檚 premium to investor enthusiasm for its AI momentum and growth trajectory.

Global mega-rounds

The United States remained the largest market for fintech funding last quarter. All told, U.S. startups attracted $5.38 billion, or 50.5% of the $10.64 billion global total in Q3.

The next-largest markets were Singapore with $1.28 billion (12.1%); Saudi Arabia with $626.6 million (5.9%); India with $452 million (4.2%); Brazil with $352 million (3.3%); and the United Kingdom with $337.8 million (3.2%). Singapore鈥檚 total was heavily concentrated in 鈥檚 $1.2 billion round. Despite the U.S. being the largest market for fintech funding, the quarter鈥檚 five largest funding rounds went to companies based outside the United States, ironically.

• Ant International, a Singapore-based provider of digital payments and financial services for businesses, raised the largest round in Q3 2026, a $1.2 billion Series A at a listed $11.2 billion post-money valuation. The data does not identify a lead investor but lists and as participants.
• , a financial app provider based in Riyadh, Saudi Arabia, raised a $329.5 million Series A at a $1.85 billion post-money valuation. , and participated in the financing.
• Hong Kong-based , which provides financing to e-commerce businesses, secured a $300 million Series C led by at an estimated $1.36 billion post-money valuation.
• Jakarta, Indonesia-based , an online investing platform, raised a $270 million Series C led by at a $1.35 billion post-money valuation.
• Rounding out the top five, Riyadh-based , a digital financial services provider, raised $233 million in a Series F round led by at a $6.5 billion post-money valuation.

Late-stage fintech funding fell to $4.8 billion, down 40.4% quarter over quarter and 17.3% year over year, in Q3, per 小蓝视频色情网页版 data.

One startup lifts early-stage funding

Early-stage fintech funding was comparatively resilient at $4.23 billion, up 11% quarter over quarter and down just 0.6% year over year. However, Ant International鈥檚 $1.2 billion Series A accounted for 28.4% of that total. Excluding that transaction from Q3 2026 alone, early-stage funding would have fallen 20.4% sequentially and 28.8% year over year.

Early-stage deal count also dropped, from 173 in Q2 to 165 in Q3, suggesting that the increase in funding dollars did not reflect a broad expansion in activity.

Fundraising expectations are rising as AI makes products faster to build and launch, Bock said. Early revenue carries less weight than sustained growth. While $1 million in annual recurring revenue once served as a common Series A benchmark, investors now want greater traction and stronger momentum, including revenue that has at least tripled over six months.

鈥淓fficiency still matters, but growth is back in focus,鈥� she said, adding that expectations of larger potential outcomes are pushing valuations higher.

Meanwhile, angel and seed funding totaled $1.12 billion, down 19.7% from Q2 and 35.2% from Q3 2025.

AI reshapes investor priorities

AI is primarily redirecting investment within fintech, although agents and payments are attracting some new capital, Green said. Companies without an obvious AI offering can still raise funding, but investors increasingly expect evidence that the technology improves their economics, speed or ability to reach customers.

鈥淭he product has to carry that story,鈥� she said.

Bock said the distinction between companies adding AI to existing processes and those building their products around it matters. The strongest AI products deliver substantial improvements that drive rapid adoption and customers鈥� willingness to pay.

鈥淲hen investors say a company is 鈥楢I,鈥� they usually mean its growth rate is high, so growth has become the proxy,鈥� she said.

QED鈥檚 recent investments span customer support, analytics, financial advice, banking infrastructure and fraud prevention. Bock sees a durable opportunity for startups that handle the specialized integrations and accuracy requirements financial services demands 鈥� work she argues frontier AI labs are unlikely to prioritize.

Green, meanwhile, said payments initiated by AI agents have moved 鈥渇rom 鈥榠f鈥� to 鈥榳hen鈥欌�� in her conversations, as major networks and processors introduced protocols to support them. She is also seeing more fintech companies seek ownership of regulated infrastructure rather than rely on outside providers.

But she sees potential overfunding in stablecoin infrastructure, where many startups are pursuing the same customers, and in tools that let AI agents complete purchases for buyers, where, in her view, startup formation has outpaced transaction volume.

Green sees more opportunity in the systems that make agent-driven commerce trustworthy, including fraud prevention, identity verification, agent permissions and tools that help merchants accept those purchases.

鈥淚 suspect the market is treating the rails as the moat,鈥� she said. In her view, payment infrastructure is likely to become commoditized, leaving more value with the companies that own customer relationships and earn their trust.

Bock鈥檚 broader thesis is that AI could reshape financial services by sharply reducing the costs of underwriting, compliance and customer service while enabling more personalized financial products. QED is particularly interested in advice and wealth management, underwriting, insurance claims, risk and compliance, and finance back-office operations, alongside stablecoins and tokenization.

鈥淭he financial services operating system will be rebuilt end-to-end,鈥� she said.

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Methodology

The data contained in this report comes directly from 小蓝视频色情网页版, and is based on reported data. Data is as of Oct. 5, 2026.

Note that data lags are most pronounced at the earliest stages of venture activity, with seed funding amounts increasing significantly after the end of a quarter/year.

Please note that all funding values are given in U.S. dollars unless otherwise noted.

小蓝视频色情网页版 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 小蓝视频色情网页版 long after the event was announced, foreign currency transactions are converted at the historic spot price.

Related reading:

• Fintech Funding Surges 23% In H1 2026 As Investors Concentrate Their Bets On AI And Financial Infrastructure
• 小蓝视频色情网页版 Data: Q3 2026 Posted A Record Count Of Billion-Dollar Rounds As The Global AI Race Heats Up
• LatAm Startup Funding Jumps 26% In Q3 As VCs Begin 鈥楻ediscovering鈥� The Region
• North America鈥檚 Startup Funding Falls In Q3 As AI Giants Eye The Public Markets

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All told, startups in Latin America raised a combined $1.3 billion across seed- and growth-stage deals in the three-month period ending Sept. 30. That was up 26% year over year but down 9% from the second quarter.

For perspective, we charted out total investment, color-coded by stage, for the past 12 quarters below.

Of the quarterly total, $757 million went into late-stage and growth deals, up 74% compared to the $435 million that flowed into such deals in the third quarter of 2025. However, that is down 21.3% from the $962 million in late-stage and growth rounds raised by LatAm startups in the second quarter. (Although it鈥檚 worth noting that Q2 2026 represented the most raised at that stage since the second quarter of 2022.)

Mexico startups have raised $1.52 billion in venture funding during the first three quarters of 2026, per 小蓝视频色情网页版 data. Notably, that鈥檚 about 40.7% more than the $1.08 billion Brazil鈥檚 startups raised in the same period. This is unusual as Brazil has historically been the largest recipient of venture funding in Latin America.

Still, Brazil continues to attract a broad range of investors.

鈥淥ver the past year, we have seen investments into Brazil from firms including , , , and ,鈥� , managing partner of , told 小蓝视频色情网页版 News. 鈥淔or several, this marked either their first investment in Latin America or their first in a long time.鈥�

, Endeavor Catalyst鈥檚 vice president and head of Latin America, noted that Mexico drew more venture funding in the first half of 2026 than Brazil. But much of 惭别虫颈肠辞鈥檚 funding went to a few startups, he pointed out, while Brazil recorded many deals.

鈥淚nvestors are concentrating on a few Mexican winners, while Brazil retains the breadth,鈥� Piquet said, adding that Brazil also leads in realized exits.

Table of contents

• Outsized rounds
• An uptick in seed funding
• ‘Another rediscovery moment’
• Methodology
• Glossary of funding terms

Outsized rounds

Several mega-raises took place in Latin America in the third quarter. And notably, the two largest funding deals were not for companies in Brazil or Mexico, which have historically raised the largest share of venture capital in the region. They also weren鈥檛 to fintech, which typically has the most funding recipients in the region, but rather to energy-related startups.

, the Chilean project company for 鈥檚 Black Giant lithium project, raised $225 million in a July round at a reported $900 million valuation. And Colombia鈥檚 , a clean energy company that builds and operates solar power systems for homes and businesses, landed a $129 million Series C in August at a reported $600 million valuation.

惭别虫颈肠辞鈥檚 , an AI-powered financial services company, raised a $125 million Series C in September at a $1.6 billion valuation. led the financing, which included a senior debt funding facility provided by .

And in August, Brazilian fintech brought in $106.9 million in an equity and debt seed round at a $626 million valuation.

Also in August, Brazil鈥檚 , an AI-native wealth advisory company, landed $85 million in聽 what 小蓝视频色情网页版 shows is the largest seed funding deal ever raised for a Latin American startup. Founded by , former CTO of , and , founder of , the startup is backed by ,,, and .

An uptick in seed funding

Latin America experienced a marked surge in very early-stage funding totals in the third quarter, largely thanks to the seed rounds mentioned above.

Of the $1.3 billion raised by Latin America鈥檚 startups in the third quarter, about 20.5% 鈥� or $266 million 鈥� was raised across the angel and seed stages.

That鈥檚 up 128% from the $117 million raised across those stages in the second quarter of 2026, and up 62% from the $165 million in the same quarter last year.

Just over 22%, or $288 million, was raised at early stages, down 18.9% from $355 million in the second quarter and 34% from $437 million in the same period last year.

We expect the Q3 deal counts to rise somewhat over time; however, as seed rounds in particular are commonly reported weeks or months after they close.

‘Another rediscovery moment’

Latin America is drawing renewed interest from international venture investors, according to Endeavor鈥檚 Taylor. He said international VCs were more active in the region, particularly Brazil, during the first three quarters of 2026 than in 2024 and 2025 combined, through both new investments and follow-on funding.

鈥淚 joke that investors from the U.S. 鈥榬ediscover鈥� Latin America every five or six years, and I think we are approaching another rediscovery moment,鈥� Taylor told 小蓝视频色情网页版 News.

Endeavor Catalyst recently closed its oversubscribed $320 million fifth fund. Latin America accounts for nearly 40% of its portfolio by both deals and dollars, and Fund V had made 14 investments in the region as of Sept. 22. Taylor also pointed to opportunities beyond Brazil and Mexico, including repeat founders building global businesses from Colombia and emerging companies in Venezuela and Central America.

Liquidity is improving, although acquisitions and secondary transactions are providing more opportunities than public listings, Piquet said. He pointed to approximately 60 private Latin American technology companies that have each raised at least $150 million as a potential pipeline for future IPOs.

鈥淭he exit door is open, although it is not primarily opening through IPOs yet,鈥� Piquet said.

Taylor expects more exits could help bring global investors back consistently, with potential listings by companies such as , or contributing to another 鈥渞ediscovery moment鈥� in 2027.

International investors remain interested in Latin America but have become more selective about deploying capital, according to of . Firms with a longstanding presence in the region remain engaged, while many that entered during the 2020鈥�2021 investment boom have shifted toward AI opportunities elsewhere and are concentrating on existing portfolios, she noted.

鈥淕lobal investors are increasingly investing behind themes rather than geographies,鈥� she said, putting Latin American startups in more direct competition with companies in the U.S. and other markets.

Investor interest is increasingly centered on AI-native businesses and financial infrastructure, particularly stablecoins and cross-border payments. Gadala-Maria said more founders are targeting global markets from the outset, marking a departure from the previous wave of startups that adapted established business models to underserved local markets.

鈥淚ncreasingly, we鈥檙e seeing founders build globally competitive technology companies from Latin America, rather than necessarily for Latin America,鈥� she said.

Gadala-Maria agrees that Brazil and Mexico remain the region鈥檚 largest markets, with Brazil offering a deeper, more mature startup ecosystem and Mexico providing substantial room for financial services innovation. She pointed to Argentina as an overlooked market, citing its strong technical talent and founders building companies for global customers.

Correction: Details regarding Kapital’s Series C were updated. The deal was a debt and equity raise.

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Methodology

The data contained in this report comes directly from 小蓝视频色情网页版, and is based on reported data. Data is as of Oct. 5, 2026.

Note that data lags are most pronounced at the earliest stages of venture activity, with seed funding amounts increasing significantly after the end of a quarter/year.

Please note that all funding values are given in U.S. dollars unless otherwise noted.

小蓝视频色情网页版 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 小蓝视频色情网页版 long after the event was announced, foreign currency transactions are converted at the historic spot price.

Glossary of funding terms

Seed and angel consists of seed, pre-seed and angel rounds. 小蓝视频色情网页版 also includes venture rounds of unknown series, equity crowdfunding and convertible notes at $3 million (USD or as-converted USD equivalent) or less.

Early-stage consists of Series A and Series B rounds, as well as other round types. 小蓝视频色情网页版 includes venture rounds of unknown series, corporate venture and other rounds above $3 million, and those less than or equal to $15 million.

Late-stage consists of Series C, Series D, Series E and later-lettered venture rounds following the 鈥淪eries [Letter]鈥� naming convention. Also included are venture rounds of unknown series, corporate venture and other rounds above $15 million. Corporate rounds are only included if a company has raised an equity funding at seed through a venture series funding round.

Technology growth is a private-equity round raised by a company that has previously raised a 鈥渧enture鈥� round. (So basically, any round from the previously defined stages.)

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He also discovered mass arbitration, a way to pursue individual claims when companies鈥� contracts prevent customers from joining class actions. At the same time, he also realized that relatively few law firms were set up to manage thousands of those claims at once.

That gap became the basis for , which has raised $10 million in Series A funding led by to help consumers pursue claims over alleged privacy violations, hidden fees and subscriptions that are difficult to cancel. , , , , , , , and also participated in the financing.

The New York-based startup works with law firms to identify potential cases, find affected consumers and gather information needed to pursue compensation. The round brings its total funding to $15.7 million, which includes a $3.6 million seed investment in April 2025 and an earlier pre-seed round.

Chariot鈥檚 latest round comes amid a surge in investment across legal tech. Funding to the legal tech space hit a record high last year, with $4.6 billion invested, per 小蓝视频色情网页版 . So far this year, legal tech startups have pulled in more than $3 billion, putting the sector on pace to match or exceed the 2025 total.

The Chariot process works like this: For each case, consumers complete an intake questionnaire that assesses preliminary eligibility in real time. Chariot鈥檚 team then reviews these pre-qualified claims through a multi-layered system of technology and human oversight before sending it to the partner law firm handling the case. Hang, the company’s CEO, told 小蓝视频色情网页版 News that Chariot typically receives more than 200,000 claim submissions per month and that it has taken on roughly 80 cases since shifting to its current business model just over a year ago.

The company has about 15 employees and is currently breaking even, he said. It plans to spend the new funding primarily on hiring and product development, including AI tools to review cases and help determine whether consumers qualify to participate.

A gap in consumer legal services

Chariot is Hang鈥檚 third startup. His first company grew out of sleep research he conducted while studying psychology and neuroscience at . He turned that research into a smart-pillow business and later sold its intellectual property to a medical-device operator. He later worked in healthcare technology and fintech, founded a startup in the Web3 space, and then moved into legal technology.

Across those industries, he said, he encountered complicated systems in which the interests of businesses and their customers did not always align. His experience helping the class-action settlement startup brought those concerns into focus.

He described being 鈥渇rustrated and mystified and horrified by how our justice system works.鈥�

Chariot started about three years ago, adopting its current model after Hang saw an opportunity to help law firms handle mass arbitration. Firms accustomed to class actions could struggle with the work required to manage large numbers of individual claimants, he said.

鈥淚t’s just really inefficient for them to try to build workflows around 10,000 claimants or 100,000 claimants,鈥� Hang said.

Chariot handles parts of that administrative work, while its partner firms handle the legal claims. The startup charges the firms flat service fees, and they work on a contingency basis. Hang emphasizes that since it鈥檚 not a law firm, Chariot itself doesn鈥檛 file claims.

It helps consumers pursue a variety of claims. Those claims include allegations that websites or apps tracked their information without consent, that companies added fees during checkout, or that businesses made subscriptions easy to start but difficult to cancel, for example.

Finding evidence and drawing objections

Chariot starts by looking for potential cases involving privacy violations, hidden fees and other consumer complaints. Its software scans public records, including federal court filings, and uses AI to analyze the allegations and prepare research for its partner law firms.

That research includes legal memos based on publicly filed complaints. Chariot is also developing tools to track changes in laws and court decisions that could affect whether a case can proceed. The work remains in the early stages, with the goal of automating more of the research that associates and paralegals would otherwise do, according to Hang.

Chariot reviews a few hundred potential cases each month, he said. It tests ads for about 30 potential cases to gauge whether it can find consumers who may have a claim, then selects roughly 10 to pursue more extensively with its partner law firms. Each case can involve many individual consumers, whom Chariot reaches through advertising and partnerships with consumer financial technology companies.

To determine whether they have a claim, consumers must provide information to help assess it. For example, in the case of a data breach, that could be a notice from the company saying their information was exposed. For a hidden-fee complaint, it could include what they purchased, when they bought it, and how much they paid.

Chariot is building AI tools to help review that supporting information and determine whether a consumer has a claim, according to Hang. The required evidence, and the difficulty of verifying it, varies by the type of alleged violation.

The startup plans to make that step easier by allowing users to connect their email accounts so its software can look for relevant documents, rather than asking consumers to fill out forms.聽 Hang estimates that such improvements could help the company handle 10x to 100x its current claim volume.

Companies push back

Not everyone welcomes Chariot鈥檚 approach. Advertising for potential claimants has brought strong objections from some of the companies involved. In fact, Chariot has received cease-and-desist letters challenging, among other things, its use of company names and trademarks in ads, noted Hang.

But he asserts that identifying those businesses is necessary to reach people who used their products or services.

鈥淎s a part of our business, we need to mention the name of the company that has this alleged violation,鈥� he said.

Chariot reviews the letters with legal counsel and complies with demands it considers valid, Hang said. He declined to name specific companies for this article.

His broader goal is to make alleged consumer abuses more costly for businesses. If more affected customers pursue compensation, he reasons, companies will have a stronger financial incentive to change those practices.

鈥淲e want to make unethical unprofitable,鈥� he said.

, general partner at Silverton, told 小蓝视频色情网页版 News via email that his firm was attracted to Chariot鈥檚 vertical AI approach to working with law firms, which he called 鈥渆xciting and defensible.鈥�

鈥淚t鈥檚 a greenfield market with space to expand horizontally and eventually into a generalized consumer legal AI solution,鈥� he wrote. The firm was also drawn to what Dodd described as 鈥渁 business model that aligns capitalistic incentives with access to justice.鈥�

鈥淲hen a company breaks the law and breaks what it promises to its consumers, the consumer rarely seeks justice,鈥� he added. 鈥淐hariot has built the technology to make individual consumer claims work at scale, and the team to operate it in a category where execution is genuinely hard.鈥�

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Two decades later, Herrin is building a business around the marketing problems she encountered along the way. She started in 2025 to help consumer brands decide which products to promote and prepare their campaigns. The startup has now raised $5 million in seed funding, it tells 小蓝视频色情网页版 News exclusively.

led the round, joined by and angel investors including former and current leaders.

Marklo helps consumer brands plan marketing campaigns using their sales, inventory and past marketing data. Its software shows which promotions drove sales, suggests what products to promote next and uses AI to draft campaign briefs and organize tasks.

While Marklo is still pre-revenue, more than 100 brands are already using its software in a pilot, according to Herrin. Those brands include , , , and .

The Burlingame, California-based company is now opening its software to the public ahead of the holiday shopping season. It plans to use its new funding to hire engineers, develop the product and expand customer support.

Planning campaigns

To decide what to promote next, marketers first need to understand what drove past sales. That can be a challenging endeavor when sales, inventory and marketing records all sit in separate systems.

Marklo collects that information from Shopify and services such as Klaviyo, , and , then puts past launches, promotions and sales into a calendar. Teams can see, for example, whether a rise in sales attributed to paid advertising coincided with a new product launch or a widely shared social post 鈥� factors they might miss when reviewing each service separately, Herrin said. Marklo can then draft a campaign plan and create related tasks for the team to review, edit and assign.

Using those records, along with competitors鈥� promotions and social media trends, Marklo suggests what a brand should market next. It also considers what the retailer has in stock and when a promotion would run.

From there, teams can use the software to plan campaigns, draft marketing materials and assign work, with suggested campaigns and reports also delivered through or email.

Herrin said the lack of broader-picture context with other AI tools frustrated her. For example, a tool might identify a Fourth of July sale as a successful promotion and suggest repeating it.

鈥淗ey, no, I can鈥檛. It鈥檚 not Fourth of July. We don鈥檛 have the inventory anymore,鈥� she recalled thinking.

In one case, Herrin described how Marklo identified a color trending on social media and a matching handbag a retailer had in stock but hadn鈥檛 promoted recently. It offered to plan a post based on one that had performed well before.

Latico Leathers offers another example of how a brand uses Marklo. The company used the software to plan a month’s worth of marketing in one day, a process that had previously taken most of a week across meetings, spreadsheets and five tools, according to Herrin.

In the end, humans review and approve Marklo鈥檚 proposed work. The company says it does not use customer data to train AI models.

As for its revenue model, Marklo plans to charge a monthly subscription fee, with additional charges based on usage. For now, brands can install and try it for free.

鈥淚’m very passionate about small and medium businesses, and I want to make it accessible to every single brand that runs on Shopify,鈥� Herrin told 小蓝视频色情网页版 News in an interview, 鈥渟o that they can operate like a big brand.鈥�

A change of direction

The decision to build Marklo followed a change in how Herrin thought about Stella & Dot鈥檚 future.

That company began as a way for women to earn money on their own schedules by hosting trunk shows in their own homes. It expanded into apparel and skincare and paid out more than $500 million to its sellers, Herrin said.

鈥淔or me, it was never about the jewelry,鈥� she told 小蓝视频色情网页版 News.

When COVID-19 put a stop to in-home gatherings, business dropped 80% almost overnight. Herrin also realized that women who might once have signed up to sell Stella & Dot products suddenly had other options. They could promote multiple brands as influencers or open their own Shopify stores.

She described Stella & Dot as 鈥渢he COVID victim that had pre-existing conditions.鈥�

Rather than rebuild the old model, Herrin sold the businesses and brands separately. Later, she bought back the Stella & Dot brand to sell through a different channel and test Marklo.

She was also reconsidering what she wanted to do next. As she approached her 50th birthday, Herrin questioned whether she would choose to start the same business again.

鈥淎s an entrepreneur, you don鈥檛 just start a business; you start a life,鈥� she said.

She still wanted to help people build businesses. What had changed was the problem she wanted to solve: Opening an online store had become easier, but managing its marketing still meant gathering information from several systems and coordinating the next campaign.

鈥淭he mission was the same, but the solution needed to evolve,鈥� Herrin said.

A return to venture capital

For Marklo, Herrin also returned to venture funding after relying more on profits to grow Stella & Dot.

She had raised venture capital for her first company, which became , but came away feeling that it had raised too much money. She took a different approach with Stella & Dot, building it with about $2 million in capital and a focus on profitability.

鈥淗ere鈥檚 a new growth strategy,鈥� Herrin recalled thinking. 鈥淗ow about profits?鈥�

With Marklo, she wanted to hire engineers and develop the software faster. Its 11-person team includes engineers who previously worked at Shopify and Google. She also sought investors with commerce experience.

鈥淩aising capital isn鈥檛 just about having money for expenses,鈥� Herrin said. 鈥淚t鈥檚 about bringing a brain trust together.鈥�

Experience as 鈥榯he customer she鈥檚 building for鈥�

, founder and managing partner of Forerunner Ventures, said Herrin鈥檚 experience building Stella & Dot was a central reason for backing Marklo.

鈥淛essica uniquely has been the customer that she鈥檚 building for right now at every stage, from starting a brand to having a brand at scale,鈥� Green told 小蓝视频色情网页版 News in an interview. That experience gives Herrin firsthand knowledge of where teams lose time, which decisions affect profitability and what brands need before trusting new technology, she added.

While AI in general makes it easier to produce marketing content, Green sees the greater opportunity in helping brands decide on what exactly to promote, as well as in understanding the impact on margins and how to grow in ways consistent with their identity. Marklo, she said, incorporates the judgment needed to turn business data into useful recommendations.

The opportunity comes as AI is taking a larger share of investment in the broader sales and marketing sector. Startups in sales, marketing and customer relationship management raised $7.5 billion across 830 rounds globally this year through mid-September, with most of that investment going to companies in 小蓝视频色情网页版鈥檚 AI-related categories. Funding is on pace to finish below 2025鈥檚 $11.1 billion, while deal volume could fall for a fourth straight year 鈥� a sign that investors are concentrating their bets in fewer companies.

鈥淚t鈥檚 one thing to collect the information; it鈥檚 another thing to understand how to present it and how to recommend an action against it,鈥� Green added.

The investor also pointed to Marklo鈥檚 ability to serve businesses at different stages, noting that coordination becomes more complex and information harder to share as brands grow. As such, the prospect of software that can grow alongside its customers was another draw.

Green said she also valued Herrin鈥檚 decision to develop Marklo alongside brands already using it, giving the company customer feedback before its broader launch.

鈥淚 like the fact that she built this product while it was being used,鈥� she said.

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Founded in 2016, Los Angeles-based Fifth Wall has grown into one of the largest investment firms focused on real estate technology, with roughly $3 billion in commitments and capital under management. Its portfolio includes , , and .

Notable exits include , which went public in 2024, and flexible workspace provider , which real estate giant of in January 2025 at a valuation of more than $800 million.

Major real estate and hospitality companies 鈥� including CBRE, , and 鈥� are among its investors, giving the startups it backs a direct connection to potential customers.

Wallace, who serves as CEO and chief investment officer, brings experience on both sides of that relationship. He worked in real estate finance at and before co-founding workforce analytics startup , which was acquired by in 2014, and ridesharing company .

As AI makes certain products easier to replicate, Wallace sees growing potential in startups that use technology to own and operate real estate themselves.

In a recent interview with 小蓝视频色情网页版 News, we discussed that shift, why proptech funding totals may overlook some of the sector鈥檚 biggest investments, what it all means for founders, and the housing problems he believes still need solving.

This interview has been lightly edited for brevity and clarity.

小蓝视频色情网页版 News: Our recent proptech funding analysis showed capital on pace to roughly match or slightly exceed last year, but across fewer deals. What does that tell you about the market?

Brendan Wallace: My sense is that statistic might belie the actual amount of funding flowing in, because what was previously characterized as proptech in the narrowest sense is probably representing a smaller share of where proptech investors are deploying capital.

There are increasingly horizontal solutions that are just as good, if not better, than many of the vertical solutions we鈥檙e seeing. We recently made a very big investment out of our growth fund into , the visualization software company. Most people reflexively think of that as being for movies or rendering scenes. But we saw real estate agents and commercial leasing brokers using it to represent spaces.

For an unbuilt home, you could feed it blueprints, have it build an entire home, put your family inside and walk through it in a full scene. That had surpassed the industry-specific solutions for space visualization that would have been narrowly defined as proptech.

Another category is investment flowing into full-stack businesses: real estate companies themselves. We just invested $135 million into , and I would be surprised if that fell under the definition of proptech.

Those are probably the two categories that might not be captured: horizontal software, with Higgsfield as an example, and businesses that reimagine physical space based on autonomy, AI or robotics. They鈥檙e actually building a physical thing. They鈥檙e building a real estate company, directly or indirectly.

How much is AI driving that shift toward software that serves multiple industries?

Wallace: It鈥檚 a big part of it. Without something profoundly better that you can鈥檛 replicate on your own, there鈥檚 less justification for buying outside software. There still is a justification, to be clear. There鈥檚 just less of one.

No real estate company can build the kind of visualization software Higgsfield has. I don鈥檛 believe they ever will. But a tenant engagement app they might have previously purchased and rolled out to their assets, they can now replicate over a long weekend.

The imperative to buy these third-party solutions has been reduced, and more value has been placed on the best horizontal solutions, whether that鈥檚 a foundation model or a specific use case like sound, audio or visual.

I don鈥檛 know the statistic, but it would be interesting to know how much real estate companies are spending on foundation models versus proptech applications. If I had to guess, we鈥檙e probably close to, or at, an inflection point where it鈥檚 passing it.

No one would characterize as a proptech company, but it might be taking a significant amount of the upstream proptech dollars, and an even larger share downstream once you go through the application layer.

Has AI reset the bar for what makes a venture-scale proptech software company? What does a startup need beyond adding AI to existing workflows?

Wallace: We鈥檙e still figuring that out, but I think it has to be something that can鈥檛 be replicated. Anything where the intellectual property moat is not extremely defensible is very challenged, and I think it will be for some time.

Simple application point solutions are becoming less financeable by venture investors because the capacity of one of the large labs to build that as an interface, or of a company to build it on its own, has caught up so dramatically.

The flip side is that bespoke AI development labs are now building solutions for the real estate industry. We backed one called , which is kind of like a for real estate. Antares works with a number of our strategic limited partners. They go in-house, identify a problem they think can be solved with an AI-based solution, build the intellectual property specifically for the organization, and leave it with them.

It鈥檚 more of a fee-for-development model than an intellectual property licensing model. You鈥檙e seeing that across industries, and you鈥檙e absolutely seeing it in real estate.

In some sectors, founders argue that domain expertise gives specialized AI companies an advantage over broad models. How durable do you think that advantage is?

Wallace: I鈥檓 a little more optimistic about foundation models’ and general intelligence’s capacity to adapt rapidly to specific use cases.

I wouldn鈥檛 argue that specific legal expertise, for example, is not valuable. But I think that moat collapses over time. You might be on a treadmill.

Think about what ChatGPT looked like in 2023 versus what Claude looks like today. At a basic level, it鈥檚 the same thing, but they鈥檙e totally different things. The sophistication, memory, capacity to be personalized, and ability to build off natural language instructions are unlike anything I think anyone could have anticipated in 2023.

What will that look like in 2029? I think what you鈥檙e saying is true today. It鈥檚 less true than it was, and it might become even less true in three years.

Is construction technology still an area of focus for Fifth Wall?

Wallace: It is, but it鈥檚 less of a focus today. When we look at our performance, it has generally favored two things: pure software businesses, whether marketplaces, consumer software or enterprise software, and full-stack businesses.

Anytime you get into physically doing things, the risk profile changes pretty profoundly. I don鈥檛 think it should be surprising that construction tech broadly is probably the biggest graveyard in all of venture. Maybe there鈥檚 a bigger category, but if there is, it鈥檚 not obvious to me.

Why do you think construction tech continues to attract significant funding?

Wallace: Because it鈥檚 still a massive market and a huge problem. If someone can bring greater efficiency to construction, you鈥檙e talking about one of the largest markets on earth. It鈥檚 such a massive prize that the largest funds are going to focus on it.

For founders raising seed or Series A funding, what opportunities would you encourage them to pursue that might not have been fundable a few years ago?

Wallace: You can actually build new real estate companies. That hasn鈥檛 really been conceptualized as being within a venture mandate, but it鈥檚 absolutely something we鈥檇 like to see.

For example, we have a better approach to buying multifamily assets, stripping out costs, and running more of the processes, operations and management with AI. That鈥檚 something I think is eminently fundable today by Fifth Wall and many venture funds. I don鈥檛 think that would have been the case five years ago.

I’d encourage founders to look at building full-stack, capital-R real estate businesses.

The second opportunity is around data. Real estate is characterized by extremely opaque data in a way that鈥檚 different from most large financial markets. Nothing that鈥檚 happened in proptech in the past 10 years has fundamentally changed that.

Where can generative AI provide signal through that opaque data to make capital allocation decisions? Real estate is generally a cash-flowing asset. We don鈥檛 necessarily think of it as comparable to bonds or stocks, but it is, and it should be.

Can you derive signal by looking at more data or different data, or use generative AI to predict what kind of real estate will perform better? The quantitative model that has existed for securities for a very long time now has the potential to affect real estate, because we have computational capacity that wasn鈥檛 previously possible.

Are there opportunities you鈥檙e interested in that have less to do with AI?

Wallace: We鈥檙e intrigued by the intersection of land, real estate and the financial instruments around them. To make most of what we describe as real estate, you usually have to build something on land. Land is an input to almost all real estate, but many real estate owners don鈥檛 want to own it or take on that risk profile.

Is there a way to decouple having to own land from owning and operating a building? That鈥檚 less about AI and more about financial engineering. There are new financial solutions emerging in that category that interest us.

How could AI, robotics and autonomy change the kinds of real estate businesses founders build?

Wallace: I鈥檓 interested in entrepreneurs who can conceptualize real estate through a sociological lens: New catalysts are changing how humans use space compared with the past.

That鈥檚 happened for a long time. E-commerce built the modern industrial real estate industry. Cloud computing, the internet and now AI built the data center industry. Cell towers were born out of mobile phones and smartphone technology. Genome sequencing helped create the lab-space real estate asset class.

I think we鈥檙e on the precipice of three new catalysts that will spawn entirely new real estate asset classes: AI, robotics and autonomy. In many ways, they鈥檙e entangled.

Do you expect fewer dedicated proptech venture firms? What would that mean for founders?

Wallace: I do think you鈥檙e going to see fewer vertical funds, in the narrowest sense of where we started this conversation. Dedicated venture capital funds investing in consumer and enterprise software focused on an industry 鈥斺�� I think you鈥檒l see less of that, and funding will probably continue to go down in that category.

The inverse is venture capital funding real estate businesses themselves. I think that鈥檚 about to explode.

If you were starting a company in this sector today, what problem would you most want to solve?

Wallace: Housing drives everything. Housing costs drive our economy, and the U.S. is grappling with a very real housing affordability crisis. It鈥檚 unclear how to solve it.

There are multiple approaches. One is building homes better, faster and cheaper 鈥� that鈥檚 construction tech. Another is financing homes at a lower cost. How do you bring efficiency to the housing finance market?

That鈥檚 a place where we鈥檝e made a lot of money and invested a lot, but I would argue it鈥檚 still unsolved. Getting a mortgage is still painful, archaic, slow and too expensive.

Related to that is making regulation more efficient to improve housing affordability. That鈥檚 not something an entrepreneur can solve alone, but there鈥檚 potential to bring efficiency to how cities deal with this problem.

There鈥檚 an intersection of regulatory technology, policy work and consulting. How do we make it so more people can afford homes in the cities they live in? That problem isn鈥檛 going away. Despite everything that鈥檚 happened, housing has gotten more expensive, not less.

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and co-led the round, bringing the Minneapolis-based startup鈥檚 total funding to about $15 million since its 2023 inception, according to co-founder and CEO .

The company says its revenue has grown 357% year over year. Its customers include major AI companies and businesses in telecommunications, entertainment and retail, although it declined to identify them, citing the sensitive nature of its work.

Gudea’s premise is that tracking mentions and hashtags tells companies only part of the story. The startup aims to map how information moves across people, groups and platforms, then predict how far a narrative will spread, when it will take off, and when it will fade.

Presley describes Gudea as the 鈥渟torm tracker for the internet.鈥�

鈥淎s a meteorologist uses patterns to predict the weather, we’re using patterns to predict information,鈥� he said in an interview, adding that the startup covers over 600 social media platforms.

From disrupting disinformation to tracking brands

The business grew out of the George Washington Initiative, a nonprofit that adapted tactics used by the intelligence community to disrupt online Al Qaeda and ISIS networks for domestic information campaigns. The team initially searched for existing software that could show how information traveled, but found social listening tools insufficient.

, Gudea鈥檚 CTO and one of its founders, began working on technology to map those pathways. When the team examined the backlash around Bud Light鈥檚 marketing campaign with actor Dylan Mulvaney, it reached a turning point. The campaign鈥檚 critics used the same information pathways that had carried COVID disinformation, noted Presley.

The team realized 鈥渢hat these patterns were repeating, and that there was a much broader use case,鈥� he recalled.

The technology spun out into a company in 2023. Its initial commercial focus has been reputational risk management, which entails helping large businesses spot potentially consequential stories before they spread widely.

鈥淲e don’t need to know what we’re looking for to find it, so we can find all of those good or bad narratives before you know what keywords you should be searching for,鈥� Presley said.

Who鈥檚 driving the conversation?

One way Gudea operates is by assessing whether users or accounts exhibiting unusual behavior are driving online conversations. It also examines how narratives move from less visible online communities into more mainstream platforms.

For example, the startup conducted an analysis of online claims linking to Nazism around an album release. 1 , Gudea鈥檚 director of client experience, told 小蓝视频色情网页版 News that the analysis uncovered that the narrative was being manipulated.

The company says its work has also involved cases of corporate espionage and stock market manipulation. More recently, it has been examining how conversations about data centers affect businesses in AI and energy. Its technology can also identify positive trends companies might capitalize on, according to Paul, in addition to reputational threats.

Predicting what spreads next

Gudea uses deep learning, including graph neural networks, to analyze behavior, and uses a large language model to summarize findings.

鈥淭hose behavior patterns are like fingerprints, and so our AI then recognizes those fingerprints, pulls it out, and then we use an LLM to summarize,鈥� Presley said.

The company sells its software to businesses through offerings that include alerts, platform access and automatically delivered reports. The latter emerged from its work with senior teams that were unlikely to log into a platform themselves.

Presley cited , and as competitors in narrative intelligence, while positioning Gudea鈥檚 predictions as its distinguishing feature.

Gudea tells customers 鈥渉ow long till it goes viral, how many people will be impacted, and how long till it goes away,鈥� he said.

For Gudea, the broader aim remains connected to its nonprofit origins: making the mechanisms behind online information more visible.

鈥淧eople are making real-world decisions off of what they see online, and not all of that information is authentic,鈥� Presley said.

The startup plans to use its new capital to expand its team, develop new features and broaden its coverage across platforms, languages and regions. It currently has 39 customers and approximately 25 employees.

, investor at Mudita Venture Partners, notes that AI is changing how stories are spread on the internet.

鈥淒amage to a brand is now starting in the fringe parts of the internet and builds momentum before it ever reaches a headline. It鈥檚 a big problem,鈥� he wrote via email. 鈥淕udea has built a solution that has the potential to own the market and know more than just the headlines. They can really see what is coming before it’s a problem.鈥�

Most other tools on the market are 鈥渞eactive and surface-layer,鈥� in Foster鈥檚 view, whereas Gudea is more proactive.

鈥淕udea models how information moves across the internet and hundreds of platforms, so it can spot a problem before anyone knows to search for it,鈥� he added. 鈥淪ince it looks at behavior instead of content, its ability to be proactive gets better even as the internet gets more clouded.鈥�

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Clarification: The co-lead investors in the article were updated.

Before was building cybersecurity software, he was performing in his father鈥檚 magic shows.

The son of a magician and an acrobat, Cielen spent his first five years living on a cruise ship, followed by five years in Las Vegas. At 10, he moved to Montreal, where he entered an intensive ballet program, spending mornings dancing and afternoons in school.

鈥淚 was rather introverted as a child, but I became extroverted through the unique upbringing,鈥� Cielen recalls.

By 15, he had skipped two grades, started college and launched a website design and development business. A later trip to a hackathon in Waterloo introduced him to venture capital investors. That same week, he said, he dropped out of school, quit an algorithmic trading internship and flew to San Francisco to start.

Now, the startup is emerging from stealth announcing it has raised $4.2 million in seed funding to help companies detect and stop unusual data activity, including the theft of sensitive information, it tells 小蓝视频色情网页版 News exclusively.

led the round, joined by,, Brickyard,,, the and. The financing brings Hilt鈥檚 total funding to $4.7 million, including a $500,000 pre-seed round led by in October 2025.

Finding the problem

When Cielen, Hilt鈥檚 co-founder and CEO, arrived in San Francisco, he had settled on cybersecurity but was still figuring out which problem to tackle. He had previously attended in Montreal and worked as an algorithmic trading intern at the.

鈥淚 had to do a lot of customer discovery,鈥� he said, describing his efforts to understand the market and talk to potential customers, 鈥渢o see what the real problems were, not to build in the dark.鈥�

A pivotal conversation came in January, when the chief technology officer of a large hedge fund described a security problem in its high-frequency trading operations. Those systems trade in microseconds, Cielen said, leaving little room for security software that introduces delays.

The hedge fund offered a potential contract worth $1 million to $2 million if Hilt could solve the problem, Cielen said. It then paid a six-figure sum upfront before Hilt had even built the solution.

Hilt鈥檚 software monitors how data moves across cloud infrastructure, computers and networks, building behavioral baselines for users and resources. It flags unusual activity, even when a user has permission to do it, and claims it can do so without slowing down the company鈥檚 systems.

For example, a late-night login might be normal for an employee who often works late. But if someone who only works during the day suddenly logs in at midnight 鈥� and has just changed their password 鈥� Hilt could flag the combination as suspicious.

Hilt can alert a company鈥檚 security team to investigate or, in some cases, quarantine the activity as it happens.

鈥淭hey don鈥檛 have to wake up and see that it happens, but we can actually stop it as it happens,鈥� Cielen said.

The startup鈥檚 approach was to work at the kernel, the core of an operating system, and stream activity data to graph neural networks, a type of AI model, that identify anomalies. Cielen said that gives Hilt visibility beneath the different software applications its customers use, allowing it to deploy across a range of environments.

He also said one customer鈥檚 measurements showed that deploying Hilt sped up its cloud systems.

The company continues to work with the hedge fund and has added another high-frequency trading firm, Cielen said. He said its customer count doubled in the month after it began making a more concerted push to market, though he did not disclose the total.

Hiring and expanding beyond finance

Beyond trading firms, Hilt is seeing interest from neobanks, healthcare businesses, professional services firms and other businesses that deal with sensitive information. Customers run the product within their own infrastructure, retaining control of their data, and pay an annual fee per data collector.

The startup has 11 employees, including co-founder and CTO and founding engineer. Originally based in San Francisco, the team moved to Chattanooga, Tennessee, with investor as it works toward product-market fit.

Cielen said the new capital will primarily support hiring and building a market presence.

鈥淭he money is very useful for hiring the best people,鈥� he said.

Investor POV

, general partner of Array Ventures, said most tools in this space do one of two things 鈥� either predict what鈥檚 sensitive upfront 鈥渁nd get it wrong a lot, or they tell you what happened after the data is gone.鈥�

鈥淗ilt can find and stop these breaches in real time, with accuracy,鈥� she wrote via email. 鈥淚t also runs entirely inside the customer’s own environment, so nothing leaves the company. For a bank or government entity, that is the difference between proactive vs. reactive response.鈥�

She also pointed out that today’s commonplace breaches don鈥檛 always look like attacks.

鈥淭hey look like normal activity such as an employee using valid access, a connected vendor, or an AI agent doing what it鈥檚 allowed to do,鈥� she said. 鈥淗ilt connects these actions and detects when normal behavior becomes suspicious. As AI agents increasingly interact and act autonomously, we think this problem will only grow, and we wanted to get ahead of it.鈥�

, founder and managing director of Verdict Capital, said he was drawn to invest in Hilt mainly because of its founder.

鈥淎t Verdict Capital, we back founders who are obsessed, have high velocity and are learning animals,鈥� he wrote via email. 鈥淲ill exemplifies these traits.鈥�

Bonatsos, a former investor at who founded Verdict in early 2026 and also backed Mercor, said he was also impressed with Hilt鈥檚 approach, noting that Hilt was one of the first checks the Verdict team wrote.

鈥淢ost security tools look at content: files, signatures, known threats. Hilt looks at how data moves, at the kernel, which is the one place you can’t hide from,鈥� Bonatsos said. 鈥淓veryone assumed you couldn’t sit that deep without slowing systems down. They did it in live financial environments鈥� Selling security into banks is about as hard as enterprise sales gets, and I watched them do it.鈥�

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The buying spree has pushed acquisitions of AI startups by other venture-backed AI companies to 195 through Sept. 29, according to 小蓝视频色情网页版 data 鈥� 14% more than in all of 2025. Yet the number of buyers grew just 2%, indicating that increasingly active acquirers are driving much of the increase.

Those deals point to a new phase of competition in the AI sector: Well-funded startups are using M&A to broaden their products and reach new customers faster than they could by building everything themselves. In legal tech, for example, acquisitions are bringing research, regulatory monitoring and litigation tools into broader platforms.

CFO described that calculus in a earlier this year explaining the company鈥檚 back-to-back purchases of multiple startups: 鈥淢&A is explicitly part of how we accelerate what we’re building. The question we always ask is: does this deal get us somewhere faster than we’d get there ourselves?鈥�

Repeat buyers step up dealmaking

Some of the AI acquirers have returned to the dealmaking table more than once in recent years, 小蓝视频色情网页版 data shows. Across the three-year period, 67 repeat buyers accounted for about 42% of all transactions tracked. OpenAI was by far the most active, with 20 AI-related acquisitions, including 10 this year.

This year鈥檚 other repeat buyers include and legal AI startup Legora, which have each announced five acquisitions, followed by legal AI startup with four. Customer-service AI provider and coding company have each made three acquisitions this year, while announced two. Many of the buyers are vertical AI startups acquiring companies in their respective areas.

Several of this year鈥檚 transactions are sizable.鈥檚 reported $1.65 billion acquisition of was the largest with a recorded price, followed by鈥檚 $1 billion acquisition of identity security startup.

Anthropic鈥檚 acquisition of, which develops AI for pharmaceutical research, was valued at $400 million. 翱辫别苍础滨鈥檚 $300 million acquisition of, a Los Altos, California, startup that uses customized AI in camera hardware for computational photography, and鈥檚 acquisition of, valued at up to $285 million, rounded out the five largest deals recorded in the dataset through Sept. 29.

Overall, prices were disclosed for only 12 of the 195 deals, making it difficult to gauge how much AI startups are spending on acquisitions overall.

Dealmaking has clearly picked up as more AI companies turn to acquisitions 鈥� and, in some cases, make them a recurring part of their growth strategy. 1

鈥淚t鈥檚 all about speed in the AI world,” according to , partner at , which has backed numerous AI startups including Anthropic and Legora. “It鈥檚 faster to acquire a team or product than build it yourself. If you鈥檙e not growing 10x, you鈥檙e not interesting to growth investors, which leaves a gap in the funding market for AI startups that need a home. High valuations have also given AI startups cheap currency to use their stock to get these deals done with minimal dilution.”

What AI startups are buying 鈥� and why

The specific companies being acquired offer insight into what AI startups hope to gain from their stepped-up M&A activity.

翱辫别苍础滨鈥檚 purchases have ranged widely, from healthcare data and scientific-writing software to developer infrastructure, security tools and specialized talent. The San Francisco-based company announced three acquisitions in January alone, setting the tone for a busy M&A year.

In January, it acquired:

• , which built AI software to help executive coaches automate leadership assessment reports.
• , an AI-powered health app that aims to unify scattered medical records from hospitals, labs, wearables and consumers.
• , which provides LaTeX editing, error detection and team collaboration.

In February, OpenAI participated in an deal involving open-source AI agent and its creator, . Then in March, it announced plans to , a creator of open-source tools for software developers. It also snapped up , an open-source tool for testing AI applications.

In June, OpenAI agreed to buy , formerly known as Gitpod, which provides secure cloud environments where developers 鈥� and, increasingly, AI agents 鈥� can continue working after a user鈥檚 laptop is closed. In August, it also picked up , an AI presentation company that converts prompts, notes and documents into editable presentations.

Vertical AI roll-ups

Well-funded vertical AI startups, particularly in legal tech, have also been busy this year buying smaller companies, an analysis of 小蓝视频色情网页版 data shows.

San Francisco-based Harvey鈥檚 AI-related purchases have focused on filling gaps around its core platform. developed tools for creating product demos, videos and guides, while built software that helps companies connect customer data and applications with AI systems.

New York-based developed software that helps asset managers capture insights from previous investments and apply them to new deals. That acquisition helped expand Harvey鈥檚 presence in the asset management space. Its fourth known acquisition this year, announced Sept. 9, was , which builds open-source tools for testing, monitoring and managing AI agents.

, COO of Harvey, told 小蓝视频色情网页版 News that the company鈥檚 M&A strategy is rooted in finding 鈥渢echnical talent with high ownership and deep experience in legal tech or an adjacent space to legal.鈥�

She pointed to Benchmark as an example. The co-founders, she said, 鈥渒now the asset management space cold, and their name was dropped so many times in customer conversations that it was a natural fit for them to join our team.鈥�

Burke further described the startup鈥檚 M&A strategy as 鈥渟elective but aggressive.鈥�

鈥淲e hold an incredibly high bar for talent and when we identify an additive company, we move quickly and will continue to do so this year and beyond,鈥� she added.

Legora has pursued an even broader legal-tech rollup. The Stockholm-based company鈥檚 five announced acquisitions this year include:

• , a nine-person Canadian startup whose agents work inside Word and Outlook;
• , a Stockholm company building AI-native legal research tools;
• , an Australian regulatory-intelligence platform that monitors more than 100 areas of law for changes;
• , whose AI agents analyze documents and data for commercial real estate companies; and
• London-based , which helps litigation teams extract facts from large collections of documents, identify inconsistencies and build case timelines.

Sierra鈥檚 acquisitions reflect an effort to expand both geographically and beyond conventional customer-service automation. In March, it acquired Tokyo-based enterprise AI startup as part of its expansion in Japan, followed by Paris-based , which helps businesses automate operational work with AI. In July, Sierra bought , a 14-month-old startup developing 鈥渓ong-horizon鈥� agents.

For acquisition targets, joining a larger platform can offer a shortcut to scale even when their own businesses are growing rapidly. TakeOff founder wrote in a July that his company was posting a 鈥渘ear 8-figure run rate鈥� with a team of just three people. 鈥淭he advice for an AI startup growing at our pace is to hire out a sales team, raise again, and keep going,鈥� he wrote. 鈥淲e had capital, customers, and great traction,鈥� but the Sierra acquisition offered an opportunity to 鈥渁ccelerate our shared vision and simultaneously build it at a grander scale.鈥�

The surge in M&A activity is driven by both a wider pool of buyers and increasingly active serial acquirers. At this pace, we should expect even more in the months and years ahead.

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Related reading:

• Data: OpenAI Has Already Done Nearly As Many M&A Deals In 2026 As It Did All of Last Year
• Startups Are Still Acquiring Startups, Led By Ultra-High-Valuation Unicorns
• Your AI Strategy May Be Destroying Your Exit Value

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