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’s 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 ������Ƶ data. Notably, that’s about 40.7% more than the $1.08 billion Brazil’s 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.

“Over the past year, we have seen investments into Brazil from firms including , , , and ,” , managing partner of , told ������Ƶ News. “For several, this marked either their first investment in Latin America or their first in a long time.”

, Endeavor Catalyst’s 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 �ѱ�澱����’s funding went to a few startups, he pointed out, while Brazil recorded many deals.

“Investors are concentrating on a few Mexican winners, while Brazil retains the breadth,” 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’t to fintech, which typically has the most funding recipients in the region, but rather to energy-related startups.

, the Chilean project company for ’s Black Giant lithium project, raised $225 million in a July round at a reported $900 million valuation. And Colombia’s , 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.

�ѱ�澱����’s , 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’s , an AI-native wealth advisory company, landed $85 million in  what ������Ƶ 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’s startups in the third quarter, about 20.5% — or $266 million — was raised across the angel and seed stages.

That’s 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’s 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.

“I joke that investors from the U.S. ‘rediscover’ Latin America every five or six years, and I think we are approaching another rediscovery moment,” Taylor told ������Ƶ 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.

“The exit door is open, although it is not primarily opening through IPOs yet,” Piquet said.

Taylor expects more exits could help bring global investors back consistently, with potential listings by companies such as , or contributing to another “rediscovery moment” 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 2020–2021 investment boom have shifted toward AI opportunities elsewhere and are concentrating on existing portfolios, she noted.

“Global investors are increasingly investing behind themes rather than geographies,” 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.

“Increasingly, we’re seeing founders build globally competitive technology companies from Latin America, rather than necessarily for Latin America,” she said.

Gadala-Maria agrees that Brazil and Mexico remain the region’s 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.

Related ������Ƶ queries:

Methodology

The data contained in this report comes directly from ������Ƶ, 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.

������Ƶ 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 ������Ƶ 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. ������Ƶ 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. ������Ƶ 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 “Series [Letter]” 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 “venture” round. (So basically, any round from the previously defined stages.)

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Congratulations to the enterprise AI company that just raised another nine-figure round to solve a problem software is supposed to solve by itself: getting humans to use the product.

You know the one. Won’t name it. Don’t have to. You’ve read the press release — “autonomous,” “agentic,” “category-defining” in paragraph one, and then, a few paragraphs down, where the money is actually going: hiring an army of engineers to sit inside customer offices and manually make the “autonomous” thing behave.

Nothing says “we’ve built the future of work” quite like raising half a billion dollars to hire more people to do the work.

The business model, decoded

Here’s how it works, in case the deck was too artfully vague:

1. Sell the customer an “AI platform.”
2. Discover the platform doesn’t actually do the thing on its own.
3. Send in the forward deployed engineers: sharp young people with CS degrees who now spend six months hand-building the workflow the website implied already existed.
4. Bill the customer for “software.”
5. Book it as ARR.
6. Raise a bigger round because “ARR go up.”
7. Repeat, forever, with an increasingly large and increasingly expensive engineering org standing between the product and the promise it made.

This is not a flywheel. This is a payroll with a pitch deck.

And the industry has stopped even pretending otherwise — it’s fundraising on it, loudly.

pledged valued at $10 billion, with another $4 billion from ,, , and . The vehicle, DeployCo, has been with more than $4 billion in investment.

Meanwhile, job postings for “forward deployed engineer” are up somewhere between 800% and 1,000% year over year. Lightcast data reported by from January to August 2026. data carried by put listings up more than 800% from January to September 2025, and data from 643 in April 2025 to 5,330 in April 2026. Total comp is pushing past $300,000 in the U.S. list at $280,000 to $320,000 base pay and OpenAI at $185,000 to $300,000 base, plus equity. Fortune reported some Anthropic roles reaching $400,000 in total compensation.

Somewhere, a VC is calling this “operating leverage” with a completely straight face.

It is not operating leverage. It’s a staffing agency that learned to say “agentic.”

The tell

Ask the one question the pitch deck conveniently never answers: “If you pulled the embedded engineer out tomorrow, does the customer still get what they paid for?”

If the honest answer is “well, no, but the ARR is real” — that’s not product-market fit. That’s people-market fit, and people-market fit has a brutally unglamorous ceiling: You can only hire humans so fast, and you can never hire them cheap enough to earn a software multiple. Every new logo doesn’t get more profitable to serve. It gets another headcount request.

Two companies, same $20 million ARR. One needs 10 engineers to keep the lights on. The other needs 100. Both slides say, “AI software company.” The difference shows up in gross margin, cost to onboard the next customer, and whether that cost falls as the company scales.

The bill always comes due

Here’s the thing about people-market fit: it feels great right up until someone asks the unfashionable questions: What does it actually cost, fully loaded, to onboard the next customer? Does that number go down as the company scales, or does it just grow the org chart? What happens to gross margin if the first year growth slows and the headcount can’t hide behind hypergrowth anymore?

Companies funding their FDE armies with fresh nine-figure rounds are betting nobody asks these questions until after the round closes and the confetti is swept up. That’s a fine bet in a hype cycle. It’s a terrible one in a market that’s about to start reading the fine print.

Because eventually, someone always reads the fine print. A CFO builds a cost-per-deployment model. A CIO benchmarks time-to-value against three vendors instead of trusting one demo. An investor finally does the unglamorous work of separating software ARR from human-powered ARR on the same balance sheet.

The distinction buyers and investors can test is on the numbers. Software ARR should get cheaper to deliver as it scales. Human-powered ARR does not. OpenAI guaranteed the private-equity backers of its deployment vehicle an annual 17.5% return over five years, which is a different structure from a growth thesis.¹

So no, your AI isn’t autonomous. But your business model might be — autonomously transferring the cost of a services company onto a software valuation, for as long as the music plays.

Enjoy the round. Hope the org chart was worth it.

, the co-founder and CEO of , is an entrepreneur and product leader with more than 20 years of experience across cybersecurity and AI. He has built three generative AI companies, including one that reached unicorn status. Gurtu is known for turning advanced AI into scalable, real-world platforms, and previously contributed to successful exits, including acquisitions by , and , and a public company, . He holds a master’s degree from the and CISSP certification.

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This is a weekly feature that runs down the week’s Top 10 announced funding rounds in the U.S. Check out last week’s biggest funding deal roundup here.

Startup investors aren’t exactly hitting pause, but they did scale back on giant rounds this past week, with just two startup financings in the multiple hundreds of millions. The biggest round went to AI infrastructure provider , which picked up $750 million, followed by construction automation unicorn , which secured $200 million. Other sectors that attracted larger rounds this past week included biotech, insurance and AI.

1. , $750M, AI infrastructure: Modal Labs, a provider of cloud infrastructure for AI inference and other applications, secured $750 million in a Series D funding round led by at a $15.75 billion valuation. The New York-headquartered company said it recently exceeded an annual revenue run rate of $1 billion.

2. , $200M, construction tech: Austin-based Icon, a developer of robotic construction systems known for its 3D-printed homes, closed on $200 million in Series D funding led by  and . The company says it is on pace for 4x revenue growth in 2026.

3. , $135M, RNA therapies: Boston-based Soufflé Therapeutics, a developer of RNA therapies, raised $135 million in Series B funding led by as part of a large syndicate of investors.

4. , $103M, gene therapies: Menlo Park, California-based Estuary Biotherapeutics, a developer of genetic medicines for patients with serious kidney diseases, launched with $103 million in financing. The funding includes $93 million in a Series A round led by as well as $10 million in previously undisclosed seed capital.

5. , $100M, AI tools: Labelbox, a developer of technology to train and evaluate AI models and to help deploy agents that improve with experience, picked up $100 million in Series E funding. led the financing for the 8-year-old, San Francisco-based company.

6. , $90M, health AI: Redwood City, California-based Suki, a provider of AI tools that turn clinical conversations into intelligence for documentation, coding and other healthcare workflows, announced a $90 million Series E financing from backers including and .

7. , $82M, caregiver platform: Abby Care, a platform for family members to become certified, paid caregivers and deliver care at home, closed on $82 million in Series C funding, per a . led the financing for the 5-year-old, San Francisco-based startup.

8. , $70M, insurance: Stand, a provider of insurance for homes in areas at higher risk of natural disasters, secured $70 million in Series C funding at a $1 billion valuation. led the financing for the 3-year-old, San Francisco-based company.

9. , $65M, semiconductor systems: Mueon Corp., a semiconductor systems company building a modular platform for AI and hyperscale computing, completed a $65 million Series A funding round. led the financing for the Portland, Oregon-based company.

10. , $60M, AI spend management: New York-based North.cloud, provider of an infrastructure spend intelligence and automation platform, raised $60 million in a Series B round led by .

Methodology

We tracked the largest announced rounds in the ������Ƶ database that were raised by U.S.-based companies for the period of Oct. 10-16, 2026. Although most announced rounds are in the database, there may be a small time lag, as some rounds are reported late in the week.

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Overall, investors put $38.5 billion into reported seed- through growth-stage financings for countries in the region in Q3, per ������Ƶ data. Of that, a majority went to AI-focused companies, echoing the trends we’ve seen globally.

Deal counts, meanwhile, were relatively flat. That indicates concentration of capital into favored startups remains pronounced but is not intensifying.

Table of contents

• An AI-driven quarter
• China leads by far in country-by-country breakdown
• Late stage
• Early stage
• Seed
• A big quarter for China
• Methodology
• Glossary of funding terms

An AI-driven quarter

Around 55% of investment — an estimated $21 billion — went to AI-focused companies. Total funding is down substantially from the prior quarter, which was boosted by a $7.4 billion financing for China’s , but still the second-highest tally on record.

For Q3, a couple of extra-large rounds bolstered the AI investment tally. The largest went to Beijing-based foundational model company , which secured $3.5 billion in July at a reported $35 billion valuation.

, the AI spinout of China-based short video platform , was the second-biggest fundraiser. The generative video startup raised around $3 billion in a July financing that included and .

China leads by far in country-by-country breakdown

China was the leading Asian destination for venture investment by a very wide margin.

Per ������Ƶ data, roughly $27 billion — or 70% of all Asian startup funding in Q3 — went to China-based companies. India was a distant second, with $2.9 billion, followed by Singapore, with $2.5 billion.

For perspective, below we charted third-quarter startup funding tallies for the six countries with the largest totals.

Late stage

Most capital went to late- and growth-stage funding rounds.

Per ������Ƶ data, investment at this stage totaled around $20 billion in Q3. That’s down a bit from the prior quarter but nearly double year-ago levels and the second-strongest showing in four years.

Multiple jumbo rounds kept the total high. The largest was Moonshot AI’s aforementioned $3.5 billion Series F.

Several others raised financings in the multiple hundreds of millions. Shenzhen-based , a provider of robotics hardware and software, for instance, picked up $400 million in a September Series C. And Hong Kong-based , an e-commerce fintech platform, secured $300 million in Series C funding as it eyes an IPO.

Early stage

Early-stage investment drifted lower in Q3 but remained at historically high levels.

Per ������Ƶ data, financing for startups at the Series A and Series B stages totaled $13.6 billion for the just-ended quarter. That’s a decline of 28% from the prior quarter but up 65% from year-ago levels.

Seed

Reported seed funding hit its highest level in years, boosted by investor enthusiasm around AI.

In total, investors poured $4.9 billion into reported seed and angel deals across Asia, per ������Ƶ data. That’s up around 22% from the prior quarter and more than double year-ago levels.

A big quarter for China

Overall, Q3 was a robust period for venture funding to China-based companies, with far smaller sums going to other Asian startup hubs. Funding to startups based in India and Singapore was down significantly quarter over quarter, while Israel’s investment totals declined slightly.

We’ll keep an eye out in the coming months to see if this divergence of fortunes persists.

Related ������Ƶ query:

Related reading:

• ������Ƶ Data: Q3 2026 Posted A Record Count Of Billion-Dollar Rounds As The Global AI Race Heats Up

Methodology

The data contained in this report comes directly from ������Ƶ, 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. ������Ƶ 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 ������Ƶ 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. ������Ƶ 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. ������Ƶ 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 “Series [Letter]” 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 “venture” round. (So basically, any round from the previously defined stages.)

Illustration:

This tracker includes layoffs conducted by U.S.-based companies or those with a strong U.S. presence and is updated at least bi-weekly. We’ve included both startups and publicly traded, tech-heavy companies. We’ve also included companies based elsewhere that have a sizable team in the United States, such as , even when it’s unclear how much of the U.S. workforce has been affected by layoffs.

Layoff and workforce figures are best estimates based on reporting. We source the layoffs from media reports, our own reporting, social media posts and , a crowdsourced database of tech layoffs.

We recently updated our layoffs tracker to reflect the most recent round of layoffs each company has conducted. This allows us to quickly and more accurately track layoff trends, which is why you might notice some changes in our most recent numbers.

If an employee headcount cannot be confirmed to our standards, we note it as “unclear.”

How would you handle a junior employee questioning the way you’ve always done something? Might you roll your eyes and dismiss their comments as naivety? Or would you indulge their curiosity?

As AI has made knowledge accessible on a scale never seen before, companies are becoming leaner and operating faster. What once took a large team months to do can now be done by a handful of people in a few days.

It is undeniable that autonomous systems are changing the way we work. In the small and dynamic teams of the future, choosing the right humans to direct your AI systems will become make or break for a company.

As a six-time founder, I’ve learned the best workplaces are the ones where questioning things is the norm. Here’s how to build a team that questions orders as much as it obeys them.

AI by design

“AI by design” doesn’t only mean building the right autonomous systems. It’s also about creating autonomous humans. You need to trust the human in the loop as much as you do the technology. The team around you ultimately shapes the company in ways you can’t control. So surrounding yourself with people you trust to make decisions when you’re not in the room is critical to success.

The nature of job roles is changing. A job description can be totally different from the one advertised by the time you’ve found a candidate, and a six-month roadmap is outdated by the time you’ve finished drawing it up.

The has stated that as AI reshapes the workplace. Hiring for a fixed set of skills won’t move the needle. The people worth investing in are those who are able to learn what comes next.

Hire for curiosity

Curiosity is the best trait you can hire for. When I look at a CV, it’s not just experience or academic credentials that draw me in. I look for what people have built on the side. What have they made on their own initiative?

Hire for curiosity, and you’ll create an environment where people are willing to throw new ideas around and challenge outdated ways of doing things. If your team is worried about saying the wrong thing, they won’t end up saying the right thing either.

In a world where technology is turning everything on its head, the companies that will thrive are the ones with curious people who are able to adapt to change quickly.

Hand over real responsibility

Employees with autonomy operate faster. They make better decisions because company vision is not abstract to people who are given real responsibility to drive it. Being involved in strategic direction stitches an employee into the fabric of the company and keeps them engaged.

Trust your employees to know what isn’t working. Allow people to pursue new ideas and reinvent things. Your employees are likely to spot potential problems before you do when they’re closer to the product. Make sure your team has the autonomy to try to fix them before they take hold.

Boredom is a big business risk

As companies become smaller, employee retention is more important than ever. Losing your best people is costlier when you have fewer of them to begin with.

The effects of a bored employee surface quickly in a small team. Boredom makes talented people a little bit slower, and a lot less likely to try the difficult thing.

The challenge for modern leaders is keeping your team engaged and interested. A bored employee is more likely to be swayed by a flattering recruiter.

Make sure to invest in development, keep your team learning, and always encourage curiosity. Give people autonomy and responsibility from the beginning, and boredom has nowhere to take hold. The mentality of risk and the drive to challenge something outdated is what you need to succeed in the changing work environment.

Dare to be a beginner, and teach your team to do the same. That junior employee questioning how you’ve always done things could be the most valuable person in the room.

is co-founder and CEO of Stockholm-based , an agent-driven, game-based learning platform. He has experience building, exiting and scaling digital companies across 70 countries. Uniplay is his sixth company to date.

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For the third quarter of 2026, global cybersecurity- and privacy-focused startups pulled in a total of $6.8 billion, per ������Ƶ data. That’s up about 50% from the prior quarter and tied for the highest quarterly tally in years, as charted below.

The funding bump was due to larger rounds, not more of them. Round counts actually declined sequentially across all stages last quarter, ������Ƶ data shows.

Biggest cybersecurity fundraisers

AI startups accounted for the overwhelming majority of cybersecurity funding, with a particular focus on identity security. Overall, at least at the intersection of artificial intelligence and cybersecurity closed rounds of $100 million or more this past quarter.

Of those, the largest funding recipient by far was enterprise identity security provider  , which picked up $1 billion in a July growth round led by . Founded in 2001, the Ohio company isn’t exactly a startup, but it has attracted the bulk of its venture funding in the past 10 years and its pivot to AI is also of a more recent vintage.

Dallas-based tied for second place with a $400 million September Series F led by . The startup, which offers tools for enterprises to see and govern how people and AI agents interact with applications and data, also secured a post-money valuation of $6.4 billion.

, operator of an enterprise data security platform focused on AI agents, also announced a fresh $400 million -led investment in September, just three months after raising $600 million. The latest infusion set a $12 billion valuation for the New York startup.

For a bigger-picture view of leading fundraisers, below we used ������Ƶ data to put together a list of the 10 largest round recipients.

Exits, too

We also saw some good-sized acquisitions of cybersecurity startups this past quarter, even as the IPO market was fairly sluggish.

led the M&A pack with its $2.4 billion purchase of anti-fraud technology provider in August. The acquisition is part of a broader effort by Visa to beef up its fraud detection and prevention capabilities for the AI age.

Among private unicorns, meanwhile, Cyera proved it can find a way to spend a good chunk of the capital it raised. It acquired , an enterprise access management platform built for AI agents, in a deal reportedly valued around $1 billion.

As for public offerings, , which offers digital security services to consumers, was the lone sizable venture-backed market debut. The Boston-based startup completed an acquisition of an Australian public company, , in July, and then renamed the combined company Aura.

Overall, an upbeat quarter

Overall, ������Ƶ funding data points to an upbeat environment for cybersecurity startup funding. This doesn’t seem particularly surprising given the steady drumbeat of worrisome news about the risks posed by rapidly advancing AI technology.

It’s not an especially reassuring time for most of us. But for cybersecurity founders, there’s certainly an ever-expanding array of hazards to keep at bay. We’ll keep watching in the current quarter to see if investment momentum continues to build.

Related ������Ƶ queries:

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He also discovered mass arbitration, a way to pursue individual claims when companies’ 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’s 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 ������Ƶ . 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’s 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 ������Ƶ 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’s 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 “frustrated and mystified and horrified by how our justice system works.”

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.

“It’s just really inefficient for them to try to build workflows around 10,000 claimants or 100,000 claimants,” 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’s not a law firm, Chariot itself doesn’t 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.  Hang estimates that such improvements could help the company handle 10x to 100x its current claim volume.

Companies push back

Not everyone welcomes Chariot’s 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.

“As a part of our business, we need to mention the name of the company that has this alleged violation,” 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.

“We want to make unethical unprofitable,” he said.

, general partner at Silverton, told ������Ƶ News via email that his firm was attracted to Chariot’s vertical AI approach to working with law firms, which he called “exciting and defensible.”

“It’s a greenfield market with space to expand horizontally and eventually into a generalized consumer legal AI solution,” he wrote. The firm was also drawn to what Dodd described as “a business model that aligns capitalistic incentives with access to justice.”

“When a company breaks the law and breaks what it promises to its consumers, the consumer rarely seeks justice,” he added. “Chariot 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.”

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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 ������Ƶ 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 — 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’ 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.

“Hey, no, I can’t. It’s not Fourth of July. We don’t have the inventory anymore,” 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’t 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’s 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.

“I’m very passionate about small and medium businesses, and I want to make it accessible to every single brand that runs on Shopify,” Herrin told ������Ƶ News in an interview, “so that they can operate like a big brand.”

A change of direction

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

“For me, it was never about the jewelry,” she told ������Ƶ 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 “the COVID victim that had pre-existing conditions.”

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.

“As an entrepreneur, you don’t just start a business; you start a life,” 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.

“The mission was the same, but the solution needed to evolve,” 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.

“Here’s a new growth strategy,” Herrin recalled thinking. “How about profits?”

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.

“Raising capital isn’t just about having money for expenses,” Herrin said. “It’s about bringing a brain trust together.”

Experience as ‘the customer she’s building for’

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

“Jessica uniquely has been the customer that she’s building for right now at every stage, from starting a brand to having a brand at scale,” Green told ������Ƶ 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 ������Ƶ’s AI-related categories. Funding is on pace to finish below 2025’s $11.1 billion, while deal volume could fall for a fourth straight year — a sign that investors are concentrating their bets in fewer companies.

“It’s one thing to collect the information; it’s another thing to understand how to present it and how to recommend an action against it,” Green added.

The investor also pointed to Marklo’s 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’s decision to develop Marklo alongside brands already using it, giving the company customer feedback before its broader launch.

“I like the fact that she built this product while it was being used,” she said.

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A first venture usually follows a well-worn playbook: a gap in the market, obsessive refinement of concept and pitch, eventual conviction that the solution is as bulletproof as it is revolutionary.

Then, the dreaded phase of product-market fit. Instead of building the future of an entire industry, you find yourself forced to focus on a narrow use case and spend the first invaluable years proving that your idea can make money and scale. The grand vision gets put on pause.

For most founders, this journey is the only one available. After all, investors are betting on someone who has never built a company before, so a small check and a narrow product limit what everyone stands to lose. But some ambitions need total commitment from the start, and having built a company before helps when taking them on.

Lessons from the front line

After selling my first venture, , to for €1.8 billion (about $1.9 billion USD) in 2022, I found myself in a new position — and it wasn’t horizontal on a sun lounger. My co-founder and I had a real war chest of our own, and a brand-new entrepreneurial bug. For the first time in our lives, we had the experience and the resources to undertake something truly world-changing.

Compliance is a gauntlet that any founder will be painfully familiar with. Scaling Tink across Europe, to 700 people operating under a host of regulators, my team and I got used to tearing our hair out over small wording tweaks, reviews that dragged on for months, and perpetual uncertainty that stopped us from iterating fast and boldly. Products were often delayed by months, a slip that meant the world in an industry moving as fast as ours.

A problem with too many heads

Compliance was a problem that could never be solved piecemeal. Every company’s obligations depend on a million different factors, each of which sits in a different part of the business that rarely talks to one another.

Legal tracks regulations; another department oversees how they are implemented. Product teams make their own calls on features, and yet another team opens a case to check whether it was compliant. Policies, standards and regulations belong to other departments entirely, and those are usually the part of a silo a compliance startup builds for — a clear buyer and a budget.

“Solving compliance” was a mission akin to fighting the Hydra: cut off one head and two more grow back. There is no minimum viable product here. Fixing one head changes nothing. What it takes is a maximum viable product from day one.

Why the second time is different

In recent years, we’ve seen a series of inspiring examples of successful founders coming back to take on new problems. Stockholm knows this well: alumni of and have gone on to found and back a long list of new companies, using the credibility from their first successes to try something harder.

The second time around, capital and access both come easier. A successful first exit opens doors to talent, partners and investors that we simply didn’t have with Tink. It is a fantastic opportunity, and one I’m grateful for. You get to bring every lesson from what went well and what went badly, and you start with a real tailwind when people want to work with you and back you.

Money also means you can fund the full build yourself, so no investor asks you to shrink the idea before you’ve tested it.

Maximum viable product

Experience and capital used to get you only so far. Ten years ago, building something this broad would still have needed hundreds of people of various backgrounds, and many years. Language models have changed that. Our team today is 22 strong and growing, and ships at a pace I could only have dreamed of when we first built Tink.

We also feel we’ve grown thicker skin from the bruises of our first 15 years as entrepreneurs.

You know the setbacks are coming, and you know that a venture is never a one-man sprint but a team marathon. None of that guarantees success. But it’s a good place to start if you want to stand your ground and build your biggest vision yet.

is the CEO and co-founder of , the Stockholm-based AI-native compliance company that came out of stealth in September 2026. He previously co-founded the open banking platform in 2012 and led it as CEO through its growth to a multimarket operation with around 700 employees before its €1.8 billion acquisition by in 2022.

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