Tech
How Kapa uses LLMs to help companies answer users’ technical questions reliably
Generative AI and large language models (LLMs) have been all the rage in recent years, upending traditional online search via the likes of ChatGPT while improving customer support, content generation, translation and more. Now, one fledgling startup is using LLMs to build AI assistants capable specifically of answering complex questions for developers, software end-users, and employees — it’s like ChatGPT, but for technical products.
Founded in February last year, Kapa.ai is a graduate of Y Combinator’s (YC) Summer 2023 program, and it has already amassed a fairly impressive roster of customers, including ChatGPT-maker OpenAI, Docker, Reddit, Monday.com and Mapbox. Not bad for an 18-month-old business.
“Our initial concept came after several friends who ran tech companies reached out with the same problem, and after we built the first prototype of Kapa.ai to address this for them, we landed our first paid pilot within a week,” CEO and co-founder Emil Sorensen told TechCrunch. “This led to organic growth through word-of-mouth — our customers became our biggest advocates.”
To build on that early traction, Kapa.ai has now raised $3.2 million in a seed round of funding led by Initialized Capital.
Getting technical
In the broadest terms, companies feed their technical documentation into Kapa.ai, which then serves up an interface using which developers and end-users can ask questions. Docker, for example, recently launched a new documentation assistant called Docker Docs AI, which provides instant responses to Docker-related questions from within its documentation pages — this is built using Kapa.ai.
But Kapa.ai can be used for myriad use-cases such as customer support, community engagement, and as a workplace assistant to help employees query their company’s knowledge base.
Under the hood, Kapa.ai is based on several LLMs from different providers and leans on a machine learning framework called Retrieval Augmented Generation (RAG), which enhances the performance of LLMs by enabling them to easily draw from relevant external data sources to provide richer responses.
“We’re model-agnostic — we work with multiple providers, including using our own models, in order to use the best-performing stack and retrieval techniques for each specific use case,” Sorensen said.
It’s worth noting that there are a number of similar tools out there already, including venture-backed startups such as Sana and Kore.ai, which are substantively about bringing conversational AI to enterprise knowledge bases. Kapa.ai, for its part, fits into that bucket, but the company says its main differentiator is that it largely focuses on external users rather than employees — and that has had a big influence on its design.
“When deploying an AI assistant externally to end-users, the level of scrutiny jumps ten-fold,” Sorensen said. “Accuracy is the only thing that matters, because companies are worried about AI misleading customers, and everyone has tried having ChatGPT or Claude hallucinate. A few bad answers and a company will immediately lose trust in your system. So that’s what we care about.”
Accuracy
This focus on providing accurate responses about technical documentation, with minimal hallucinations, highlights how Kapa.ai is a different kind of LLM animal — it is built for a much narrower use-case.
“Optimizing a system for accuracy naturally comes with trade-offs, as it means we have to design the system to be less creative than what other LLM systems can afford to be,” Sorensen said. “This is to guarantee the answers are only generated from the universe of content they provide.”
Then there is the thorny issue of data privacy — one of the major deterrents for enterprises that may want to adopt generative AI but are wary about exposing sensitive data to third-party systems. As such, Kapa.ai includes PII (personally identifiable information) data-detection and masking, which goes some way toward ensuring private information is neither stored nor shared.
This includes real-time PII scanning: When a message is received by Kapa.ai, it’s scanned for PII data, and if any personal data is detected, then the message is rejected and not stored. Users can also configure Kapa.ai so that any PII data detected in a document will be anonymized.
Businesses can, of course, assemble something akin to Kapa.ai themselves using third-party tools such as Azure’s OpenAI service or Deepset’s Haystack. But it’s a time-consuming and resource-intensive endeavor, especially when you can just tap Kapa’s website widget, deploy its bot for Slack or Zendesk, or use its API that allows companies to customize things a little with their own interfaces.
“Most of the people we work with don’t want to do all the engineering work, or don’t necessarily have the AI resources on their teams to do so,” Sorensen said. “They want an accurate and reliable AI engine that they can trust enough to expose directly to customers, and which has already been optimized for their use-case of answering technical product questions.”
In terms of pricing, Kapa.ai says it uses a SaaS subscription model, offering tiered pricing based on the complexity of the deployment and usage — though it doesn’t publish these prices.
The company has a remote team of nine spread across the globe in two main hubs in Copenhagen, where Sorensen is based, and San Francisco.
Aside from lead backer Initialized Capital, Kapa.ai’s seed round saw participation from Y Combinator and a slew of angel investors, including Docker founder Solomon Hykes, Stanford professor and AI researcher Douwe Kiela, and Replit founder Amjad Masad.
Tech
Passionfroot raises $15M to expand its B2B creator marketplace to the US
Passionfroot, a German startup building a marketplace connecting B2B creators with brands, said on Wednesday it has raised $15 million in a Series A funding round led by Insight Partners.
Rebecca Liu-Doyle, managing director at Insight Partners, said Passionfroot is placed well at a time when creators are specializing as AI companies look for more visibility.
“Passionfroot has the perfect dynamics on both sides to warrant a true marketplace for B2B creators. On the demand side, there is increasing consumerization of the way B2B brands go to market. That’s a product of, in part, AI technology requiring evangelism, narrative building, and education. On the supply side, there are people who have real expertise, understand a market deeply, and want to create quality content,” she told TechCrunch over a call.
With the funding, the Berlin-based startup’s co-founder and CEO, Jen Phan, is moving to New York, where Passionfroot is opening an office to expand its U.S. operations. The company is also opening an office in São Paulo, and expanding its current headcount of 15 employees.
As AI makes it easier to build products, companies are focusing on using creators to improve brand recall and recognition, Phan said.
“Every head of marketing or growth leader I’m talking to is saying really the same thing: AI is commoditizing software and flooding every category with new products, features, and launches. It’s incredibly crowded and noisy. That is why B2B buyers are going to channels like LinkedIn, a creator’s Substack, or a podcast on YouTube to discover new products and tools,” she said.
Phan said over the last year, the company increased its revenue by 13 times, and onboarded clients such as ElevenLabs, Figma, Replit, Framer, and Gamma.
Since its last fundraise in 2024, the company has released an AI agent called Zest, which helps brands create, execute and monitor the performance of campaigns. Passionfruit claims Zest can also help companies find suitable creators both inside and outside the platform that are suited to its marketing strategy.
The startup says it uses a proprietary creator graph based on data about reach and performance from thousands of campaigns. There’s also a wallet that companies can use to pay creators across the globe, and measure their expenditure.
Passionfroot claims it has paid at least $10 million to creators on its platform in the last 18 months.
The company says it is working on helping its clients measure how a campaign is impacting AI citations, and how their brand appears in AI-powered answers. The startup is also planning to build AI features for creators, such as helping them with monetization tips and content ideas.
The funding comes as creator platforms like Substack and Beehiiv move to help creators find better monetization opportunities. Beehiiv launched a new community and ad marketplace last week, and Substack has introduced subscriber-only perks within newsletters.
Passionfroot’s Series A also saw participation from existing investors Creandum, Supernode Global, and s16vc. The company has raised more than $21 million so far.
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Tech
Cascade raises $3.5M to help construction firms find and win projects
Cascade, a startup building a platform to help architecture, engineering, and construction firms find and win projects, has raised a $3.5 million seed round from Andreessen Horowitz Speedrun, Ada Ventures, and Snowball VC.
Launched in 2025, Cascade is a result of its founders, Hannia Zia and Joana Ferreira, witnessing firsthand the difficulty construction businesses face with predictably securing work.
“My mother worked in a company that sold materials to construction companies, and my uncle built mansions in the Middle East. They’re incredible at their craft but just don’t have access to the right tools to get more work,” Ferreira told TechCrunch. And Zia recalled the time her father tried starting a construction business back in her native Pakistan: “He just couldn’t get enough projects to sustain himself.”
Zia describes the current process of finding construction projects as a “constant treasure hunt,” with firms having to log into each U.S. state, city, district, county, and federal agency’s portals. “So if you’re really good at building suspension bridges, you have to find all of those opportunities across these disparate portals.”
Cascade aims to help architecture, construction, and engineering firms on this front by tracking ongoing and upcoming projects, and then using prior tender data to predict which developers are likely to win the deals.
Here’s how the platform works: A company signs up to the platform, and then Cascade uses AI tools to determine which projects they have the best chance of winning. It also predicts what projects are coming up, using different signals and data points across U.S. states, local districts, private contracts, and federal agencies. For example, if a state announces a $100 million affordable housing grant, Cascade will monitor which developers won the grant the last time it was announced.
“We connect that data, and we tell our customers: ‘Most likely one of these five developers will win this newly announced grant, so go start talking to them to win projects,’” Ferreira explained.
The duo applied to a16z’s Speedrun last September. They said the pressure to do well on demo day and being around the “brilliance” of other founders helped the company sign contracts with firms that have built the JFK and La Guardia airports, Four Seasons hotels, and some data centers. “Speedrun gave us visibility and a stamp of approval to close big deals,” Zia said.
The startup will use the fresh cash to go to market, host industry events, and hire more engineers.
Other startups in this area include GovWin IQ and ConstructConnect, but Ferreira argues Cascade is a bit more AI-native than these platforms.
“Every time a customer wins a bid, they give feedback, so the system keeps getting smarter. Over time, we’ll have a complete map of the industry that our AI can traverse to predict the best projects and leads for each customer,” she said.
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Tech
If you pay a hacker’s ransom, chances are that they’ll come back for more
Governments have long warned not to pay a hacker’s ransom demands, arguing that doing so only lets criminals profit from their cyberattacks and funds the next one. There’s also another reason: The hackers are unlikely to leave you alone if you pay up once, and many will come back demanding more.
In a report published Wednesday, cybersecurity giant Proofpoint said it surveyed 953 companies and found that over one-third of companies that paid a hacker’s ransom were hit with a second extortion demand. The findings underscore the long-held understanding among security researchers and network defenders that it’s impossible to negotiate in good faith with an extortion racket because there’s no incentive for the other side to actually walk away.
Proofpoint’s data shows that ransomware attacks and extortion attacks have evolved from a single transaction where hackers would get paid once and move on, into an effort using multiple forms of leverage, such as retaining stolen data under the threat of publicly releasing it.
While hackers have claimed in the past that they will delete or destroy the victim’s stolen data, past incidents have shown that not to be the case.
Last month, a hack at market research firm Klue exposed data belonging to its customers, including several cybersecurity firms. The company said it struck a deal with the hackers, who claimed to have deleted the data, but the company later conceded that a separate hacking group swiped a sample of the company’s stolen data, leaving its customers exposed to potential future extortion demands.
A similar situation befell Change Healthcare in 2024, after a Russian-speaking ransomware gang stole the health and medical data of the majority of people in America, some 192 million people. Amid a dispute between the hackers and their affiliates (criminal groups often subcontract out attacks), Change Healthcare paid separate ransoms to both groups of criminals to keep the sensitive medical data off of the internet.
Security researchers have long suspected that ransomware gangs and extortion rackets will keep hold of the victim’s stolen data, even after a payment is made. U.K. law enforcement confirmed this during their takedown efforts targeting the prolific LockBit ransomware gang in 2024. Police said that they found victims’ stolen data stored on LockBit’s servers long after they had paid the ransom.
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