Klarent, the Zurich-based autonomous software testing startup founded by former Google engineers, has raised $8 million in seed funding led by Mosaic Ventures, with participation from Moonfire Ventures and angel investors including Jürgen Galler, Harris Barton, and AngelInvest Ventures. The round brings Klarent’s total disclosed funding to approximately $8.2 million.
The funding comes as Klarent prepares to expand into the United States and the UK, hiring sales teams to bring its deterministic autonomy approach to enterprise QA teams. The company’s platform is already used by JD Sports, NZZ, and Sixt, with customers running approximately 3,500 tests daily on the platform .
Klarent Funding Overview By CREDX Media
| Startup Name | Klarent (formerly Fore AI) |
| Founders | Asheem Panakkat (CEO), Momchil Ivanov (CTO) |
| Founded | 2023 |
| HQ | Zurich, Switzerland |
| Round | Seed Funding |
| Amount Raised | $8M (€7.13M) |
| Valuation | Not disclosed |
| Category | AI Software Testing, Enterprise QA, Developer Tools |
| Lead Investor | Mosaic Ventures |
| Key Investors | Moonfire Ventures, AngelInvest Ventures, Jürgen Galler, Harris Barton |
| Total Funding Raised | ~$8.2M |
| Employees | 25 |
| Key Customers | JD Sports, NZZ, Sixt |
| Testing Volume | ~3,500 tests/day (one customer) |
💡 Use of Funds
- US and UK Sales Expansion: Building sales teams in the United States and the United Kingdom .
- Engineering Growth: Expanding the engineering team to deepen platform capabilities .
- Cross-Platform Testing: Broadening support across web, Android, and iOS applications .
Startup Overview: Klarent
What Klarent Is Building in Simple Words
Klarent has built an autonomous quality assurance platform that uses AI agents to write and maintain software tests—but with a critical design choice: the AI writes the tests, it doesn’t run them. The resulting test code executes deterministically, producing repeatable, reliable results that enterprises can trust in production .
- Deterministic Autonomy: LLMs author and adapt test code, but the tests themselves run deterministically—no probabilistic model calls at runtime .
- Human-in-the-Loop Verification: Every result is checked by a human engineer before being reported as verified. Customers only pay for outcomes .
- Self-Healing Tests: The platform’s tests can adapt when interfaces change, reducing the maintenance burden that plagues traditional automation .
- Multi-System Workflows: At JD Sports, Klarent’s agents run processes spanning the order on the website, the order management system, and the delivery systems—a level of end-to-end automation that in-house SDET teams couldn’t replicate .
- Deployment Flexibility: Available in the cloud, private cloud, or on-premises .
Why Klarent Matters
- AI-Generated Code Is Creating a Testing Bottleneck As coding agents produce more software, human testing capacity cannot keep pace. Mosaic Ventures Partner Chandar Lal framed the problem: “With AI, code generation is scaling faster than our ability to test it, so QA will become the key bottleneck” .
- Traditional Outsourced QA Is a $35 Billion Market Ripe for Disruption CEO Asheem Panakkat estimates the quality assurance market at approximately $35 billion, with most carried out manually . Traditional outsourcing relies on “brute-force” test script creation—a model Klarent directly challenges .
- First-Generation AI Testing Tools Are Unreliable Panakkat is direct about competitors’ approaches: “Many of the startups in this area are taking an approach which I would refer to as probabilistic, in which agents make model calls as the software runs. It’s very impressive on a small scale, but it doesn’t actually work when it comes to enterprise scale” .
- Enterprise Traction Validates the Model Klarent’s customers span e-commerce (JD Sports), media (NZZ), and travel (Sixt), with expansion underway into financial services including top-tier banks and insurance companies .
Startup Story: From Google Product Teams to Autonomous QA
Klarent’s founders spent over a decade at Google before leaving in 2023 to build something new. Asheem Panakkat worked on Google Maps, Search, Shopping, and Lens. Momchil Ivanov was a staff software engineer focused on information retrieval .
The founding insight came from watching AI transform software development from the inside. As Panakkat put it: “Our first step was to develop evaluations for LLMs, but we soon realised that the greater opportunity lay in QA. Since AI is going to write more of the world’s code, testing will cease to be a cost centre and will instead become the factor that decides whether any of that code can be trusted” .
The company originally operated as Fore AI, developing evaluation tools for large language models, before pivoting to the larger opportunity in software testing. That shift—from LLM evaluations to autonomous QA—is what attracted investors. Mosaic’s Chandar Lal noted: “At Google, both Asheem and Momo noticed this phenomenon before the rest of the industry did” .
By October 2026, Klarent had grown to 25 employees and secured customers including JD Sports, NZZ, and Sixt. The company also landed on the Microsoft Marketplace, providing a distribution channel that can shorten enterprise procurement and security review cycles .
Our Take on Klarent
Klarent is solving the least glamorous problem in software development—test maintenance—and that’s precisely why it matters.
Every engineering leader knows the pain: test suites grow, interfaces change, and maintenance costs spiral. Traditional automation tools break. AI-generated code compounds the problem by increasing the volume of code that needs verification. Klarent’s insight is that AI should write tests deterministically, not decide pass/fail probabilistically at runtime .
The $8 million seed is justified on multiple fronts: a clear technical differentiator (deterministic execution with human verification), named enterprise customers across multiple industries, and founders with deep product and engineering credibility from Google. Mosaic’s leadership signals conviction that Klarent can become the control layer for enterprise testing as AI-generated code proliferates.
The real risk is the human-in-the-loop ceiling. Klarent’s verification layer is its differentiator—it’s what makes enterprises trust the platform in production—but it also creates a service cost that must fall relative to usage as the company scales. As EUVC noted, “The US expansion will therefore test more than demand: it will show whether Klarent can preserve enterprise confidence while automating enough of planning, maintenance and verification to keep delivery economics attractive” .
For now, Klarent has capital, customers, and a clear thesis: deterministic tests for a world where AI writes the code.
Founder Background: The Google Engineers Who Saw the Testing Bottleneck Coming
Asheem Panakkat (CEO) spent 12 years at Google, working on products including Shopping and Lens. He holds an MBA from the Indian School of Business.
Momchil Ivanov (CTO) was a staff software engineer at Google focused on information retrieval.
Their Google pedigree—and the fact that they’re product builders rather than career QA specialists—shaped Klarent’s approach. As Startuply noted, “They are product builders who understand the scale and complexity of modern software release cycles, and the bottlenecks that form around manual testing”.
The AI Testing Race Nobody Expected
Klarent enters a software testing market that is being reshaped by AI.
Traditional test automation vendors including Selenium, Cypress, and Playwright rely on deterministic scripts but require significant maintenance and don’t handle AI-generated code well.
First-generation AI testing tools use probabilistic model calls at runtime, which Panakkat argues fail at enterprise scale .
Klarent’s differentiation is the combination of AI authorship with deterministic execution and human verification. The company calls this “deterministic autonomy”—a design choice that addresses the reliability concerns that prevent enterprises from trusting AI in production QA .
The competitive moat, if it materializes, is workflow entrenchment. Each successful deployment makes the platform more valuable for the next customer, particularly as Klarent extends from web to native Android and iOS testing .
Founder Intelligence: What Founders Can Learn from Klarent
- Pivot when the bigger problem appears: Klarent started with LLM evaluations but recognized that AI-generated code would make testing the critical bottleneck. The pivot from a smaller market to a larger one attracted investors and enterprise customers.
- Determinism is a feature, not a constraint: Panakkat’s insistence on deterministic execution rather than probabilistic model calls at runtime is what makes enterprises trust the platform in production. Reliability beats raw capability in regulated environments.
- Human-in-the-loop can be a moat: The verification layer creates cost, but it also creates trust. Enterprises pay for outcomes they can verify.
- Named customers attract investors: JD Sports, NZZ, and Sixt provided validation before the seed round closed. Enterprise references matter more than user growth metrics in B2B.
Investor Intelligence: Why This Round Gets Premium Capital
Mosaic Ventures led the round. Partner Chandar Lal framed the thesis around the widening gap between AI-assisted code generation and testing capacity. He also identified traditional outsourcing as the primary competitor: “In the old days you depended on people, usually those who were outsourced to a low-cost country… It was a brute-force approach, and there is a huge service market in this area bringing in hundreds of billions of revenue” .
Moonfire Ventures participated, bringing early-stage European tech expertise. Angel investors include Jürgen Galler (former Google Director), Harris Barton, and AngelInvest Ventures .
The investor composition signals conviction: Mosaic’s enterprise software focus validates the market opportunity, while the Google-alumni angel network provides both capital and connections.
The Road Ahead for Klarent
Expansion Targets
With $8 million in capital, Klarent will:
- Build sales teams in the United States and United Kingdom
- Grow engineering headcount beyond the current 25 employees
- Deepen cross-platform testing across web, Android, and iOS
- Expand into financial services, including top-tier banks and insurance companies
Competitive Response
Expect:
- Traditional test automation vendors to add AI capabilities
- First-generation AI testing startups to address enterprise reliability concerns
- Large outsourcing providers to face displacement pressure
- Klarent’s moat: deterministic execution + human verification + enterprise trust
Conclusion
Klarent’s $8 million seed funding validates the thesis that AI-generated code requires a new approach to software testing—one that combines AI authorship with deterministic execution and human verification. The company’s named enterprise customers and 3,500 daily tests demonstrate early product-market fit.
Market tailwinds favor Klarent: the $35 billion QA market dominated by manual processes, the growing volume of AI-generated code, and enterprise demand for reliable, repeatable testing. The company’s Google pedigree, deterministic approach, and Microsoft Marketplace distribution position it to capture this opportunity as QA shifts from cost center to trust layer.
For deep analysis of AI funding, developer tool innovation, and companies reshaping enterprise software infrastructure, explore CREDX Media for comprehensive coverage of startup funding trends, valuation intelligence, and founder playbooks.
Want the latest funding news and market insights delivered straight to your inbox? Subscribe to CredX Letters for verified deal terms, valuation benchmarks, and daily macro signals.
Frequently Asked Questions
1. What is Klarent’s total funding and latest round size?
Klarent has raised approximately $8.2 million total. Its $8 million seed round was led by Mosaic Ventures in October 2026 .
2. How much did Klarent raise in its seed round and who led the investment?
Klarent raised $8 million (€7.13 million) in seed funding led by Mosaic Ventures, with participation from Moonfire Ventures, AngelInvest Ventures, Jürgen Galler, and Harris Barton .
3. What is the valuation of Klarent?
Klarent did not disclose its post-money valuation. The company has raised approximately $8.2 million in total disclosed funding .
4. Who are the major investors in Klarent?
Major investors include Mosaic Ventures (seed lead), Moonfire Ventures, AngelInvest Ventures, Jürgen Galler, and Harris Barton .
5. Who are Klarent’s founders?
Klarent was founded in 2023 by Asheem Panakkat (CEO) and Momchil Ivanov (CTO), both former Google engineers. Panakkat worked on Maps, Search, Shopping, and Lens; Ivanov focused on information retrieval .
6. What is Klarent’s business model?
Klarent operates a B2B SaaS model with outcome-based pricing. Customers only pay for verified results, and the platform is backed by a human-in-the-loop service that checks every outcome.
7. What is deterministic autonomy?
Deterministic autonomy is Klarent’s approach where LLMs author and adapt test code, but the tests execute deterministically—without probabilistic model calls at runtime. A human-in-the-loop verifies every result.
8. What customers use Klarent’s platform?
Klarent’s customers include JD Sports (e-commerce), NZZ (media), and Sixt (travel). The company is also expanding into financial services with top-tier banks and insurance companies.
9. What are Klarent’s plans for the $8 million seed capital?
Klarent will use the funding to establish US and UK sales operations, grow its engineering team, and deepen cross-platform testing across web, Android, and iOS.
10. How does Klarent differ from traditional test automation?
Traditional automation requires manual script creation and breaks when interfaces change. Klarent uses AI to write and maintain tests that self-heal, while executing deterministically for repeatable results.
11. Who is the CEO of Klarent?
Asheem Panakkat serves as CEO and co-founder of Klarent. He spent 12 years at Google working on products including Shopping and Lens.
Join the Discussion
No comments on this briefing yet
Join the conversation above. Share your thesis, legal nuance, or empirical observations.