Zurich-based startup Klarent (formerly known as fore ai) has raised €7.13 million in Seed funding. The company develops AI-powered tools that automate software testing.
The funding round was led by London-based Mosaic Ventures, with participation from Moonfire Ventures and several angel investors.
Other investors include Jürgen Galler, a former director at Google, Harris Barton, and AngelInvest Ventures. The new funding will support Klarent as it continues to develop its software testing technology.
“At Google, Momo and I could see two things coming at once: LLMs were getting better fast, and the amount of software being created was about to explode. The question that wouldn’t leave us was the feedback loop. How do you keep these coding agents honest? We began by building evals for LLMs and quickly realised the bigger opportunity was QA itself. As AI writes more of the world’s code, testing stops being a cost centre and becomes the thing that determines whether any of that code can be trusted,” said Panakkat.
“With AI, code generation is scaling faster than our ability to test it, so QA will become the key bottleneck. Asheem and Momo saw this at Google before most of the industry did. What convinced us was their insistence on determinism – using AI to write tests, not to run them – which is why enterprises are trusting Klarent in production. We’re delighted to back the team as they expand to the US,” said Chandar Lal, Partner, Mosaic Ventures.
Klarent was founded in 2023 by two former Google engineers, Asheem Panakkat, who is the CEO, and Momchil Ivanov, who serves as the CTO.
Before starting Klarent, Panakkat spent more than 10 years at Google, leading teams working on Google Shopping and Google Lens. Ivanov worked as a Staff Software Engineer, focusing on information retrieval.
The founders saw a growing problem as AI models became more powerful and started generating more code. The tools used to test and check that code were struggling to keep up. They founded Klarent to help make software testing faster and more efficient.
