AIUC raises $40M Series A to build a UL-style certification layer for AI agents

AIUC -- short for Artificial Intelligence Underwriting Company -- announced a $40 million Series A on September 15, led by Ribbit Capital with First Harmonic and Terrain also participating. Combined with a $15 million seed round from NFDG, that puts total funding at $55 million. The company was founded by Rune Kvist, an early Anthropic employee, and Rajiv Dattani, former COO of AI evaluation nonprofit METR.

The pitch: certify agents the way UL certified appliances

AIUC's own framing draws a direct historical parallel: "electricity burned down houses until insurers funded Underwriters Laboratories (UL) to certify products." Its AIUC-1 standard tests AI agents against roughly 5,000 risk-and-attack combinations -- jailbreaks, hallucinations, data leaks -- built with input from more than 250 security and risk leaders. Customers already certifying against it include Cursor, ElevenLabs, Harvey, KPMG, Lovable, UiPath and Fin.

Kvist told TechCrunch the underlying problem isn't capability, it's trust: "AI is getting smarter at an increasingly rapid rate," but that doesn't make it easier for regulated industries to adopt -- "banks, hospitals, governments and militaries" hold back not because a model can't do the job, but because "nobody can currently guarantee" it will stay inside the commitments those institutions have made to their own customers.

Why this matters for spend, not just safety

A certification layer like AIUC-1 is effectively a third-party answer to a question Merit AC's own scoring pipeline tries to answer internally -- whether AI output can be trusted enough to count as real work rather than something a human has to re-check anyway. If AIUC-1 (or something like it) becomes a standard enterprises require before expanding agent deployments, it becomes one more signal feeding the same buy decision as a spend/value dashboard: not just how much a company is spending on AI, but whether it's spending on agents cleared to act unsupervised.