AI hallucinations have graduated from amusing party trick to genuine liability. Probably, a San Francisco-based startup founded by Peter Elias, just raised $9M in seed funding to make sure AI systems stop confidently lying to people.
The round was led by Andreessen Horowitz, and the capital will fund what Probably describes as a reliability layer for AI: a system designed to catch factual errors before they ever reach end users.
The 99.99% problem
Probably is targeting 99.99% accuracy on precision-sensitive tasks. In English: for every 10,000 answers, the system aims to get no more than one wrong.
The company’s approach wraps language models in what it calls deterministic validators. The LLM generates a first-pass answer, and the validation layer checks whether that answer holds up against verifiable data before it gets delivered.
