Most of the AI industry is trying to fix hallucinations by building bigger, smarter models. A startup called Probably is betting on the opposite.
The company has raised $9m in a seed round co-led by Andreessen Horowitz and Accel, with Tokyo Black and Vermilion Cliffs Ventures, to catch AI’s factual errors before they ever reach a user. It is aiming for the 99.99% accuracy that ordinary software takes for granted but large language models rarely hit.
Its trick is to lean on the model less, not more. Probably’s first product, a local ‘verifiable data agent’ that answers questions from messy datasets, runs each answer through what founder Peter Elias calls a ‘data science mech suit’.
A harness, not a bigger brain
The model takes a first pass, then a separate, deterministic validator checks the answer against the actual data and bounces anything that does not match. The model is trained against that validator, and every result ships with a citation and an audit trail.
