1 link tagged with all of: reliability + inference efficiency + hallucination detection + factual grounding
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Probably raised $9 million to build an AI system that catches hallucinations and factual errors before they reach users. Their data-science tool wraps LLM outputs in a deterministic validator “mech suit,” letting it run smaller models locally while ensuring each answer matches the source data.
- Probably raised $9M from a16z to build a validator system that blocks LLM outputs unless they match source data exactly, aiming for 99.99% accuracy.
- This validation approach lets them use models "four classes weaker" than frontier LLMs, cheap enough to run on a desktop instead of a GPU farm.
- Elias argues big AI labs won't build this themselves because their revenue model benefits from users paying per interaction, including ones spent correcting errors.