Latent

Latent catches wrong or unsupported LLM answers before they reach users by reading the model's own internal state as it generates — no second checker model, no added latency, and nothing leaving your deployment. Teams get a review queue of the answers most likely to be wrong, calibrated to their own model and traffic, with a plain explanation of why each was flagged.

LLMs are now in production across finance, legal, healthcare and customer support, where a confident-sounding wrong answer is a real liability. The usual fix — running a second model to grade every output — doubles inference cost and latency, which Latent sidesteps entirely.

Founded in 2026 and based in San Francisco, Latent is tackling the gap between models that sound right and models that are right. The company says it verifies outputs from the inside rather than bolting on an external judge.

Founded by Vedant Gaur, Latent is early and pre-product-at-scale, so the signal here is the approach rather than a long track record. Reading a model's internal state for real-time verification is a sharp, specific bet.