Resect AI secures $25M to tackle LLM hallucinations during inference

Resect is going after one of the hardest and most commercially important problems in the current LLM stack: not just spotting hallucinations after the fact, but intervening while a model is still forming an answer. Its pitch is an accountability layer that can inspect internal signals, detect failure modes in flight, and modify model behavior before output reaches the user.([resect.ai](https://resect.ai/news-releases/resect-ai-launches-out-of-stealth)) If that works at scale, it could make high‑stakes deployments in finance, healthcare, and law far more palatable, because operators would gain a new control point between the raw model and the end user.

For the race to AGI, this is less about raw capability and more about making powerful models governable enough to embed everywhere. A credible mid‑stream control would lower perceived risk, accelerate enterprise adoption, and potentially increase demand for even larger, more capable base models whose behavior needs managing. At the same time, the technical bar is extremely high: Resect has not yet released reproducible benchmarks showing that its approach improves outcomes without adding crippling latency or false positives. Until that evidence appears, incumbents like OpenAI, Anthropic, and Google can point to their own guardrail stacks and ask whether another control layer is worth the complexity.