Confidence scoring on every hit: how it works and when to use it
Confidence scoring on every hit, returns a per-hit confidence score (a geometric mean of similarity, freshness, trust, validation, and domain accuracy) so you decide the bar reuse must clear. Here's how Crowkis does it and why it matters for cost and safety.
Your users ask the same things all day, phrased a hundred different ways. Confidence scoring on every hit is how Crowkis returns a per-hit confidence score (a geometric mean of similarity, freshness, trust, validation, and domain accuracy) so you decide the bar reuse must clear.
How it works
Crowkis returns a per-hit confidence score (a geometric mean of similarity, freshness, trust, validation, and domain accuracy) so you decide the bar reuse must clear. It runs inside one Redis-compatible engine, so it composes with semantic caching, agent memory, and the other intelligence layers instead of being a separate service you wire together.
CGET "refund policy?" WITHCONFIDENCE
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Confidence scoring on every hit is part of what makes that reuse safe rather than reckless, the difference between a cache you trust in production and one you audit after every incident. It's one self-hosted binary, Redis-compatible, free to run.
Infrastructure earns the critical path one boring, verifiable feature at a time.