Adaptive confidence thresholds: how it works and when to use it
Adaptive confidence thresholds, learns the right reuse bar per intent class with a feedback loop, and persists it across restarts, so it stops both over-serving and missing safe hits. 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. Adaptive confidence thresholds is how Crowkis learns the right reuse bar per intent class with a feedback loop, and persists it across restarts, so it stops both over-serving and missing safe hits.
How it works
Crowkis learns the right reuse bar per intent class with a feedback loop, and persists it across restarts, so it stops both over-serving and missing safe hits. 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 "..." WITHCONFIDENCE # returns the score it gated on
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Adaptive confidence thresholds 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.