Auto fact extraction from conversations: how it works and when to use it
Auto fact extraction from conversations, pulls durable facts out of a transcript deterministically, dropping questions, greetings, and filler, with no model call. Here's how Crowkis does it and why it matters for cost and safety.
Production LLM traffic is deeply repetitive, and repetition is exactly what a bill is made of. Auto fact extraction from conversations is how Crowkis pulls durable facts out of a transcript deterministically, dropping questions, greetings, and filler, with no model call.
How it works
Crowkis pulls durable facts out of a transcript deterministically, dropping questions, greetings, and filler, with no model call. 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.
CMEMEXTRACT support u_42 "<transcript>"
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Auto fact extraction from conversations 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.