Crowkis for RAG document search: cut cost and latency
RAG document search are full of the same questions re-running retrieval over the same corpus. A safe semantic cache turns that repetition into instant, free hits.
RAG document search are one of the most repetitive LLM workloads there is: the same questions re-running retrieval over the same corpus. Every repeat is a full-price model call for an answer you already produced.
What Crowkis changes
Crowkis sits in front of your model and reuses answers by meaning, not exact text, so a reworded question still hits. It adds structural matching, per-hit confidence, freshness control, and tenant isolation, so reuse is safe, not just cheap.
In plain words. For RAG document search, the repetition is the bill. Remove the repetition and the bill drops.
On workloads like this, semantic caching cuts LLM costs up to 60-70% on repetitive workloads, and hits return in well under a millisecond, so RAG document search feel faster too. Community edition ships at full power, free to run.
The cheapest, fastest answer is the one you already have and can safely reuse.