Crowkis for internal copilots: cut cost and latency
internal copilots are full of employees asking overlapping questions of the same knowledge base. A safe semantic cache turns that repetition into instant, free hits.
Internal copilots are one of the most repetitive LLM workloads there is: employees asking overlapping questions of the same knowledge base. 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 internal copilots, 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 internal copilots feel faster too. Runs self-hosted with zero egress, nothing leaves your machine.
The cheapest, fastest answer is the one you already have and can safely reuse.