Crowkis for voice assistants: cut cost and latency
voice assistants are full of latency-sensitive, repetitive spoken queries. A safe semantic cache turns that repetition into instant, free hits.
Voice assistants are one of the most repetitive LLM workloads there is: latency-sensitive, repetitive spoken queries. 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.
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 voice assistants feel faster too. It's one self-hosted binary, Redis-compatible, free to run.
The cheapest, fastest answer is the one you already have and can safely reuse.