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.
In plain words. For voice assistants, 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 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.