Crowkis for onboarding assistants: cut cost and latency
onboarding assistants are full of every new hire asking the same first questions. A safe semantic cache turns that repetition into instant, free hits.
Onboarding assistants are one of the most repetitive LLM workloads there is: every new hire asking the same first questions. 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 onboarding 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 onboarding assistants 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.