A retail inventory accuracy team built a shared LLM platform and saw hallucination rates drop from fifteen percent to one point five percent after six months. The platform adds automated retry loops, an intent‑validation gate, and a prompt registry that can be updated at runtime. It also tracks token costs and enforces tool authorization at the resource server. The changes were applied to a multi‑agent system that runs on models like Gemini and GPT.
Why it matters
For retail operations teams, the lower hallucination rate means fewer false inventory recommendations and less costly manual correction.
