# Retail AI platform cuts hallucinations from 15% to 1.5% with infrastructure changes

> A large retailer reduced misleading AI output by moving LLM concerns to a shared platform, not by changing the model.

Oossa · 2026-10-05 · https://oossa.com/en/retail-ai-platform-cuts-hallucinations-from-15-to-1-5-with-infrastructure-change

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.

## The facts

- Hallucination rate fell from 15% to 1.5% in six months.
- The platform serves dozens of application teams across inventory audits and replenishment reviews.

## Why it matters

For retail operations teams, the lower hallucination rate means fewer false inventory recommendations and less costly manual correction.

## Sources & references

1. [Article: The Platform Engineering Playbook for Production LLMs](https://www.infoq.com/articles/platform-engineering-playbook-production-llms/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=AI%2C+ML+%26+Data+Engineering) – InfoQ, 2026-10-05

Last updated: 2026-10-05