How I Built a Production RAG System for Warehouse Operators — Not Data Scientists
A solo engineer's honest account of shipping an AI querying layer on top of real logistics data in the UAE — what worked, what broke, and what I'd do differently. The Problem No One Talks About Most RAG tutorials start with a PDF and a Jupyter notebook. Real warehouse operators don't have PDFs. They have spreadsheets, WhatsApp voice notes, half-filled ERP exports, and a shift manager who's been…
A logistics company in the UAE developed an AI-powered system called Logistics Oracle to help warehouse supervisors ask questions about inventory and forecasting in plain language. The system retrieves data from a live database rather than using pre-processed PDFs or Jupyter notebooks, which is more suitable for real-world warehouse operations.
The architecture includes a FastAPI layer, PostgreSQL database, Redis cache, Groq LLM inference service, and a Next.js frontend with JWT authentication. The key challenges faced were retrieval quality, hallucinated data, and translating operator intent into structured queries. The system uses a weighted hybrid retrieval approach combining structured and semantic search, validates SKU codes against the database, and uses a lightweight intent classifier to map natural language queries to specific retrieval strategies.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
