Layered Agentic Retrieval for Retail Floor Questions: A Solo PoC

How I Routed Associate Questions Across Specialized TF-IDF Indexes Before Assembly TL;DR This write-up documents a personal experiment I ran while thinking about how retail associates actually use knowledge in the moment. A shopper rarely asks a question that fits neatly into a single policy PDF. The phrasing is noisy, the intent is mixed, and the clock is always ticking. I built a small Python system that treats each question as a routing problem rather than a retrieval problem with a single index. Three independent TF-IDF corpora stand in for returns policies, product care guidance, and service-floor procedures. An orchestrator scores each domain, retrieves top hits from the winner, and optionally blends in a second domain when the primary score looks weak. I kept the entire pipeline on-device without calling a hosted language model, because I wanted the evidence to be inspectable and reproducible on a laptop. The repository is public for learning purposes only; it is not a product r