Enterprise RAG Support Platform

An employee asks an operational question in their own words. The pipeline loads session context, classifies intent and decides whether to clarify first, then rewrites the question for retrieval while preserving exact tokens like form codes and policy numbers for the keyword leg.

Hybrid retrieval pulls candidates by semantic similarity and exact keyword match; a cross-encoder reranks them down to the few best passages. The evidence-sufficiency gate then asks a simple question - is this good enough to answer? If yes, the model generates an answer drawn only from those passages, with inline citations and a confidence score, and a verification pass claim-checks each assertion against its cited source before anything reaches the employee.

Not every question has a good answer in the corpus, and the system is built to admit that. If the evidence gate fails, the pipeline never spends on generation - it short-circuits to a clarifying question or to an escalation. If the answer is generated but verification or routing finds the confidence too low, the same honest off-ramp applies.

An escalation isn't a dead end: it lands in the operator console's queue with the full conversation, the retrieved evidence and the AI's draft attached, routed to a suggested team. A human resolves it, and the audit trail records who did what. Those escalations and the thumbs-down feedback also feed the Knowledge-gaps view, which is how the corpus gets better over time.