RAG & Knowledge Retrieval for Logistics & Supply Chain

Most RAG projects fail at retrieval, not generation, the model was fine, the right passage was never fetched. We benchmark retrieval separately, because that is where the accuracy actually lives.

In logistics & supply chain, your data depends on partners whose systems you do not control. That single fact reshapes how rag & knowledge retrieval has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is proof-of-delivery capture, usually integrated against TMS. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.

Multi-model by default, so a provider outage is a routing decision rather than an incident. Six weeks to something running in production, not six quarters to a strategy document.