RAG in Supply Chain
RAG (Retrieval-Augmented Generation) in supply chain is the architectural pattern of combining a large language model with a retrieval system that pulls relevant operational data (shipment status, carrier performance, facility metrics, order details) from enterprise systems before generating a response.
Why It Matters
Generic LLMs do not know your shipment status, your carrier's performance, or your facility's dwell time. RAG bridges this gap by retrieving relevant data from the Digital Twins and Graph before the model generates a response. Without RAG, conversational analytics would produce generic answers. With RAG, they produce data-grounded, customer-specific intelligence.
The FourKites Perspective
FourSight AI uses RAG architecture: when you ask 'which carriers had the worst on-time rate this quarter,' the system retrieves your specific carrier performance data from the Graph, enriches it with network benchmarks, and generates a response grounded in real data. The retrieval spans all Digital Twins and the full Graph intelligence layer, ensuring answers reflect the complete operational picture.