Optimizing Supply Chain Operations with LLMs

Supply chain operations generate massive unstructured datasets. Shipping manifests, supplier contracts, customs declarations, and real-time IoT telemetry often arrive as long documents that traditional rule-based systems struggle to normalize. Large language models can bridge this gap by extracting entities, classifying exceptions, and generating structured decisions, but token-based inference costs scale directly with document length. For teams processing high-volume logistics paperwork or running multi-step agentic workflows, unpredictable per-token billing erodes the ROI of automation. Oxlo.ai offers a