LOGOS: AI-Powered Logistics Optimization

LOGOS (Logistics Optimization & Generative Operation Strategist) is an AI-powered operational concept for logistics and customs teams. It addresses regulatory friction, unstructured customs documentation, and supply-chain delays by turning fragmented operational data into guided, automatable workflows.

Python backend services orchestrate document processing, workflow logic, and operational integrations.

RAG (Retrieval-Augmented Generation) grounds AI responses in relevant customs, compliance, and operational knowledge.

Model Context Protocol (MCP) and function calling connect the workflow layer to ERP and customs APIs through deterministic actions.

XGBoost and LightGBM support predictive analytics for ETAs and potential delays.

DBSCAN and Isolation Forest identify anomalous records and operational patterns for review.

Deterministic integration patterns for ERP and customs API workflows.

Real-time NCM compliance checking to surface classification and regulatory considerations during operations.

Operational automation that routes relevant context, predictions, and exceptions to the people and systems responsible for next actions.

LOGOS is presented as a concept case study focused on the system architecture and product direction for AI-enabled logistics operations.