LLM.txt & AI Crawler Setup Guide for Logistics companies

Create a text file at /logistics-ai.txt with a brief introduction of your logistics operations and data scope.

Include markdown-style links to your most critical data feeds, API documentation, and real-time tracking portals.

Add a 'Data Glossary' section in the file to define key logistics terms and their associated data structures for AI consumption.

User-agent: SupplyChainBot Allow: /shipment-data/ Allow: /inventory-levels/ Disallow: /internal-communications/

Verify your crawler permissions using a simulated logistics data environment tester.

Monitor crawl frequency and data payload size in your server logs to ensure SupplyChainBot is accessing relevant, high-value data nodes.

Wrap your primary shipment status updates in schema.org/Shipment or schema.org/Delivery updates.

Use JSON-LD for structured data, defining properties like 'carrierName', 'trackingNumber', 'estimatedDeliveryTime', and 'originAddress'.

Ensure all historical performance data tables use proper semantic headers (e.g., 'On-Time Delivery Rate', 'Transit Time Variance') for structured data extraction.

Keep related shipment events and status updates within logical data chunks (e.g., < 1000 data points per logical segment).

Avoid 'floating' data points; explicitly link related cargo IDs, order numbers, and container manifests within each chunk.

Eliminate ambiguous references (e.g., 'the package', 'that delay') and replace them with specific identifiers like 'Order #12345' or 'Container XYZ'.