Practical AI, not AI for its own sake
AI is useful in logistics where the input is messy and the rules are clear: reading documents, sorting emails, pulling the important details out of unstructured text. It is not a replacement for your business rules or for the people who make final decisions.
Potential applications
- Freight document extraction. Pull fields from rate confirmations, bills of lading and PODs.
- Email and request classification. Sort incoming messages so they reach the right person or queue.
- Organizing unstructured information. Turn free-text notes and attachments into structured records.
- Document review assistance. Flag missing or inconsistent fields for a person to check.
- Internal summaries. Short summaries of long threads or documents for your team.
- Rule-based routing. Send requests where your business rules say they should go.
Validation is part of the design
AI-generated information should be validated before it affects financial records, shipment decisions or other important operations. We build schema checks, business-rule validation and human review steps into the workflow, and we let regular code, not the model, handle calculations.
Experience with AI in production
We have built LLM pipelines where deterministic code computes every number and the model only classifies and writes, with validation that rejects bad output. The same discipline applies to logistics documents and data.