Deterministic Duty Drawback Automation

The real moat here is not using more AI, it is knowing exactly where AI cannot be trusted. Duty drawback is a rules and matching problem, not a language problem. The system has to connect import lines to export lines, apply the right regulatory method, and maximize the refund without mistakes. That makes deterministic optimization the core engine, while LLMs are useful mainly for turning messy PDFs and invoices into structured fields that can then be checked by humans and fed into the calculation workflow.

• In practice, the hard part is not writing text, it is pairing millions of import and export records under CBP rules. Pax describes the calculation as selecting feasible matches that maximize refund value, with zero error tolerance, which is why it uses regulation based algorithms instead of black box model outputs.
  1 sacra
• Legacy providers still collect bills of lading, invoices, PDFs, CSVs, and ERP exports, then key them into spreadsheets or old software. That is why claims can take nine to twelve months and why smaller claims are often uneconomic. AI helps most at the document ingestion layer, where speed matters more than perfect first pass accuracy.
  1 sacra
• The competitive split is becoming clearer. Charter Brokerage built a large manual services business, while newer players like Pax and Flexport use software and AI to lower cost to serve and raise refund yield. Flexport also positions AI around tariff refund automation, but still emphasizes measurable error rates and workflow control rather than fully autonomous judgment.
  1 sacra 2 sacra

The next wave of advantage will come from combining better document extraction with rule engines that update as tariff schedules and HTS classifications change. That pushes duty drawback toward mainstream software infrastructure for brokers, manufacturers, and retailers, instead of a slow specialist service reserved mostly for the largest enterprises.