Automate TMS Invoice Audits and Vendor Dispute Resolution

Many distributors rely on the built-in auditing modules of their Transportation Management System, like McLeod or TMW. These modules are rule-based and rigid. They can check if a line item matches a rate confirmation, but they cannot interpret context. A standard TMS cannot determine if a "lumper fee" is valid for a specific warehouse or if a "detention" charge corresponds to the driver's actual wait time recorded in a separate ELD system.

Consider a mid-sized food distributor with a 3-person AP team processing 500 carrier invoices a week. For each invoice, a clerk manually compares the PDF against the rate confirmation in their TMS. An invoice arrives with a line item for "Detention: 3 hours @ $75/hr". The rate confirmation allows detention pay after 2 hours free. The clerk must now find the load, pull up the driver's check-in/out times from the Motive portal, and manually calculate the difference. This single check takes 10 minutes. If missed, the company overpays by $75. If caught, it starts a 7-day email chain with the carrier.

Generic OCR tools also fail because they only extract text; they do not understand logistics. An OCR tool can pull "Fuel Surcharge" as text but cannot validate the percentage against the DOE weekly average for that specific lane and date. The output is just key-value pairs, not a structured, validated data object ready for payment.

The structural problem is that a TMS is a system of record, not a system of intelligence. Its architecture is built for transactional data storage, not for processing unstructured documents. The data models are fixed, making it impossible to add a new validation rule that cross-references an external data source without a costly, months-long project from the TMS vendor.

The first step is a thorough audit of your current invoice workflow. Syntora would review samples of your 5-10 most frequent carrier invoices and their corresponding rate confirmations. We would map every field, document all common accessorial charges, and confirm your TMS's API capabilities for retrieving load data. This audit produces a data mapping document that serves as the blueprint for the entire build.

We have built document processing pipelines using the Claude API for financial documents, and the same pattern applies directly to logistics invoices. A FastAPI service would use the Claude API to parse incoming documents like PDFs and emails into a structured JSON format. This structured data is then processed by a validation engine that checks it against information pulled from your TMS API and external sources like DOE fuel indexes. Pydantic models enforce strict data schemas, catching format errors before they cause downstream issues.

The delivered system would be a serverless function on AWS Lambda, triggered when a new invoice arrives in a designated email inbox or S3 bucket. It pushes validation results—approved, rejected, or needs review—and a reason code directly into your TMS. Your AP team would shift from manually checking 500 invoices to managing a small queue of 30-40 flagged exceptions per week. You receive the full Python source code, documentation, and a maintenance runbook.