Hiring an AI Consultancy for Logistics Vendor Management

Many small logistics companies and 3PLs rely on their TMS, like McLeod or MercuryGate, for core operations. These systems are excellent for managing loads and tracking shipments once data is inside them. The problem is getting the data in. When a new carrier is onboarded, an operations manager receives a 30-page PDF contract and a 15-tab Excel rate sheet. They spend hours manually keying liability limits, payment terms, and thousands of lane rates into the TMS.

Consider a 15-person freight brokerage that needs to add a new LTL carrier to their network. The carrier's rate sheet is a PDF with complex tables and footnotes detailing accessorial charges. Standard OCR tools fail here because they cannot interpret the structure of the table or understand that a footnote on page 12 applies to a rate on page 3. The brokerage team resorts to printing the document and entering the data by hand, a process that takes half a day and is prone to costly typos.

This manual bottleneck is not an oversight by TMS providers; it is a structural limitation. TMS platforms are designed for structured data transactions, not for interpreting unstructured, human-readable documents. They lack the sophisticated parsing capabilities needed to make sense of legal language in a contract or the context-dependent nature of a rate sheet. The result is a permanent operational drag, where your most valuable people spend their time on low-value data entry instead of booking freight.

The first step would be a discovery audit of your existing carrier documents. Syntora would analyze a sample of 10-15 contracts and rate sheets (PDFs, Excel files, Word docs) to map every critical data point, from insurance coverage limits to specific fuel surcharge formulas. This audit produces a definitive data schema that becomes the blueprint for the entire system. You see exactly what will be extracted before any code is written.

The technical approach uses the Claude API for its advanced document comprehension capabilities, running within a Python environment. For each new document, a script running on AWS Lambda would send the content to the Claude API with a prompt engineered to extract the data points defined in our schema. The structured JSON output is then validated using Pydantic and stored in a Supabase database. This approach can parse a 50-page contract in under 90 seconds.

A lightweight FastAPI service would expose this clean data via a secure API endpoint. This endpoint allows your existing TMS to pull carrier information on demand, or the system can be configured to push updates directly into your TMS. The delivered solution includes a simple web interface for your team to upload new documents and review the extracted data. The entire cloud infrastructure is designed to operate for under $50 per month at a scale of several hundred documents.