Automate Your Logistics Operations with Custom AI

Many small logistics companies rely on a mix of their TMS, separate routing software, and manual checks of load boards. A tool like Route4Me can plan a multi-stop route, but it cannot dynamically account for complex constraints like vehicle capacity, HOS regulations, and specific delivery time windows simultaneously. The optimization is generic, not tailored to a fleet's historical performance or real-time driver availability from ELD data.

Consider a 15-truck freight brokerage. A dispatcher spends the first two hours of their day manually checking DAT and Truckstop for profitable loads that fit their available trucks' locations and hours. They copy-paste details into a spreadsheet, call drivers to confirm availability, and then manually enter the booked load into their McLeod TMS. A last-minute cancellation forces them to restart the entire manual search, wasting another 45 minutes of valuable time.

This inefficiency exists because off-the-shelf tools are built as closed ecosystems. Your TMS is designed to manage existing data, not actively source and score new opportunities from external platforms. Your route planner optimizes a static list of stops but cannot connect to your WMS to factor in pick-and-pack times. The structural problem is the lack of integration and custom business logic; each tool solves one piece of the puzzle, forcing dispatchers to become the manual, error-prone integration layer between systems.

The first step would be a data audit of your current systems. Syntora would map the data flow between your TMS, WMS, and any telematic (ELD) data to understand your operational reality. This process identifies the highest-value manual workflow to automate first, whether it is load matching, route planning, or rate comparison. You would receive a technical scope document detailing the approach and data requirements.

For a route optimization project, the technical approach would use a Python library like Google's OR-Tools to model your specific constraints. This logic would be wrapped in a FastAPI service and deployed on AWS Lambda for low-cost, on-demand processing. For load matching, a system would poll APIs from DAT and Truckstop, parse the data using Pydantic for validation, and score opportunities against your fleet's real-time location data stored in a Supabase database. We've built document processing pipelines using the Claude API for financial documents, and a similar approach would work for parsing unstructured BOLs or rate confirmation sheets.

The delivered system integrates directly into your existing software. A dispatcher would see an 'Optimize Route' button inside their TMS that triggers the AI model and returns the result in seconds. There are no new dashboards to learn. The system is delivered with full source code in your GitHub, a runbook for maintenance, and monitoring to track performance and cost, which typically runs under $50 per month on AWS.