Custom AI for Logistics Process Optimization

Many logistics companies first try off-the-shelf routing software. These tools are excellent for standard last-mile delivery but fail when faced with unique business rules. A distributor with constraints like vehicle-specific refrigeration, priority client time windows, and dynamic load matching finds these platforms too rigid. Their optimizers are black boxes that cannot be tuned for the specific trade-offs your business has to make every day.

A common next step is to use webhook-based platforms to connect a WMS to Google Maps. This approach is brittle and expensive. A 'new order' trigger that looks up a location, adds it to a sheet, and re-calculates a route can consume 3-5 tasks per order. For a business processing 200 orders a day, this results in over 600 tasks and a high monthly bill for a process that is slow and frequently breaks.

This forces an operations manager into a manual workflow. We see this with clients like a 30-person wholesale distributor. Every morning, their manager spent 90 minutes grouping 150 daily orders into routes for 5 trucks. They used a combination of local knowledge and Google Maps, but could never truly optimize for both fuel costs and client delivery windows, leading to constant rework and driver overtime.

Syntora's engagement would begin with a discovery phase to understand your specific logistics operations and data landscape. We would start by auditing your existing Transportation Management System (TMS) or Warehouse Management System (WMS) to identify optimal integration points via their native APIs. Our team would then design a data pipeline to securely extract historical order and delivery data, typically spanning the last 6 months. This data would be loaded into a Supabase Postgres database where we would clean, structure, and prepare it for building optimization models.

The core of the system would be engineered as a Python service, leveraging advanced libraries like Google's OR-Tools to solve complex vehicle routing problems. Syntora would work closely with your team to codify your specific business constraints, including vehicle capacities, driver hours, and customer delivery windows, ensuring the optimization logic aligns precisely with your operational realities.

This Python solver would be wrapped in a FastAPI service and deployed on a serverless platform such as AWS Lambda. The API would be designed to receive a list of the day's orders and return a JSON object containing optimized routes for each driver. This serverless architecture offers inherent scalability and cost-efficiency, as you only pay for the compute time utilized during route calculations. We've built similar data processing pipelines using Claude API for sensitive financial documents, and the same robust architectural patterns apply to logistics data processing and optimization.

The delivered system would integrate directly back into your TMS or WMS. Alternatively, Syntora can develop a simple front-end interface on Vercel to display the optimized routes. For operational visibility, we would configure structured logging using `structlog` and monitor system performance with AWS CloudWatch. Alerting would be established for critical events, such as route calculation job failures or unexpected timeouts, enabling our team to conduct immediate investigations and proactive support before operational impacts occur.