Automate Warehouse Picking and Packing with Custom AI

Many SMB warehouses run on the built-in modules of ERP systems like NetSuite or inventory platforms like Fishbowl. These tools are great for tracking inventory counts but poor at optimizing physical movement. They generate pick lists sorted by SKU number or order entry time, not by efficient travel paths through the warehouse aisles.

Consider a 30-person warehouse team using their ERP's standard picking module. A multi-item order arrives, and the system generates a list that sends a picker from Aisle 1 to Aisle 5, then back to Aisle 2. The picker wastes minutes on every order zig-zagging across the floor, passing the same shelves multiple times. This lost travel time accumulates to hundreds of unproductive hours per month. Furthermore, special packing instructions in an order's text field are often missed by packers scanning SKUs, leading to a 2-3% error rate from incorrect packing.

The structural problem is that these off-the-shelf systems treat a warehouse as a list of database records, not a physical space. Their architecture is designed for transactional integrity, not spatial optimization. They lack the logic to calculate a shortest path based on real-world layouts, current picker locations, or even temporary obstructions. Customizing these platforms to add true optimization logic requires expensive, platform-specific consultants and long development cycles.

The first step is a warehouse process audit. Syntora would analyze 3 months of your historical pick lists and order data to identify common travel patterns and bottlenecks. We would also map the physical layout of your racking and bin locations, either from a diagram or a simple walkthrough, to create a digital model of the warehouse floor.

A custom system would use a graph-based algorithm to calculate the most efficient path for any given pick list. A lightweight FastAPI service would receive order data from your WMS via an API call, query the optimal route, and send a sequenced pick list to a simple web app on a tablet or mobile device. For packing, a Claude API pipeline would parse unstructured customer notes from new orders to flag special instructions, displaying them prominently at the packing station. The entire system would run on AWS Lambda, processing events for just a few hundred milliseconds at a time.

The delivered system integrates directly with your existing WMS. Pickers receive clear, step-by-step routes on their devices, reducing travel time by a projected 30-40%. Packers get explicit alerts for special handling instructions. You receive the full Python source code, a Supabase database for logging performance metrics with a pre-built dashboard, and a complete runbook for maintenance and monitoring.