Automate Repetitive Warehouse Fulfillment Tasks with AI

Many small logistics companies rely on a WMS like Fishbowl or SkuVault, which are excellent for managing inventory once the data is inside them. The bottleneck is getting the data in. When a key customer emails a multi-page PDF purchase order, someone on your team must manually read that PDF and type the SKUs, quantities, and shipping details into the WMS. This is the source of expensive, time-consuming errors.

Consider a warehouse that receives 50 PDF purchase orders a day. A staff member spends 5 minutes on each, totaling over 4 hours of pure data entry. If they mistype a single SKU or quantity, it causes a mis-shipment. The result is a costly return, a credit memo, a reshipment, and an unhappy customer. The problem compounds because each customer sends a slightly different PO format, so a rigid template-based extractor tool fails on the second document it sees.

Off-the-shelf automation platforms cannot solve this because they are built for structured data. You might connect your email to your WMS, but these platforms cannot intelligently read the contents of a PDF attachment with a multi-line-item table. Their document parsers are often limited to simple key-value pairs, not the complex tables found in a real-world PO. They lack the logic to handle variations in formatting or to flag ambiguous entries for human review.

The structural issue is that your WMS is a database that expects perfect, structured input. Your customers send you messy, unstructured documents. The gap between these two systems is currently filled by slow, error-prone manual labor. You do not need a new WMS, you need an intelligent bridge between the documents you receive and the system you already have.

The first step is a document audit. Syntora would start by collecting 20-30 examples of real purchase orders from your key accounts. We would analyze these to identify all the critical fields (PO Number, SKUs, Quantities, Ship-To Address) and document all the layout variations. This audit produces a clear data map that defines what the AI needs to extract and becomes the blueprint for the system.

The technical approach uses an AWS Lambda function that triggers whenever a new email with an attachment arrives in a designated inbox like orders@yourcompany.com. This function passes the PDF to the Claude API with a carefully engineered prompt that instructs it to extract the order details into a structured JSON format. A FastAPI service then validates this JSON using Pydantic schemas to ensure data integrity before making an API call to create the sales order in your WMS. If any data is missing or a SKU does not match your product catalog, the system automatically forwards the original email and the extracted data to a manager for a 15-second human review.

The delivered system runs entirely in your own AWS account. Your team continues their work in your existing WMS and communicates via email or Slack as they always have. The only difference is that new orders appear in the WMS seconds after the PO email is received, without any manual keying. You receive the full Python source code, a runbook for monitoring, and a simple Supabase dashboard to track processing volume and any exceptions.