Automate Manufacturing Processes with Claude AI

A small fabrication shop receives purchase orders as PDFs via email. An office manager manually reads each PDF, extracts the part number, quantity, and due date, then types it into an Excel sheet. This sheet is used to schedule jobs on the shop floor.

This manual entry takes 10-15 minutes per order and is prone to typos. A single mistyped part number can lead to manufacturing the wrong component, wasting materials and delaying a shipment. The process halts completely when the office manager is on vacation or overloaded with other tasks, creating a critical single point of failure.

Some shops attempt to fix this with brittle OCR scripts, but these fail when a customer sends a PO with a slightly different layout. Off-the-shelf ERP systems are priced for large enterprises and require six-figure budgets and lengthy implementation. A specific module for automated order entry is an expensive add-on that still requires a rigid PO format that customers will not follow.

Syntora's approach to automating manufacturing processes with Claude API would typically involve several key stages. We would begin by auditing existing workflows and collecting 50-100 sample purchase order PDFs or other target documents, along with the corresponding data entered manually. Using Anthropic's Claude API, we would engineer a system prompt to instruct the model to act as a data entry specialist, defining the exact output schema required using Pydantic. This ensures every API response is a clean, predictable JSON object containing fields like `part_number`, `quantity`, and `customer_id`.

The core logic of the system would be built using a FastAPI application. Upon the arrival of a new email with an attachment, for example, a webhook could trigger an AWS Lambda function. This function would download the PDF, send it to the Claude API with our engineered prompt, and receive structured data back. The system would include logic to handle multi-page documents and purchase orders with multiple line items, parsing each one correctly.

Parsed data would then be validated against existing records, potentially in a Supabase database. This validation step verifies if a `customer_id` matches a known client or if a `part_number` is in the product catalog. If validation passes, the data would be written to the job scheduling system via the Google Sheets API or a direct database connection. If validation fails, an alert could be sent to a Slack channel with a link to the original PDF for human review. This design aims to significantly reduce manual error rates.

The entire application would be deployed on AWS Lambda. We would set up structured logging with `structlog` to track every step of the process. A simple Vercel frontend could provide a dashboard showing daily processing volume and the rate of validation failures, offering visibility into the system's operation. This engagement delivers a custom-built automation system, configured specifically for the client's operational needs and integrated into their existing tools.