A Done-For-You AI System Built in Under a Month for Your Firm

Many teams start with email parsing tools like Parsio or Mailparser. They work for structured emails from a single sender, but fail with varied formats. A customer might send a PDF attachment one day and a plain text order in the email body the next. These tools cannot handle that variance and require constant rule updates.

The operations manager then tries a point-and-click automation platform. They build a workflow that triggers on new emails, but the logic gets complex fast. A simple check to see if the customer is already in their TMS requires one path. A second check for priority status requires another branch. Soon they have a 50-step workflow with duplicated logic that is impossible to debug and costs over $400/month in task usage.

The core problem is that these platforms are designed for simple A-to-B connections, not business-critical logic. They lack proper error handling, version control, and the ability to process complex, unstructured data. When a workflow fails overnight, no one gets an alert, and 150 shipping requests are simply lost. This is not a tooling issue; it's an engineering problem.

Syntora would start an engagement with a detailed discovery phase to understand your specific document types, data extraction requirements, and existing workflows. This would involve collecting 100-200 examples of real inbound emails and attachments, along with mapping out every field you need to extract, such as Purchase Order numbers, shipping addresses, delivery dates, and item SKUs.

Based on this discovery, we would design and build prompt engineering strategies using the Claude API, aiming to reliably extract the required data from varied formats, including messy PDFs and poorly formatted email bodies. We've applied similar document processing pipelines using the Claude API for financial documents, and the same pattern applies to other industries with high-volume document intake.

The extraction logic would be built into a Python service using FastAPI. For each incoming email, this service would call the Claude API, validate the extracted data (for example, by checking if a SKU exists in your product database, which could be stored in Supabase), and format it into a clean JSON object. The system would be designed for high throughput, with individual processing cycles typically completing in a matter of seconds. This service would be containerized with Docker to ensure consistent behavior from local development and testing to deployment.

For deployment, Syntora would recommend and implement a serverless architecture, typically using AWS Lambda for its cost efficiency and scalability, where execution costs are minimized per request. An endpoint would be exposed via Amazon API Gateway. We would then configure your email server to securely forward relevant messages to this endpoint. The service would subsequently make a secure API call to your Transportation Management System or other relevant internal systems, creating a new record. This automated process is designed to significantly reduce the manual data entry time per request.

As deliverables, Syntora would provide the full source code in your private GitHub repository and a comprehensive runbook detailing the architecture, deployment, and operational procedures. A monitoring dashboard, potentially hosted on Vercel, could also be developed to provide visibility into processing volume, system latency, and an error log. This dashboard could be configured to send alerts to a shared Slack channel if, for instance, the Claude API fails to parse a document repeatedly, allowing for manual review of problematic emails.