Build Production-Grade Automation, Not Brittle Workflows

Visual automation platforms are great for connecting two systems with a simple trigger-action rule. The problem starts when you run a core business function on them. They lack robust error handling. When a third-party API has a momentary outage, the entire workflow run fails silently, losing the data. There is no automatic retry or dead-letter queue.

A regional insurance agency with 6 adjusters learned this the hard way. They used a visual builder to route new claims from a web form to their claims management system. The platform’s connector for their industry-specific software had a 5-minute sync delay. But worse, if the claims system's API was down for maintenance, new claim submissions from the website would fail and vanish. This resulted in 10-15 lost claims per month.

These platforms also penalize complexity. A workflow with conditional logic, like checking a customer's status in a CRM before sending an email, requires branching paths. These paths cannot merge back together, forcing you to duplicate every subsequent action. This inflates your monthly task count and creates a brittle, unmanageable diagram that no one on the team understands.

Syntora approaches custom automation engagements by first understanding your exact problem and data requirements. For a document processing task, the initial step would be to collaboratively define a strict JSON schema for the data you need to extract from your specific document types.

We would then engineer targeted prompts for the Claude API to perform optical character recognition (OCR) and data extraction, ensuring consistent output tailored to your documents. The core logic of the system would be deployed as a serverless function, for example, on AWS Lambda, triggered by events like a file upload to an S3 bucket or a new entry in a queue.

The system would use asynchronous API calls to Claude, allowing it to process multiple documents concurrently. To ensure reliability, we would implement exponential backoff retry logic for API calls. Should an attempt fail after a defined number of retries, the problematic file would be moved to a dead-letter queue, and an alert would be sent, preventing data loss.

All processed data, along with relevant metadata, would be stored in a Supabase Postgres database. This creates a permanent, auditable record showing when each document was received, processed, and the exact data extracted. Structured JSON logs, generated using `structlog`, would be sent to AWS CloudWatch for real-time monitoring and debugging. This level of observability is a critical advantage over no-code platforms.

Syntora would deliver the complete, production-ready source code, including infrastructure-as-code scripts, to your company's GitHub repository. A typical engagement for this complexity of document processing system would take approximately 4-8 weeks from discovery to deployment, depending on the number of document types and integration requirements. You would need to provide example documents, access to relevant stakeholders for requirements gathering, and designated cloud accounts for deployment.