Stop Fighting Off-the-Shelf Tools. Get a Custom AI Process Built.

Most small businesses first try point-and-click automation platforms. These tools are great for simple A-to-B connections, but they break down when faced with complex, multi-step logic. Their conditional paths often cannot merge, forcing you to build duplicate, hard-to-maintain branches that burn through your task allowance on every run.

A regional insurance agency with 6 adjusters tried to automate their claims intake process. New claims arrived as PDFs attached to emails. Their CRM's automation module could trigger on a new email, but it could not read the PDF attachment. It could only parse the email body, which missed 90% of the required information. Every single claim still required manual review and data entry, defeating the purpose of the automation.

Their next step was hiring a freelancer to write a script. The script worked for one specific PDF layout from a single carrier. When a slightly different form arrived, the script failed silently, losing the claim entirely. There was no error handling, no logging, and no monitoring. It was a temporary fix, not a reliable business system that the company could depend on.

Syntora would start an engagement by auditing your existing process and analyzing 15-20 examples of your source documents or data inputs. We would work with your team to define the key data fields required, such as policy number, claimant name, and date of loss, establishing a precise data map that forms the foundation of the system. This initial discovery phase is crucial for ensuring the solution accurately reflects your business logic and integrates effectively with your operations.

Next, we would design and build the core processing engine. The system would use Python libraries like PyMuPDF to extract raw text from documents. We would then develop a prompt for a large language model, such as the Claude 3 Sonnet API, to reliably extract the defined fields from the raw text and structure them as JSON. This prompt chain would be engineered to handle variations in form layout without needing extensive rule sets. We would wrap this extraction logic in a FastAPI service, providing a clear API endpoint for processing. FastAPI handles synchronous and asynchronous requests efficiently, ensuring the core service is responsive. All operations within the service would be logged using structlog for clear, machine-readable audit trails.

The FastAPI application would be deployed as a container on a serverless platform, typically AWS Lambda, fronted by an API Gateway endpoint. This architecture allows for cost-effective, usage-based billing, scaling automatically with demand. We would integrate the endpoint with your existing systems, such as an email provider to trigger processing from new attachments, or directly with your enterprise resource planning (ERP) system. Extracted data would be sent to your ERP via a direct API call, using libraries like httpx for robust communication.

For system monitoring and reliability, the system would store every processed file and its JSON output in a database like Supabase for a configurable period, typically 30 days. We would configure CloudWatch alarms to provide alerts via Slack or other communication channels if error rates exceed defined thresholds or if processing times indicate an issue. The client would receive the full source code in their private GitHub repository, along with a runbook detailing maintenance procedures and troubleshooting guides. A typical build of this complexity takes 4-8 weeks, depending on data variability and integration points.