Build Production-Grade AI Workflows for Your Business

Many businesses try to automate critical operations by stitching together multiple specialized SaaS tools. Consider a regional insurance agency with 6 adjusters. They use one tool to scan PDF claim reports, another for data entry, and manual work to connect the two. This approach fails because the individual tools are not designed to work together on a specific, multi-step business process.

The off-the-shelf PDF summarizer provides a generic text block, forcing adjusters to re-read the original 20-page document to find the policy number, incident date, and claimant address. The data entry tool has no context of the claim, leading to a manual data validation error rate of over 15%. This piecemeal method creates more work, as the team spends its time fixing errors and bridging the gaps between disconnected systems.

Trying to solve this with a generalist freelancer often fails for different reasons. They might write a Python script, but they lack the experience to deploy it as a production service on AWS, handle API failures gracefully, or set up the necessary monitoring. The project stalls, and the business is left with a script that only runs on a developer's laptop, not a reliable system for a business-critical process.

Syntora approaches custom AI workflow automation as a structured engineering engagement. The initial phase would focus on discovery and data pipeline establishment. We would audit existing client systems to understand integration points, such as connecting to a claims management system via its REST API. A secure AWS S3 bucket would be configured for ingesting incoming PDF reports. For industries dealing with unstructured documents, AWS Textract would be used to perform OCR on a representative sample of historical documents. This process creates a structured dataset essential for engineering and rigorously testing prompts for the core AI logic.

The core workflow would be built as a Python service using FastAPI. The architecture would leverage serverless functions, where an AWS Lambda function would be triggered when a new PDF report arrives in the S3 bucket. The FastAPI service would then call the Claude 3 Sonnet API with a carefully engineered prompt, designed to accurately extract specific fields relevant to the client's needs. The AI's response would be robustly parsed and validated using Pydantic, ensuring correctly typed JSON output and data integrity.

Extracted and validated data would populate a Postgres database, potentially managed by Supabase for scalability and ease of access. Syntora would develop a simple front-end dashboard, possibly hosted on Vercel, to provide a user-friendly interface. This dashboard would allow client personnel, such as adjusters, to review extracted fields alongside the original PDF for a final validation step. Upon approval, the structured data would be written back to the client's primary claims system, streamlining existing manual processes.

Monitoring and observability are integral to the system's reliability. Structured logging with structlog would send all events to AWS CloudWatch, providing comprehensive insights into system operation. Automated alerts would be configured for critical events, such as consecutive Claude API failures or elevated data validation error rates, notifying stakeholders via channels like Slack. Typical infrastructure costs for such a cloud-native architecture are designed for efficiency and scalability.