Rebuild Your Core Business Processes with AI Automation

Teams often start by stitching together apps with visual workflow builders. These tools are great for simple triggers, but they fail when a core business process depends on them. Their pricing model, which charges per task or step, becomes expensive. A single new client onboarding can trigger 15 tasks, and at 100 clients per month, that is 1,500 tasks and a surprise bill.

A regional insurance agency tried to automate new claim intake this way. The workflow parsed an email, created a record in their claims system, uploaded files to Google Drive, and sent a Slack alert. The email parser misread policy numbers 10% of the time, creating junk data. The workflow had no validation logic. If the Google Drive step failed because a folder name already existed, the entire process would halt silently, requiring a manual check of every single claim.

This approach is fundamentally brittle. It spreads business logic across multiple third-party systems you do not control. When a step fails, you get a generic error message, not a specific HTTP status code or a payload you can debug. There is no central place to manage error handling, retries, or logging, making the process impossible to depend on.

Syntora's initial engagement would involve mapping your existing workflow into a detailed technical specification, often using Python for clarity. We would leverage libraries like httpx to interact directly with relevant service APIs, defining precise data schemas with Pydantic. This discovery and mapping phase typically takes 3-5 business days, establishing a robust blueprint for the new system.

The core logic would be developed as a single, unified FastAPI service. For AI-powered document processing, such as extracting specific data points from emails or other unstructured text, we would integrate with large language models like the Claude API. We've built similar document processing pipelines using Claude API for financial documents, and the same pattern applies to other complex document types. All custom business logic, including validation and routing rules, would be implemented as explicit Python code and maintained in a private GitHub repository. This approach consolidates fragmented processes into a high-performance, maintainable system.

The FastAPI service would be containerized with Docker for consistent deployment and then deployed to a serverless platform like AWS Lambda. This architecture ensures scalability and cost-efficiency, as you would only pay for active execution time. A robust CI/CD pipeline would be established using GitHub Actions to facilitate tested, automated deployments.

We would implement structured logging with tools like structlog, directing operational data to a dedicated database such as Supabase for comprehensive monitoring and querying. Robust error handling would be built in, including exponential backoff for API calls to manage transient network issues. Alerts for critical failures, such as repeated external API call failures, would be configured via AWS CloudWatch and delivered to team communication platforms like Slack, aiming for high system availability.