Build Custom AI Automations That Run Your Operations

Many small businesses first turn to visual automation platforms to connect their apps. These tools are great for simple notifications, like posting a new sale to a Slack channel. The trouble starts when you try to run a core business operation on them. A workflow that qualifies a lead by checking your CRM, enriching the data, and checking a suppression list can burn 5-10 tasks per lead. At 100 leads per day, a single workflow can generate a massive monthly bill.

A 20-person logistics company faced this issue with their shipment request process. A customer fills out a web form, which triggers a workflow to extract data from the attached invoice PDF and enter it into their ERP. Their no-code platform's OCR module had a 15% error rate on varied invoice formats. There was no way to flag low-confidence extractions for human review, so every failure required an administrator to manually find the error and fix the ERP entry, defeating the entire purpose of the automation.

These platforms are fundamentally not built for high-reliability tasks. Their pricing models penalize complex logic. They lack proper error handling, retry mechanisms, and detailed logging needed for auditable, business-critical systems. When your core operations depend on a shared, multi-tenant platform, you cannot control for performance or guarantee processing integrity.

Syntora would start by auditing your existing manual workflow to map it into a series of logical steps and Python functions. This custom logic would be wrapped in a FastAPI service, exposing a private API that your organization would own. We would deploy this service on AWS Lambda, allowing for a pay-per-execution model that typically results in hosting costs under $20 per month for most workflows.

For document processing, the system would integrate an AI model like Claude API for initial data extraction. To ensure accuracy, especially for critical data, the architecture would include a human-in-the-loop validation step. If Claude API returns a confidence score below a configured threshold, the document and its extracted data would be sent to a simple, custom-built web interface for a human to review and approve with a single click. This approach balances automation efficiency with the need for high data integrity.

The core logic would connect to your other systems using `httpx` for asynchronous API calls. This allows the system to communicate with your CRM, ERP, or industry-specific platforms without blocking the primary workflow. Syntora engineers would write robust integration code that respects API rate limits, handles authentication securely, and incorporates retry logic with exponential backoff. The complete, production-ready source code would be delivered to your company's private GitHub repository.

For operational visibility, we would implement structured logging using `structlog`. Every step, API call, and outcome would be recorded in a Supabase database, creating a permanent audit trail. We would configure automated alerts, such as Slack messages, to notify stakeholders if the system's API error rate exceeds a defined threshold over a specified time window, allowing for proactive issue resolution.