Custom-Build Your AI Automation, No In-House Team Required

Many businesses start by hiring a freelancer for a small automation task. The initial Python script works, but when an external API changes, it breaks. The code is undocumented and the original developer is unavailable, leaving you with a broken tool you cannot fix or extend.

Next, you might evaluate a full-service development agency. They quote a 3-6 month timeline and require a project manager, a business analyst, and two developers for a task one senior engineer could complete in three weeks. Your 10-person team spends more time in status meetings than seeing progress, and the budget is an order of magnitude too high.

This happens because your problem does not fit standard solutions. You need to connect to a proprietary CRM, parse a non-standard PDF format, or enforce complex business logic. Off-the-shelf tools lack the flexibility, and agencies add too much overhead. You need an expert who can write production code, not just connect pre-built blocks.

Syntora's approach to building AI automation starts with a detailed discovery phase to map your current manual workflows and identify key data sources. This includes understanding existing systems, API access requirements, and any rate limits. We would architect Python connectors, potentially using libraries like httpx, to securely handle authentication and data retrieval from your specific applications.

The core logic would be implemented as a FastAPI service. For a document processing workflow, for example, a new document upload or an event from an existing system could trigger the service via a webhook. It would then fetch relevant data and pass the document content to a large language model API, such as the Claude API, using a carefully structured prompt to extract specific fields or generate summaries. We have experience building similar document processing pipelines for financial documents using the Claude API, and this pattern applies directly to various document types. The system would be designed to execute these steps efficiently, with performance targets defined during the discovery phase.

Deployment of the FastAPI service would typically be on a serverless platform like AWS Lambda, optimizing for cost by only paying for compute time during actual processing. A small Supabase Postgres database would store an immutable log of all transactions, processing metadata, and any errors, providing a searchable audit trail without incurring high SaaS costs.

We would implement structured logging using structlog for easy debugging and monitoring. Automated alerts, triggered by log patterns such as repeated API failures, would notify relevant teams via a shared communication channel like Slack. As part of the engagement, Syntora would deliver the complete, tested source code to your GitHub repository, along with a comprehensive runbook for ongoing maintenance and support documentation.