Custom AI Automation for Your 5-50 Person Business

Many businesses start with visual, task-based automation platforms. They connect common apps quickly, but the cost model is punishing. A workflow that reads an email, saves an attachment, extracts data, and updates a CRM burns 4 tasks. At 200 emails a day, that is 800 tasks, driving the monthly bill into hundreds of dollars for a single process.

A regional logistics company with 25 employees faced this issue. Their dispatch workflow needed to check inventory in their ERP and customer status in their CRM before sending an SMS. In a visual builder, this required two separate conditional branches that could not merge. This forced them to duplicate the final SMS step, doubling task consumption and creating a maintenance headache whenever the message template changed.

The alternative, hiring a full-time AI engineer, costs over $150,000 per year and is overkill for building one or two core systems. Large consultancies will take the project, but they assign junior developers managed by a non-technical project manager. You pay for overhead, not for senior engineering talent focused exclusively on your build.

Syntora's approach to AI automation begins with a detailed discovery phase to understand your current manual workflows. We would map these workflows into a state machine representation, identifying each step that could be automated by a Python function. For a document processing pipeline, this typically involves defining how documents are received (e.g., from an S3 bucket or email inbox) and how they flow through extraction and validation stages.

The technical architecture for such a system would involve several key components. Optical character recognition (OCR) would be integrated as needed to extract raw text from documents. This text would then be passed to a large language model API, such as the Claude API, using a carefully engineered prompt designed for structured data extraction, often in JSON format. Syntora has built similar document processing pipelines using Claude API for financial documents, and the same pattern applies to other industry documents requiring data extraction. This initial data capture step would be instrumented with structlog for detailed, JSON-formatted logs to ensure transparency and debuggability.

The core logic would be orchestrated within a FastAPI application. httpx would handle asynchronous calls to external services like the Claude API, allowing the system to maintain low processing latencies. Data validation would be managed by Pydantic models, which are crucial for catching malformed output from the AI and routing it to an exception queue for manual review. This queue would typically be managed within a Supabase Postgres database.

For deployment, the system would be designed for serverless environments like AWS Lambda, which optimizes operational costs by only charging for compute time used. A workflow processing thousands of documents monthly often incurs low double-digit monthly AWS fees. A Supabase Postgres database would log every transaction, store processed results, and manage the exception queue, providing a full audit trail. The entire infrastructure would be defined as code to enable repeatable deployments and maintain consistency.

Integration with your existing software is a critical final step. The system would expose secure API endpoints to push extracted and validated data directly into your CRM, ERP, or other industry-specific platforms. A typical build for a system of this complexity and scope usually takes between 4 to 8 weeks, depending on the number of document types and integration points. Syntora would deliver the complete Python source code to your private GitHub repository, ensuring you have full ownership and control over the custom-built system, equivalent to what an in-house engineer would provide. The client would need to provide access to example documents, define structured data requirements, and specify integration targets during the discovery phase.