Build Your Custom AI Automation Workflow

Many businesses try to automate with off-the-shelf tools, but hit a wall with business-critical workflows. A platform like Airtable Automations is great for simple triggers within its own database. But it fails when you need to process an unstructured PDF attachment or call a non-standard industry API that requires OAuth2 authentication. The tool simply lacks the required function.

A common scenario is a 6-person insurance agency processing 200 claims per week from PDF forms. They might try a generic parsing tool, but its OCR engine consistently fails on handwritten notes and complex table layouts, causing a 15% error rate that requires manual review. They might try a visual workflow builder, but the cost model breaks them. A single claim might require checking the policy, assigning an adjuster, and creating a calendar event, burning 3 tasks. At 200 claims/week, that is 2,400 tasks a month, pushing them into an expensive tier for one broken workflow.

These tools are designed for connecting well-known SaaS apps with simple data structures. They are not built for the messy, specific, high-stakes processes that actually run your business. They force you to simplify your process to fit the tool, instead of building a tool that fits your exact process.

Syntora would begin by mapping your exact manual workflow. We would use 50-100 real-world examples, such as PDF invoices or support emails, to establish a thorough test set. The initial step involves writing a Python script, often using `pdfplumber` for text extraction and the Claude API for structured data generation. This process converts unstructured documents into clean JSON that a computer can read. Our aim is to achieve high accuracy, targeting over 99% for critical fields like invoice numbers and client names. We have successfully applied this pattern in document processing pipelines using Claude API for financial documents, demonstrating its effectiveness for structured data extraction.

The core logic would be built as a FastAPI service. This Python application would contain all the specific business rules that visual workflow builders cannot handle. For instance, such a service could take extracted claim data, call a CRM's custom API using `httpx` to validate a policy, and query a Supabase table to find a suitable resource. The system would be designed for quick execution, with typical decision processes completing in milliseconds.

Syntora would deploy the FastAPI service on AWS Lambda, where it can be triggered by new files. This serverless architecture is designed to be cost-effective and scalable. For example, an application handling a moderate volume might incur AWS costs under $20 per month. We would configure `structlog` for structured logging. This ensures that if an error occurs, the exact input data and API response that caused the failure are recorded, allowing for rapid issue identification and resolution.

Finally, Syntora would integrate the output into your existing systems. The service would push processed data, assignments, and a link to the original document directly into your main management platform via its REST API. Your team would see results within their familiar tools, requiring no new software to learn.