Custom Claude AI Workflows

Many teams start by trying to connect their apps with a visual workflow builder. A 15-person logistics company might try to parse shipping manifests from PDF email attachments and load them into Google Sheets. The tool's built-in parser often misreads table columns, mixing up tracking numbers and destination addresses. A workflow to handle just three different manifest formats requires nested conditional paths that quickly burn through a 5,000 task/month limit.

Next, they try a dedicated document parsing service. These tools work well for a single, fixed template but require manual retraining for each new client's manifest format. The UI-based training is slow, taking 2 hours per template, and it fails completely on documents with handwritten notes or stamps. The error rate for manifests with any variation exceeds 30%, forcing a full manual review.

These platforms are designed for simple, linear triggers. They cannot handle multi-step data validation, retry failed API calls with exponential backoff, or adapt to structural variations in documents. The workflows are brittle, fail silently, and create more review work than they save.

Syntora would begin an engagement by auditing your existing operational workflows and data sources to define precise automation requirements. For a document processing workflow, the system would connect to your relevant data storage, such as an email server or file repository. An AWS Lambda function would then be configured to trigger upon the arrival of new documents containing attachments, or on a scheduled basis. We would use a library like pdfplumber to extract raw text and structured data from documents like PDF manifests.

The extracted text would be sent to the Claude 3 Sonnet API through a custom FastAPI service. We have built document processing pipelines using Claude API for various applications, including financial document analysis, and the same fundamental pattern applies here. A specific system prompt would be engineered to instruct Claude to parse the manifest content into a structured JSON object, identifying defined fields. Pydantic would be used for strict validation of the returned JSON structure, ensuring data consistency and integrity.

Once the data is validated, the FastAPI service would write it to a designated table in your existing database, such as Supabase PostgreSQL. This step would use asynchronous database writes for efficiency. Subsequent actions, such as calling an external API like ShipStation for label creation, would also be handled by functions using asynchronous requests with built-in retry logic.

We would deploy the workflow on cloud platforms like Vercel and AWS Lambda, choosing the best fit for your existing infrastructure. Structured logging would be implemented using tools like structlog, with events sent to a monitoring system like Axiom for real-time visibility. An alerting system would be configured, for example, sending a Slack notification with the failed document attached if data validation consistently fails.

Building a system of this complexity typically requires 6-12 weeks, depending on the number of document types, data volume, and external integrations. The client would need to provide access to relevant systems (e.g., email servers, databases, APIs) and define the output structure. Deliverables would include the deployed, production-ready system, source code, documentation, and a plan for ongoing maintenance and support.