Get Done-for-You AI Automation, Built by an Engineer

Small businesses often start with point-and-click tools. They are great for simple tasks, like posting a Typeform submission to a Slack channel. The problem arises when logic gets complex. For example, a tool might offer conditional paths, but these paths cannot merge back together, forcing you to build duplicate branches that double your task count and your bill.

Consider an 8-person law firm processing new client documents. They need to parse a PDF, classify it into one of 14 matter types, and create a record in Clio. An off-the-shelf PDF parser gets the text but fails on complex layouts, misclassifying 20% of documents. The routing logic requires a multi-step conditional path for each matter type, creating a fragile, 50-step workflow that breaks silently.

These platforms are designed for linear, stateless workflows. They cannot maintain state, handle retries gracefully, or manage complex dependencies between steps. When a critical business process like document intake or lead routing fails, there is no log to debug, no version control to roll back, and no way to fix the underlying platform logic.

Syntora would begin an engagement by thoroughly mapping your existing manual workflow. For a document intake system, this involves collaborating with your team to define all relevant document types and collecting a representative sample of example PDFs for each. We've built document processing pipelines using Claude API for financial documents, and the same pattern applies to other industries facing unstructured document challenges. Using this sample, we would prompt Claude's API for classification, iteratively testing model configurations to achieve optimal accuracy for your specific use case. All system logic would be written in Python, leveraging the pydantic library for robust data validation at every step.

The core system would be built as a FastAPI application. This service would expose an endpoint to receive documents, typically triggered by an S3 bucket upload or a webhook from your existing systems. It would then call the Claude API to parse and classify the document content. Depending on your requirements, the system would integrate with your existing tools, using their REST APIs (for example, Clio for case management) to create new entries or update records, meticulously mapping the classified data to the appropriate custom fields. The system would be engineered for efficiency, minimizing processing time while ensuring accuracy.

We would deploy the FastAPI service as a container on AWS Lambda. This architecture typically offers efficient operational costs, often under $50 per month, and scales automatically to handle varying document volumes. Supabase would be used for a PostgreSQL database to log every transaction and its outcome, providing an essential audit trail and aiding in debugging any processing failures.

As part of the engagement, a Vercel-hosted monitoring dashboard would be provided, displaying key metrics such as processing volume and error rates. We would configure alerts in CloudWatch to notify your team via Slack if specific thresholds are exceeded, for example, if the error rate surpasses a defined percentage within an hour. The client would need to provide access to relevant APIs, example documents, and subject matter expertise during the discovery phase. Such a system typically involves a 4-6 week build and integration phase following initial discovery, depending on the complexity of document types and integration points. Following a collaborative monitoring period, Syntora would deliver the full, documented source code in your GitHub repository, a comprehensive runbook, and credentials for all deployed services and monitoring tools.