Stop Paying Per Task. Own Your AI Workflow.

Small businesses often hit a wall with generic tools. An internal AI tool built on a platform's app builder might work initially, but it cannot handle production workloads. These tools often have opaque rate limits, and a single API call can take 2-3 seconds, making real-time use impossible. They lack granular error logging, so when a workflow fails, you only know that it broke, not why.

A common scenario is a regional insurance agency trying to automate claims processing. They use a SaaS document AI tool that costs $600/month for 3 adjusters. The tool extracts standard fields but fails on 20% of their custom forms, forcing manual review. It cannot connect to their industry-specific claims management system, so adjusters spend an hour each day copying and pasting data between windows.

This approach fails because it treats a core business process like a generic task. The SaaS tool is a black box you cannot modify, the per-seat pricing penalizes growth, and the lack of deep integration means your team is still stuck with manual work. You are paying a premium for a partial solution that does not solve the last-mile problem.

Syntora begins an engagement by thoroughly mapping your existing manual workflow. This involves identifying every data source, decision point, and downstream system to be integrated. For document processing, this means auditing how documents are received and what information needs to be extracted for your operational platforms.

The core of such a system would be a Python application built with FastAPI. It would expose a secure API endpoint designed to receive raw documents. We often use httpx to make asynchronous calls to the Claude API for data extraction and then process and validate the API's response. Syntora has extensive experience designing specific prompts for the Claude API to extract precise fields from various document types, achieving high accuracy in similar data extraction tasks for financial and legal clients. Every processing step would be logged with structlog, providing a searchable history of documents.

Syntora would containerize the FastAPI application for deployment on a serverless platform like AWS Lambda. This architecture helps manage infrastructure costs by only incurring charges when the system is actively processing, potentially keeping costs low for typical small business volumes. Processed data would be stored in a Supabase Postgres database, and Syntora would build a simple dashboard within Supabase for reviewing any exceptions.

Finally, Syntora would integrate the extracted, structured data directly into your existing systems. This typically involves webhooks or direct API calls to update records in your operational platforms. Upon completion of the engagement, the full source code would be delivered to your GitHub repository, accompanied by a runbook detailing deployment, monitoring, and operational procedures.