Fully Automate Core Business Processes with Custom AI

Many businesses first try an off-the-shelf SaaS tool for document parsing. These tools work well on standard, high-quality PDFs but fail on the exceptions that create most of the manual work. Low-resolution scans, handwritten notes, or non-standard invoice formats cause the parser to fail, forcing employees to review every single document and manually correct the errors.

A regional insurance agency with 6 adjusters processing 200 claims per week faced this issue. A SaaS tool handled their standard forms but failed on 30% of their documents, which included photos and adjuster notes. They were paying over $700 per month for a system that still required full manual oversight, defeating the purpose of automation.

Similarly, generic chatbot builders can answer basic FAQs but cannot access private business data. When a customer asks a valuable question like "What is the status of my order?", the bot can only respond with "Please call our support line." This creates a frustrating customer experience and turns the bot into a glorified search bar, not a functional automated agent.

The engagement would typically start with a discovery phase to audit existing source systems and define precise data extraction requirements. Syntora would connect to source systems using their native APIs, such as a shared email inbox via IMAP or a cloud storage folder using the AWS S3 API. For document pipelines, a collection of sample documents, representative of the full range of quality and formats, would be analyzed to define the exact data fields to be extracted, like 'Policy Number' and 'Date of Loss', often using Python and the Pillow library for initial image analysis.

A FastAPI service would orchestrate the workflow, acting as the core logic for the system. The Claude 3 Sonnet API would be used for data extraction, provided with a structured JSON schema to guide output. This approach allows for consistent extraction of specific fields from unstructured text. With this architecture, the full OCR and data extraction process for a typical two-page document can be completed quickly, often in under 8 seconds. httpx would be used for asynchronous calls to the Claude API, and structlog for machine-readable logging.

This FastAPI application would be containerized with Docker and deployed to AWS Lambda for event-driven processing. New documents arriving in an S3 bucket could automatically trigger the function. The extracted data would be written directly into the client's core system, such as a claims management platform, via its REST API. The infrastructure for such a system can be cost-optimized, often running for under $50 per month for several thousand documents, depending on exact volume and services used.

Syntora would build a simple internal dashboard using Streamlit to show processing history, flag documents with confidence scores below 95%, and allow staff to manually correct exceptions. This feedback loop helps in fine-tuning prompts for the Claude API. The system could also send daily summaries to a Slack channel with processing volume and the number of manual reviews required.