Build Internal AI Tools Your Team Will Actually Use

Many teams start by using public tools like ChatGPT Plus for summarization. Copying and pasting sensitive client data (names, policy numbers, accident details) into a third-party LLM is a major security risk and violates data privacy agreements. There is no audit trail, no access control, and the data can be used for OpenAI's model training.

Trying to connect an internal system to the OpenAI API via a no-code platform introduces a different problem: cost. These platforms charge per task or API call. A workflow that parses a PDF, sends text chunks to an LLM, and combines the summary can burn 10 tasks per document. For an agency processing 200 claims per week, that is over 8,000 tasks per month, pushing them into a high-cost plan for a single process.

These approaches treat AI as a disconnected step. Real efficiency comes from integrating it directly into the primary workflow. An adjuster needs the summary inside their claims management system, not in a separate browser tab. Off-the-shelf connectors cannot provide this level of embedded, secure integration.

Syntora's approach begins with a comprehensive discovery phase, auditing your current data sources and workflows. We would start by mapping your existing data, typically residing in a PostgreSQL database or document stores like AWS S3. Using Python and libraries such as boto3 for AWS, we would define strict schemas with tools like pydantic to ensure accurate parsing of critical entities like claim numbers, policy IDs, or dates. This initial data mapping and architecture design phase typically takes about 4 days, resulting in a clear blueprint for development.

The core of the system would be built around a FastAPI service. For document processing, when a new file is uploaded, a Python function would extract its text. We've built document processing pipelines using Claude API for sensitive financial documents, and the same robust pattern applies to other industry documents, where the Claude API is carefully prompted to extract specific entities such as claimant names, incident dates, and policy numbers. This ensures high accuracy and consistency in data extraction. The system would be engineered for rapid processing, aiming for significant time reductions compared to manual methods.

We would design and build a secure web interface, potentially using Vercel, to display processed documents alongside their structured summaries. User authentication would integrate with your existing company credentials through role-based access control, which we'd manage with tools like Supabase. The FastAPI backend would be deployed as a serverless function on AWS Lambda, providing cost-effective operation that automatically scales with demand, ensuring high availability and efficient resource use without over-provisioning.

The delivered system would integrate directly with your primary claims management or operational software via its API, allowing users to leverage the AI insights without leaving their main tools. Syntora would implement structured logging with solutions like structlog and CloudWatch to ensure comprehensive monitoring. We would configure alerts for critical performance metrics, such as Claude API latency or error rates, enabling proactive identification and resolution of any issues. This ensures the system remains reliable and performs as expected.