Integrate Your Business Software with AI Tools via Custom APIs

Many businesses first try general automation platforms to connect their apps to AI. The problem is that these platforms' built-in AI modules are generic. A 'Summarize Text' action often has a 25,000 character limit, which fails on longer documents like legal contracts or detailed project reports. You also cannot control the output format, making it impossible to extract structured data like names, dates, and invoice amounts consistently.

Some SaaS platforms now offer native AI features, like an 'AI Assistant' in a CRM. These are black boxes. You cannot inspect or modify the prompt, control the model version, or add specific business context. When the output is wrong, there are no logs to debug and no way to improve its performance. It is a locked feature, not a true integration that adapts to your unique operational needs.

Consider a regional insurance agency with 6 adjusters handling 200 claims a week. They tried to use a platform's AI feature to summarize PDF claim reports. It failed on any report over 15 pages and could not extract policy numbers correctly. This forced them back to manual review, as the unreliable automation created more work than it saved.

Syntora would begin an engagement by defining the exact job for the AI, working with your subject matter experts to create a target JSON schema. This precise output format ensures the data from the Claude API can be mapped directly to fields in your existing software, minimizing manual data manipulation.

We would then architect and build a FastAPI service in Python to orchestrate the workflow. This service would expose a single, secure endpoint designed to accept incoming documents. It would typically use a library for optical character recognition (OCR) to extract raw text, then make an asynchronous call to the Claude API using httpx with the extracted text and the defined structured data prompt. The entire process, from API request to AI response, would be logged using structlog to a Supabase table for observability and performance monitoring.

The API would be packaged into a container and deployed on a serverless platform like AWS Lambda, offering cost-efficiency and scalability. We would secure the endpoint using API Gateway. Integrating this new API with your existing software would involve updating your application to call the new endpoint, typically a small number of code changes to trigger AI processing when relevant data or documents are uploaded.

Syntora would deliver the complete Python source code to your company's GitHub repository. The deployed system would include monitoring and alerting, such as pre-configured CloudWatch alarms designed to notify your team if the API error rate exceeds a defined threshold or if average processing time goes above expected limits. You would own the delivered code and the infrastructure, avoiding vendor lock-in.