From Data to Insights: Building AI-Powered Data APIs
summary
Describes an implemented claims pipeline that ingests hundreds of thousands of PDFs/images, runs OCR then an LLM for extraction and evaluation, and stores outputs in Cosmos DB, cutting claim processing from 2–5 months to about a week and improving throughput by ~60–70%. Architecture uses event-driven APIs, Azure Durable Functions for orchestration, Logic Apps for connectors and Azure OpenAI for summarization and quality checks; governance, cost caps and MLOps practices are required to make it reliable at scale. Azure Durable Functions Cosmos DB Azure OpenAI
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chapters (17)
- 0:00AI-powered data APIs intro
- 1:12Understanding AI APIs
- 6:14Data landscape challenges
- 9:07Solving data problems with APIs and AI
- 12:58Automating data cleaning with AI
- 17:35Why AI APIs are in demand
- 21:14AI API implementation examples
- 22:52Building AI-powered APIs approaches
- 26:26Document processing automation
- 28:33Insurance claims AI processing
- 31:49Modern vs traditional AI solutions
- 32:10Claims processing workflow steps
- 35:28Why AI projects fail to scale
- 37:15API and AI integration best practices
- 40:30Scalability and performance
- 43:20Key takeaways for AI APIs
- 44:42Q&A
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