Fortune-level design-build enterprise
Document Intelligence and Ingestion APIs
Built ingestion pipelines and REST APIs that turn semi-structured documents, forms, technical exports, and meeting transcripts into queryable data the AI stack consumes.
- Headline number
- 100s of documents automated
- Role
- Architected and built
- Category
- Data Platforms
- Updated
Architecture
- PDFs and formsTechnical exportsMeeting transcripts
- Blob landing zone
- Extraction pipelineFunctions + Document Intelligence
- Warehousestructured rows + source ref
- REST and MCP APIs
- Apps, GPTs, agents
Key takeaways
- Blob landing zone, custom extraction models, and structured output into the warehouse
- REST and MCP API surfaces so agents and apps consume the same data
- Three meeting-intelligence integration paths evaluated, one productionized
- Monitoring wired in from day one, so a silent extraction failure is impossible
The situation
A large share of the information the business ran on lived in PDFs, forms, technical exports, and meeting recordings. None of it was queryable. Analysts retyped it, and the AI initiatives could only see the structured half of the company.
What I built
An ingestion path on Azure: documents land in Blob storage, a Functions pipeline runs them through Document Intelligence with custom extraction models, and the structured output lands in the warehouse with its source reference attached. For meeting intelligence I evaluated webhook, API, and MCP approaches, built a sanitized payload library to test against, and designed the production path for transcripts, metadata, and action items.
On top of the data I exposed REST and MCP surfaces so the same extracted fields serve applications, custom GPTs, and agents without three separate integrations.
The outcome
Hundreds of documents a month flow in without a human in the loop, and the unstructured half of the business became queryable. The meeting pipeline turned recordings into action items that agents can act on.
Stack
Related work
Same discipline, different system.
Need this built inside your organization?
One person, architecture to deployment. Tell me what is stuck and I will tell you where I would start.