When a healthcare technology client came to us needing a pipeline to process documentation across 4,400 Medicare Advantage plans, the timeline they proposed was six weeks. We delivered in 72 hours. This is not a story about cutting corners. It's about what becomes possible when you pair deep architectural judgment with the right AI tooling.
The Problem
Medicare Advantage plan documents are notoriously inconsistent. PDFs with varying layouts, scanned pages, nested tables, and regulatory language that shifts year over year. Extracting structured data at scale from this corpus had previously required armies of contractors or months of engineering time to build fragile rule-based parsers.
The Architecture
We started with a document ingestion layer built on async Python, using a queue-based approach to handle the volume without hammering any single service. Each document was routed through a classification step before extraction — this alone eliminated the need for a monolithic parser that tried to handle every layout variant.
For extraction, we used a combination of vision-capable models for scanned content and text-extraction pipelines for machine-readable PDFs. The key insight was not to treat every document the same. Routing logic made extraction dramatically more reliable and cheaper.
Why It Worked
Three factors compressed the timeline: a clear data contract defined upfront, modular components that could be built and tested in parallel, and AI tooling that handled the long tail of edge cases that would have taken weeks to code manually. The architecture was not complex. It was precise.
The client had production data flowing within 72 hours of kickoff. The pipeline has been running reliably in production ever since.