This team engagement covered three connected AI workstreams. A document-intelligence pipeline used Amazon Textract and Comprehend to extract and normalize structured information, retain source evidence, detect missing or conflicting fields, and route low-confidence results for review. Across 14 document categories, it processed more than 1,800 test pages with 92.6% field accuracy, a median 3.7 seconds per page, and a 78% reduction in manual review time.

A deployment study compared proprietary and open-source diffusion, vision, and language models across output quality, cost, latency, privacy, and compliance. Model loading, resource allocation, repeated inference, and routing changes lowered operating cost 65%, cut GPU time 48%, increased throughput 2.1×, reduced median latency 31%, and produced a 99.1% completion rate.

The retrieval workstream embedded more than 125,000 text segments in PostgreSQL with pgvector, pairing similarity search with metadata filters and real-time conflict checks. It reached 89% precision@5, detected 84% of seeded information conflicts, returned vector results in a median 220 ms, and reduced time spent locating related records by 57%.