Outamation (Extern)

Shipped a production RAG system for financial document intelligence with 95% extraction accuracy and sub-60s SLA.

Overview

Engaged as AI Engineer & Technical Analytics Consultant for Outamation through Extern, working on high-stakes financial document intelligence for loan and mortgage underwriting workflows. The mandate was to deliver a production-grade autonomous extraction system that would hold up against real enterprise SLAs.

The Challenge

Mortgage underwriting documents are structurally brutal — multi-column layouts, inconsistent formatting, dense numerical data, and significant cost if extraction fails. The system needed to pair high accuracy with sub-60-second response times to meet enterprise SLAs, and the foundation model choice had to be defensible to client leadership on both performance and cost grounds.

The Solution

I deployed a production RAG system delivering 95% autonomous extraction accuracy with sub-60-second response times against enterprise SLAs. To justify the architecture, I benchmarked 4 competing LLM/ML approaches against the financial document corpus, selecting the optimal foundation model (95% confidence on complex multi-column layouts) and presenting tradeoffs, ROI, and deployment recommendations to client leadership. I built and monitored streaming data pipelines for real-time document processing and semantic search over vector indexes, investigated edge-case model behavior as it surfaced, and shipped agile logic changes to keep SLA health green.

My Thoughts

This was my first production RAG deployment under a hard enterprise SLA, and it crystallized something for me: retrieval quality and prompt discipline matter more than raw model horsepower. The benchmarking exercise — four approaches, one picked — was the most instructive part, because the decision had to survive both technical scrutiny and ROI questions from client leadership.

Key Achievements

• Delivered production RAG system with 95% extraction accuracy and sub-60s response times
• Benchmarked 4 LLM/ML approaches and selected optimal foundation model for complex layouts
• Built streaming data pipelines for real-time document processing and semantic vector search
• Presented model tradeoffs, ROI, and deployment recommendations to client leadership
• Shipped agile logic changes to maintain SLA health as production edge cases emerged