Structured Output

How Do You Trust a Number an LLM Extracted?

A pipeline returns a perfectly-shaped JSON object. Every field present, every type valid, Pydantic green across the board, not a parse error in sight — and the number inside it is …

Your Extraction POC Works on 10 Documents. Here's Why It Breaks on 10,000.

An extraction pipeline has to hold up on the whole corpus, not the slice in the demo — and the demo is the one place that gap stays invisible. The vendor demo ran clean: a dozen …

Incremental Map-Reduce LLM Pipeline with Idempotent State and Prompt-Injection Defense

For the same leadership-development AI platform I built the production infrastructure for (AI Book Companion Platform with Enterprise Security and Observability), I designed and …

AI Game Narrative Extraction Pipeline with Multi-Stage LLM Architecture

For a sports media technology company, I designed and built a production AI pipeline that extracts compelling game narratives from live broadcast transcriptions. The system …

AI-Powered Technical Report Generation with Multi-Source Document Analysis

As technical lead and AI architect for a civil engineering firm, I designed and built an end-to-end technical report generation system combining multi-source document ingestion, …

AI-Powered Personal Fitness Coach: A Proof of Concept

For a fitness technology startup, I developed an AI-powered personal fitness coach that generates fully personalized workout plans through multi-stage conversational interaction. …