Executive Summary
Founded The AI Pit Stop and built a repeatable operating system, APOS, for taking applied AI products from opportunity to production — shipping the AI Readiness Diagnostic, a Customer Support Agent, Fantartistic, and VectorPM's Context Debt product under one evaluation and governance framework.
Business Problem
Most GenAI ideas stall between prototype and production. Without a repeatable path from opportunity to a shipped, evaluated agentic product, teams either over-invest in demos that never ship or ship without the evaluation and governance a production AI product needs.
Why It Mattered
The gap between an impressive demo and a trustworthy production product is where most applied AI initiatives fail. Closing it required an operating system, not just individual product decisions.
My Role and Scope
Founder & AI Product Lead — own product strategy and hands-on development across the full portfolio, from discovery and workflow design through prototyping, evaluation, and production.
Constraints
Building and validating multiple products at once as a small operation, with the added discipline of applying consistent evaluation standards — not just shipping fast — across every product.
Decisions I Made
- Created APOS, a repeatable framework for identifying valuable AI opportunities, designing coordinated agent workflows, managing context, and introducing evaluation and governance checkpoints, and applied it consistently across every product rather than treating each launch as a one-off.
- Defined evaluation and governance using golden datasets, HHH criteria, LLM-as-judge scoring, confidence thresholds, and human-in-the-loop review as a standard, not an afterthought.
How the Operating System Worked
Every product — the AI Readiness Diagnostic, a Customer Support Agent, Fantartistic, and VectorPM's Context Debt product — moved through the same discovery, prototyping, evaluation, and launch process under APOS, with GPT, Claude, and Gemini benchmarked for output quality, reliability, latency, and cost.
Cross-Functional Leadership
Works directly with the small team and partners supporting each product, translating evaluation results and governance decisions into concrete build and launch calls.
Outcome and Evidence
- Four applied AI products shipped to production under one evaluation framework: golden datasets, HHH criteria, LLM-as-judge scoring, and confidence thresholds.
- GPT, Claude, and Gemini benchmarked for quality, reliability, and cost.
What I Learned
A repeatable operating system — not one-off prototypes — is what gets applied AI from idea to production, and the evaluation framework is what makes "production" a credible claim rather than a marketing one.
Related Capabilities
- Agentic workflows
- AI evaluation
- RAG
- Prompt architecture
- Production readiness
- Applied AI strategy
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