A point of view
The hard part of AI isn't the model. It's everything around it.
I architect the systems that make generative & agentic AI trustworthy in production — the evaluation harnesses, golden datasets, and guardrails that tell you when the model is wrong before your customers do. Fourteen years, five companies, tens of millions of users.
25M+
Monthly users served
4M+
AI datapoints evaluated / yr
14+
Years shipping at scale
1
Granted patent
How I think about building AI
01 — 0401
Models are commodities. Judgment is not.
The advantage was never the LLM you picked. It's the evaluation harness, the golden datasets, and the guardrails that tell you — reliably, daily — when the system is wrong.
02
Ground it in production reality.
RAG and agents earn trust only when they're measured against real customer utterances — not tidy benchmarks. The eval set is the product spec.
03
Make the black box explainable.
Per-utterance MRR, mAP and Hit-Rate, surfaced so a non-ML stakeholder can debug a bad score themselves. Trust scales when understanding does.
04
Automate the boring parts.
A good agentic workflow closes the vulnerability ticket and opens the PR for review — freeing humans for the ambiguous, high-judgment work only they can do.
Selected work
All case studies →- 01
GenAI Regression Evaluation
A golden dataset grounded in real customer utterances, automated daily runs, explainable retrieval metrics.
Intuit · 2026 - 02
Reinventing the Power Platform
Generative & agentic AI for Power Apps and Power Pages at massive scale.
Microsoft · 2024–25 - 03
Data Protection as a Service
Privacy-by-design microservice on a cloud platform — the work behind my granted patent.
SAP · Patent - 04
Modernizing Enterprise Manager
A 30-year-old product with 10,000+ live customers, brought onto current technologies without breaking anyone.
Oracle · 2022–24
Let's build
Hiring for AI, platform, or cloud at scale?
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