I bring 12 years of Big Tech AI execution to the table — not as a consultant, but as a technical co-thinker who has built the systems you're trying to build, at the scale you're trying to reach.
Not generic consulting — this is hands-on, technically deep, and founder-empathetic advisory. I've built personalization systems serving millions, fraud detection platforms protecting global ad ecosystems, and LLM infrastructure replacing multi-million-dollar vendor workflows. I know what production-ready actually means.
I work with a small number of founders and organizations at a time, by design. The value I provide is specific to your situation — your architecture, your data, your regulatory environment, your team. That specificity requires depth, not breadth.
I dive into your AI stack — architecture, data pipelines, inference infrastructure, and team capability. I identify bottlenecks, redundancies, and the two or three decisions that are quietly limiting your scalability. Most founders are surprised how quickly the critical issues become visible.
From FHIR integration to HIPAA-compliant LLM training, RAG on secured legal corpora to privacy-preserving ML pipelines — I help you navigate the regulated industries where AI creates the most durable value, without slowing your build velocity.
Breakthrough thinking, curated resources, and carefully considered introductions that can reshape your trajectory. Having operated at the frontier of AI for 12 years, I know what's real and what's hype — and who's actually building what matters.
"A health-AI founder was 6 months into building a clinical documentation system when I was brought in. Their architecture was transmitting patient data to a public cloud LLM API — a HIPAA exposure they hadn't fully mapped. We redesigned the inference layer around a locally hosted model with a FHIR-compliant retrieval pipeline. They shipped a compliant v1 three months later and closed their seed round with the architecture as a differentiator."
"An early-stage legaltech team had built a contract analysis tool using a general-purpose LLM with poor accuracy on legal language. Rather than fine-tuning from scratch, I advised a RAG-first architecture grounded in their proprietary clause library. Retrieval precision improved dramatically, hallucination on legal citations dropped to near zero, and the product was demo-ready within six weeks."
Whether you're pre-product or scaling Series B, I can help you build the right AI foundation — one that will still be standing when your ambitions catch up to it.