Bridge the gap between technical reality and business strategy — fluent with engineering teams, effective in the boardroom, and useful in both.

Hands-on across the full AI stack: classical machine learning, predictive systems, LLMs, retrieval-augmented generation (RAG), and agentic architectures.

Move companies from "we should be doing something with AI" to a clear investment sequence, a coherent product strategy, and assets that compound in value over time.

Identify what companies actually own — turning accumulated data, institutional knowledge, and operational playbooks into structured, defensible product assets before competitors see the same opportunity.

Bring product discipline to AI-native organizations: discovery frameworks, roadmap prioritization, and the operating rhythm that lets teams ship coherently rather than reactively.

Build product teams from scratch — hiring, structuring, and coaching PM organizations for the specific demands of AI, where feedback loops are longer and the wrong hire is costly.

Translate complex technical decisions into clear executive and investor narratives — experienced connecting the engineering whiteboard to the capital raise conversation.

Specialize in AI/ML, AdTech, and SaaS — industries where the product surface is defined by the intelligence underneath it.