10+ years taking products from 0→1 and scaling them across SaaS, AdTech, FinTech, and AI platforms. Built Google's AdWords sales platform to $40B in managed revenue. Launched AI systems that generated $4B in incremental revenue. Recently shipping hands-on AI products with LLMs, RAG, and OCR.
AI platform product leader with 10+ years building and scaling SaaS, AdTech, FinTech, and data platforms — with recent hands-on work shipping AI products including LLM tooling, RAG pipelines, OCR, and AI inference with human-in-the-loop workflows.
Known for taking products from 0→1 and scaling them across large, cross-functional organizations. I've built platforms used by tens of thousands of people, launched AI systems that moved billions in revenue, and co-founded two companies — one of which reached $12M ARR with 23 patents.
Built Google's SMB AdWords CRM and sales platform from scratch — no prior internal tooling existed at this scale. Rolled it out across 24 sales programs in three regions and onboarded 40,000 sellers without centralized support escalation, achieving the highest CSAT ever recorded for an internal Google product.
Launched an AI/ML-powered sales system that shifted optimization from clicks to business outcomes — a fundamental rethinking of how Google's sales motion worked. The platform identified high-value opportunities, guided seller actions, and drove measurable incremental revenue at scale.
Launched a petabyte-scale cloud data platform on Snowflake, consolidating legacy databases and enabling external data sharing. Unified 200+ APIs across the product and shipped an ML-powered anomaly detection and trend-surfacing system — doubling feature adoption by reducing false positives.
Built an API-first enterprise data platform used by 250 engineers, consolidating authentication, customer data, and lead management across six business units onto a single platform. Created a unified customer view that enabled intelligent lead routing, eliminated data silos, and cut loan approval time by 25%.
An open-source tool that automatically evaluates and compares RAG chunking strategies so you can find the optimal one for your documents — without the trial-and-error. Upload a document, run a benchmark, and see which chunking approach gives your retrieval pipeline the best results.
I'm always open to interesting conversations — whether that's a new role, a product challenge to think through, or just trading notes on what's happening in AI.