Drive product and ML roadmap for agentic SaaS platform orchestrating enterprise case file summarization, knowledge surfacing, and adjudication with human-in-the-loop reliability, delivering material reductions in review time and cost.

Limited partner and angel investor in PE, VC, and B2B AI SaaS companies, operating hands-on as a fractional product and AI leader to founders and investment teams by setting product direction, shaping AI and data strategy, and driving enterprise GTM, while building data-driven investment infrastructure that strengthens sourcing and diligence, accelerates portfolio value creation, and makes investment operations more efficient, effective and repeatable.

Head of Product & Innovation at a $6B+ AUM growth-stage B2B AI venture capital fund, led the firm-wide AI agenda across sourcing, diligence, investment thesis development, and portfolio acceleration. Built data-driven investment and diligence products and infrastructure, launched the firm’s innovation program, and codified repeatable AI value-creation playbooks spanning sales acceleration, workflow automation, and trust and governance. Led programs with portfolio product leaders to translate AI readiness frameworks into post-close roadmaps tied to commercial KPIs and board-level operating cadence, and helped scale internal product, engineering, and data capabilities while supporting technical diligence and portfolio initiatives across regulated domains.

Led a 20+ person engineering and ML organization and owned the technical roadmap for an adaptive language intelligence platform. Built evaluation and guardrail infrastructure for human-in-the-loop NLP, including data and label observability across annotation workflows, quality controls, and systematic detection and remediation of training data issues. Shipped enterprise production systems for classification and risk scoring with measurable impact for Fortune 500 customers, including step-change improvements in customer-service routing and stronger compliance monitoring. Inventor on patents spanning human-in-the-loop ML, corpus construction, data observability, and NLP architectures that enabled reliable, repeatable AI workflows at scale.

Oversaw post-acquisition integration and scaling of AI assets from a legal tech startup into EY’s global Forensic Analytics platform, establishing a repeatable foundation for new products and services across investigations, compliance, and eDiscovery. Led cross-functional teams of forensic experts and ML engineers to build and harden production NLP and ML systems for high-stakes matters including financial fraud, insider trading, MNPI leakage, FCPA, and insider threat. Architected end-to-end workflows over large-scale unstructured data using semantic search, entity normalization, graph-based link analysis, and supervised classification, and scaled predictive coding and document classification across hundreds of millions of pages. Delivered human-in-the-loop review pipelines and defensible reporting that improved auditability and materially reduced cycle time and cost through evaluation-driven reliability controls and iterative improvement loops in regulated enterprise environments.