Expertise
AI Product Management
Showcasing experience building AI-powered products, AI product thinking, AI systems design, and product leadership in AI environments. From copilots and RAG systems to agent architectures and driving true AI adoption.
AI Product Philosophy
- Trust over accuracy: Users will forgive a wrong answer faster than an unpredictable one.
- Human-in-the-loop systems: AI should empower the operator, not replace the context.
- Adoption is more important than demos.
Products Built
Security Insight Decision System
Problem: Fragmented threat intelligence.
Solution: Consolidated AI copilot.
Outcome: Faster data-driven security decisions.
Investora
Problem: Complex mutual fund data.
Solution: RAG-powered financial assistant.
Outcome: Accessible investment insights.
Google MCP Server
Problem: Lack of agent standardization.
Solution: Open-source Model Context Protocol server.
Outcome: Seamless tool use across agents.
Featured Articles
Why 99% Accurate AI Agents Still Lose Users
Why Agent Protocols Will Matter More Than Models
The AI 4D Framework
AI Product Frameworks
- → RAG (Retrieval-Augmented Generation)
- → Agent Design & Multi-Agent Systems
- → MCP (Model Context Protocol)
- → LLM Evaluation Frameworks
What I'm Exploring
- → Agentic Systems
- → Multi-Agent Workflows
- → Enterprise AI Adoption