Building trusted AI products, ready for real life.
I lead AI products from discovery to scale, combining product fundamentals with practical experience in assistants, agents, tool integration, enterprise controls, and human-AI collaboration.
Product leadership, AI experience, and hands-on building.
AI Product Leadership
Turning AI opportunities into clear product direction by defining the user need, business case, success metrics, roadmap priorities, and operating model required to move from idea to execution.
Human-in-the-Loop Design
Designing clear control points between AI and people, including when the system can act independently, when it should recommend, and when human review, approval, or accountability is required. This keeps AI workflows useful, trusted, and safe for real enterprise environments.
Hands-On Builder
I turn AI product ideas into working prototypes and production-ready patterns using tools like Claude Code, Figma MCP, and agentic workflows. By building directly, I can validate ideas faster, uncover product and technical risks earlier, and reduce the gap between strategy, design, and execution.
What AI product management includes
More than models and chat interfaces.
AI product management is about turning intelligence into useful, trusted, and scalable product experiences. Strong AI products need a clear customer problem, a valuable workflow, the right context and data, safe actions, measurable quality, and clear boundaries between automation and human judgment.
I bring a hands-on builder mindset to this work. I use Claude Code to build working product prototypes, explore product patterns, and move quickly from idea to interaction.
This includes building MCP servers that expose tools, APIs, and enterprise capabilities to AI assistants, IDEs, and agentic systems. This is becoming essential for organizations that want AI to safely interact with real business systems.
It also includes designing skills for AI assistants, shaping agents that can use tools and complete multi-step tasks, and creating web interfaces that expose product capabilities through WebMCP.
I also use Figma MCP and code-to-design workflows with Claude Code to move faster between design, prototype, and implementation. This makes AI product ideas tangible earlier, helps teams validate real interactions, and exposes product, experience, and technical risks before deeper investment.
AI tools I use to build, not just evaluate.
Tools I use to turn ideas into working products.
AI-assisted development for prototyping, shipping production code, and validating product ideas, moving from strategy to working software in the same session.
MCP and WebMCP patterns for building agent-ready products that can safely expose enterprise tools and capabilities.
Agent-User Interaction protocol for connecting AI agents to user-facing applications, enabling real-time updates, shared state, and human-in-the-loop product experiences.
Multi-step AI workflows that connect research, planning, prototyping, and validation, helping product teams move faster from insight to execution.
Design-to-code workflows via Figma MCP, connecting design system components and product screens to AI-assisted implementation.
Workflow automation for connecting tools, triggering actions, routing data, and building repeatable AI-assisted processes across product and business systems.
AI that simplifies complexity.
The best AI products do not feel like technology added on top of a product. They feel like a better way to get work done.
My focus is on designing AI experiences that help people understand complex systems, take action with confidence, and move through workflows with less friction. That requires product judgment, enterprise discipline, strong interaction design, and enough technical fluency to know what is possible.