I build AI-native products and ship working examples that bridge the gap between AI strategy and execution.
Most of my work is hands-on: AI vibe coding, agentic workflows, product strategy teardowns, and real-world implementation of AI evaluation frameworks (RAGAS, DeepEval) and Agent-to-UI (A2UI) protocols.
My hands-on experiments — complete demo builds, agentic UI prototypes, and metrics-driven sandboxes — live at aadhar.build. Private repo, public learnings.
What I work on
- AI-Native Product Strategy: from ideation to Go-To-Market, telemetry, and growth-driving mechanics
- Agentic UI (A2UI): dynamic, agent-driven interfaces bridging chat, filtering, and progressive rendering
- AI Evals & Telemetry: LLM-as-a-judge, production observability, and real-world evaluation metrics
- Hands-on AI Prototypes: clone → install → run demos (like my What-to-Eat app and UI-heavy sandboxes)
- EU Compliance (AI Act & GDPR): bridging cutting-edge LLM features with enterprise risk tiers and regulatory frameworks
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AI Product Leader & Senior PM with 15+ years of experience across global brands (Samsung Research, Airtel Digital, Brevo, LinkedIn), driving outcome-led growth and AI integration.
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Writing about AI Product Management, working culture, and agentic workflows at aadhar.build.
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Operating with a Global, customer-centric, pragmatic, and culture-first perspective. (Currently relocating to Europe / Nordics).
🌱 Building what's next in AI Agents, AI Evals, or Agentic UIs? I'd love to chat and share input. Reach me directly or check out my recent experiments.