My Approach
How I Build AI Products
I build AI products that solve real problems, scale reliably, and deliver measurable business value. My approach blends strategic clarity, technical depth, and user-driven execution — shaped by leading mission-critical systems and hands-on AI product development.
These principles guide how I make decisions, prioritize impact, and lead cross-functional AI teams:
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Start with real problems
Use AI Intentionally and know when not to
Design for Trust, Control & Reliability
Ship Fast, Learn, Adapt & Scale
Think Like and Owner
Make Decisions and Align Teams
Measure reality and kill what's not working
Build Enterprise Rigor to AI
I begin with user pain points, workflows, and evidence — not technology. If a simpler solution works (UX, rules, automation), I use it. AI is a lever, not a default.
I apply AI where it creates meaningful lift. If complexity outweighs value, we simplify or stop. Great product leadership is choosing the right solution, not the most impressive one.
AI must be understandable, override-able, and safe. I prioritize explainability, transparency, safeguards, and graceful failure handling — especially in enterprise and mission-critical contexts.
Deliver small, valuable increments, test with real users, and measure behavior. I anticipate scaling challenges early — integration complexity, latency, drift, monitoring, and maintenance burden.
I weigh user value, engineering cost, risk, and ROI. I model TCO (training, inference, data ops, observability) early and focus investment where impact is proven, not assumed.
Engineering, design, and business needs create tension. I build alignment, drive clarity, and move with conviction. Trade-offs are explicit and momentum matters.
I track adoption, usability, reliability, and business impact. When a feature under-delivers, we investigate, iterate — or sunset it. Focus beats sunk cost.
Telecom and cloud systems taught me resiliency, fail-safes, and edge-case thinking. I bring the same operational mindset to AI — building systems that are dependable, observable, and ready for real-world scale.
My belief
Great AI doesn't replace people — it elevates them.
The goal is not automation alone, but enabling better decisions, faster workflows, and expanded human capability.
Currently
Applying these principles across hands-on AI products and continuous experimentation with LLMs, agent systems, and responsible AI — grounded in customer value, reliability, and operational reality.
My AI Product Toolkit
Tools I use to move fast from concept to prototype, evaluate results, and communicate impact.
Prototyping & Low-Code: Bubble.io · Snapdev · Glide · Figma · Notion AI
AI Platforms: OpenAI API · Vertex AI · Claude · LangChain · Hugging Face
Data & Evaluation: SQL · Python · Google Colab · MLflow · PromptLayer
Experimentation & Feedback: A/B Testing · User Research · Miro · Loom · Notion · Jira
Design & Storytelling: Canva · FigJam · Webflow · Wix Studio