My work combines a designer’s eye for behavior and product experience, a GM’s attention to GTM and unit economics, and a builder’s bias toward action under ambiguity.
See my latest AI prototype →By the numbers — 13+ years in product · $500M+ cumulative product and venture revenue · products shipped across US, EU, and APAC · teams of 8–40 across PM, design, engineering, data, and GTM · 4 ventures incubated 0→1
Palo Alto Networks · BCG Digital Ventures · HP · ADP · Bunnings · PepsiCo · Blue Cross Blue Shield · SC Johnson · Polycom
Palo Alto Networks had world-class enterprise security, but no obvious consumer category to enter. Okyo Garde was a 0→1 bet to turn enterprise-grade protection into a household product — built inside a company learning B2C for the first time.
Reframed a stalled B2B SaaS sales motion at ADP into a commercialization pivot — turning a complex, sales-dependent HR product into a digital self-serve experience SMBs could evaluate and buy on their own.
Bunnings wanted to extend its trade ecosystem into home services. Contractor workflow SaaS — not demand aggregation — became the stronger and more scalable wedge.
HP saw a higher-margin growth opportunity beyond hardware: SMB IT services. The challenge was turning messy break/fix support into a trusted digital service product small businesses could understand, buy, and rely on.
When the customer problem is real but the product hasn't found its shape, I figure out what to actually build. Sometimes that's a SaaS workflow, sometimes a marketplace, sometimes a productized service, sometimes none of the above. The work is in not assuming the answer.
I trained as a designer before I became a PM. That shows up as observing users in their real context, building around behavior rather than features, and treating craft as a strategic asset, not a downstream execution detail.
From cybersecurity to PEO benefits to SMB IT services, I work in high-trust, high-consideration categories where adoption depends on whether users can understand and believe the product before they fully use it.
I think in unit economics, pricing, and GTM motion alongside product specs. That's how 0→1 work actually ships, scales, and compounds revenue — rather than getting stuck as a proof of concept.
Technical feasibility is almost never the assumption most likely to kill adoption. Whether the user who matters most actually has the problem you think they have — usually is. I design the first weeks of any new product around stress-testing that assumption, not building the most demoable version.
In complex categories — cybersecurity, AI, B2B platforms — a product can work perfectly and still feel like nothing is happening. The product job isn't done until value is legible: visible enough to trust, specific enough to recommend, real enough to renew.
The most important design decisions aren't about color or polish. They're structural: what appears first, what's hidden, what's the default, what the system implies about how users should behave. These choices shape behavior before anyone reads a single word — and they're product strategy, not styling.
In early products, love is a more reliable signal than completeness. It tells you whether you've found something worth scaling — or just something that technically works. Minimum lovable, not minimum viable.
I build small AI prototypes to develop sharper product intuition — about where intelligence belongs in a workflow, how much autonomy a system should have, and what trust cues make AI products actually adopted instead of ignored.
Problem. Applying well to job roles is a research, judgment, and writing workflow. Each role requires reading the JD, assessing fit, researching company priorities, mapping the opportunity to past experience, and tailoring the resume — often 2–3 hours per role for a busy professional exploring multiple opportunities.
Product bet. The opportunity was not a smarter job board or AI resume rewriter. It was a multi-agent application pipeline where specialized agents screen fit, research company strategy, retrieve relevant experience, draft the resume, and evaluate it cold like a recruiter — reducing prep from hours to minutes while preserving human judgment and claim fidelity.
Built. Built in ~40 hours using Claude Code, Anthropic SDK, ChromaDB, Airtable, Pandoc, and Vercel. The system uses approved resume and story-bank inputs only, routes work across models, stores each role in Airtable, and gates every tailored resume through a six-criteria evaluator before sending a curated digest.
Problem. Preparing for the day as a PM meant checking too many disconnected sources — news, blogs, competitor updates, email, Slack, and calendar — then manually deciding what mattered and how it connected to my weekly goals, personal priorities, and professional learning.
Product bet. The opportunity was not another summary tool or feed aggregator. It was a “so what” layer: an agent that could connect external signals, personal context, and daily commitments into a brief that explains what matters, why it matters, and how to make time for it.
Built. Built with Claude Code and run locally through LM Studio using Hugging Face / Qwen 9B.
Problem. Busy households cycle through the same few meals. Finding something new means hopping across Google, YouTube, Instagram, and ChatGPT — and none of them hold taste, mood, and constraints in one place.
Product bet. Search and video help once users know what to cook; chat can suggest ideas but is not visual, persistent, or personal enough to plan around. The opportunity is not a better recommender — it is collapsing recommendations, recipes, images, and video into one flow, so the idea and the means to act on it live together.
Built. Live app in ~20 hours using Cursor, Supabase, YouTube, Pixabay APIs, and Vercel.
I write about product leadership and AI product building — particularly the parts the model-centric conversation misses: autonomy calibration, trust signals, stakeholder influence, and why technically accurate AI products still get ignored.
Based in Los Angeles. Open to Principal PM roles in large tech and Head of Product or Founding PM roles in startups.
Consumer · SaaS · Marketplaces · AI 0→1 · Onboarding · Monetization · Behavioral Product Design Master of Design, IIT Institute of Design
I came into product through a slightly unusual path.
I grew up in India, studied engineering, and found my way into design after a paragliding accident. Design gave me a sharper way to observe people and understand how products fit into their lives. But I was quickly drawn beyond the object or interface to a larger question: what makes something worth building, viable as a business, and adopted in the real world? That question pulled me into product management.
Since then, I've worked across a strange and useful range — from potato chips to consumer apps, cybersecurity, SMB services, marketplaces, and AI-enabled workflows. Different categories but similar user challenges. What I care about most is building products that some people genuinely love, and teams that feel braver for having shipped them.
Outside of work I build furniture, ride motorcycles, and am slowly learning to make shoes.
Making things outside software has kept my product instincts honest. Each of these influences how I think about real product work.
I build furniture and shop infrastructure for my own garage — most recently a mobile tool cart that solved my own workflow friction. Working in wood teaches a different kind of product discipline: you can't hide behind abstraction. Sequencing, tolerance, ergonomics, and the actual repeated use of the object all show up immediately.
I'm slowly learning the craft of making shoes — a category where function, fit, identity, comfort, and feeling all sit on top of each other in a single object. It's a reminder that great products don't ask users to contort themselves into the product; they adapt to the human.
Motorcycles make systems thinking physical. Power, weight, friction, feedback, timing, and control all have to work together — and small changes can alter the whole experience. They also remind me that products are never just functional. The best ones work across physical, cognitive, emotional, and cultural layers at the same time.
A space held open for the next thing.