Customer Product Manager · Zuper
Dilith Dinesh
Three clicks. You're either very bored or very thorough. Either way — hello.
Product operator at the boundary between customer reality and product systems.
I translate what breaks in the field into capabilities that scale — and build AI systems to show how I think about them.
Most product teams learn about customer problems through tickets, NPS scores, and quarterly reviews.
I've spent two years inside the operational layer — sitting with contractors, mapping how they actually collect money, watching where workflows break before anyone files a bug report.
That proximity changes how I think about product decisions. Not every problem needs a feature. Not every feature request is the actual problem. The real work is finding the gap between what people say they want and what would actually help them.
That's the kind of product thinking I'm building toward.
Stack
The AI PM toolkit.
Models, automation platforms, and product tools I reach for every week.
- Claude
- GPT-4
- Figma
- Notion
- Linear
- Supabase
- n8n
- ElevenLabs
- Deepgram
- Postman
- VAPI
- Lovable
Work
Not success stories.
Decision logs.
Writing
Teardowns, field notes,
and frameworks.
Rapido nailed the habit loop. But they're still optimizing for the ideal user — not the actual one.
Read →
Most imitators copy the format. They miss the emotional architecture underneath.
Read →
Projects
Personal friction,
built into tools.
Iris
I work across four devices. Every day I need to move something between them — a screenshot, a file, a link. Every existing solution added friction I didn't want. So I built one.
SuperPilot
B2B sales reps lose 2 hours a day across 6 deal handoffs. The data to fix it exists. I built the AI layer that synthesizes it into memory.
The Voice Agent
A contact form would tell you I'm interested in AI product work. A voice agent shows you how I think about it. So I built one — and documented every decision behind it.