Product Leadership / AI-native Engineering
A hosted-view product for a business-data platform
From a very short request to a working product. Research, definition, UX, build, test, and internal adoption.
Context
The request was short. A new product was needed on top of a widely used business-data platform, and its shape was open.
What it does: it takes data that lives inside the platform and publishes it as hosted pages, so people who have no account there can still see what they need.
I took it from that request to a working product: competitor and market research, use-case discovery, customer and sales feedback, product definition, requirements, UX, technical design, implementation, testing, team coordination, and internal adoption.
Decision
No CSS. No JavaScript. No expertise required.
The easy answer was maximum customisation. The product decision was to prioritise clarity, low friction, and supportability instead. People should get value from it without needing a developer next to them.
Build
AI across the whole loop, not just the code.
AI was used from research through engineering, testing, and review. The point was never that AI wrote code. It was that AI-native development compressed the distance between product and engineering, so one person could hold a wider range of responsibility and iterate faster.
v0.1 was built solo. Once the requirements were clear, tasks went to other engineers, and areas like infrastructure went to the people strongest in them. I kept product direction, UX, review, testing, schedule, and the feedback loop.