KAVIA AI
software lifecycle
A blank page to an enterprise-ready AI coding platform, with the design system behind it. The team raised $1M pre-seed on the result.
Three critical unknowns
No precedent to follow. A new category of AI tooling with no established patterns, only the expectations developers already bring from other dev tools. Nothing to copy, so every decision had to earn its place.
Explaining agent work. Agents act on their own, and that progress is hard to make legible. Trust breaks the moment output feels like a black box, and developers will not hand over real work to something they cannot inspect.
Enterprise readiness. Pilot buyers expect maturity: reviews, permissions, audit trails, and consistency across a surface shipping weekly. The product had to look established before it was.
My role & process
I designed KAVIA AI end to end, from the first sketch to an enterprise-ready platform, working with a product manager, engineering, and QA.
- 01
Discover
Developer interviews, pilot feedback sessions, and competitor benchmarking to learn what developers expect from their tools, and where autonomy starts to feel like risk.
- 02
Define
The research pointed at one goal: make agent work legible. Developers trust what they can inspect, so every screen had to show the work, not just the result.
- 03
Design
Concept prototypes for the agent workspace, tested with developers, then built out alongside a shared component set so the system grew with the product.
- 04
Validate & ship
Usability testing and pilot feedback fed back into weekly releases, across 90+ enterprise pilots.
Goals & research
Make autonomous agent work legible to developers. Design for trust by showing the work, not just the result. Get to enterprise-ready without slowing the team down, and keep the product looking consistent while shipping weekly.
Developer interviews, pilot feedback sessions, competitor benchmarking, and usability testing all pointed the same direction: developers trust what they can inspect, autonomy without visibility reads as risk, and investors read polish as evidence of maturity.
Strategy into interface
Three principles carried the whole surface: reduce the thinking required, make outcomes predictable, and unify the visual language so a fast-shipping team never had to design the same screen twice.
Key decisions
- 01
Show the work, not just the result
Every agent action explains what it did, why, what it touched, and what to check before it merges. Research was clear: autonomy without visibility reads as risk, and developers will not hand real work to a black box.
- 02
Familiar mechanics for a new category
There were no established patterns for AI agents, so the workspace leans on mechanics developers already know from their own tools. The novelty sits in what the agents do, not in how the interface works.
- 03
Build the design system from day one
Pilot buyers and investors read consistency as maturity, and the team shipped weekly. A shared toolkit built alongside the first screens meant nobody designed the same screen twice, and it paid for itself within months.
Learnings
In a new category, the job is comprehension before features. Showing the work is what earns permission to automate it. A design system built early pays for itself within months, and fundraising turned out to be a design problem as much as a narrative one.
KAVIA AI went from a blank page to a platform enterprise teams were willing to pilot. Designing for legibility rather than magic is what made autonomous agents feel safe to adopt, and it is what the $1M pre-seed round was raised on.