AI-native design and delivery
We brought AI into how product teams design and ship. The hard part was not the tooling. It was finding the line between where AI speeds you up and where people need to stay in charge.
The challenge
Design cycles took around six weeks. Business partners reviewed static specifications, feedback arrived late, and engineering waited for decisions.
Leadership wanted AI in the workflow, but in a safety-critical domain it had to be trustworthy, reviewable and honest about its limits.
The shape of it
Illustrative screens with invented data.
What we did
Define
An AI strategy with leadership: where AI helps, where it does not, and which guardrails apply.
Design
A sandbox front end where business partners click through proposed screens instead of reading specs. Feedback in days, not weeks.
Build
An end-to-end GenAI workflow. Front-end code, use cases and user stories go in. Backend code, documentation and test cases come out.
Improve
AI connected to Jira and Confluence to draft and comment on tickets. The most complex systems stay human-led, because no model holds enough domain context to be trusted with the whole job.
What it took
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