Role: Design Lead & PM
Scope: Zero-to-one internal AI product. Shipped MVP in 4 weeks. Scaled to 500+ users in 12 weeks through continuous production releases. Flint predated the current generation of no-code AI builders. At the time, there was no established playbook for how these tools should work or who they were for.
Flint predated the current generation of AI app builders and became an early internal experiment in natural-language software creation.
Problem
Non-technical teams across IBM wanted to prototype internal tools to improve workflows, but building even simple applications required engineering support. Existing no-code tools struggled to produce outputs aligned with IBM’s Carbon design system and enterprise standards, which reduced trust and limited real adoption.
What I designed
Flint — a natural-language → application builder that generates functional mini-apps from prompts while enforcing Carbon design system constraints. I led product design and strategy from 0→1, prototyping interaction patterns directly in production code and running weekly user testing to iterate rapidly.
Key insight
Users valued momentum over model sophistication.
Fast, editable drafts created trust and engagement, while slower “higher-quality” generations broke creative flow. Designing for rapid iteration and visible system feedback proved more important than maximising model reasoning depth.
Impact
Flint was an internal natural language-to-application builder, similar to tools like v0 or Lovable, designed to allow non-technical IBM teams to create functional mini-apps from text prompts.
I led product design and product strategy from inception through scale, shipping an MVP in four weeks and growing adoption to 500+ internal users within 12 weeks. I worked directly in production code to prototype new interaction patterns and ran weekly user testing to inform rapid iteration. For this, we collaborated with the Carbon MCP team to keep outputs aligned with IBM's design system and brand standards.