Bonfires had a bot prototype, no interface and no defined users, so I designed the first product UI and the design system, and frontend implementation time dropped about 30%.
Role
Founding Product Designer
Dates
Sep 2024 - Feb 2026
Team
CEO, CTO, FE/BE/AI engineers, infra specialists





Groups produce knowledge in chat and lose it there. Decisions get rediscovered, context does not persist, and newcomers have nothing to read. When I joined, Bonfires was an unfinished whitepaper and a rough Telegram bot: no interface, no defined users, no happy path, and a team building on its own assumptions. My job was to find out who this was for and build the first product they could use. Bonfires connects an agent to a community chat. Every 20 minutes a pipeline summarises the discussion and extracts durable knowledge into a graph that people query through chat or explore in the UI.
The founders saw Bonfires as a product for anyone and did not want it designed around personas, and they wanted the dense graph as the default view. The original graph was laggy and unreadable, and most people did not know what they were looking at.
I disagreed on both and proposed we settle it with real users. The interviews confirmed it: people needed a first run built for their own situation, and the graph only made sense to power users. Personas became the basis for the flows. I redesigned the graph as a clustered D3 view inspired by Obsidian, readable before dense, mobile first, and moved it behind chat and summaries.
Made against the founders' preference at the time.


Evidence. Daily graph usage rose after the redesign and the performance complaints stopped.
Teams had no single place to see what was going on and kept asking the agent the same questions.
I designed a dashboard that pulls activity, governance events, tasks and summaries into one surface, with sections the group can add or remove.

Evidence. Repeated agent queries dropped and people caught up from one screen.
Episode summaries were long and full of jargon, so people skimmed past them or ignored them.
I reframed episodes as activity and split each into a short scan layer and a deeper layer, with links from the timeline into the graph.

Evidence. People scanned recent developments faster and found related context they had missed.