Our first AI job-search assistant, 2024
Inspired by Amazon’s Rufus, we explored guidance at the point of search: turning a job query into context on roles, salaries, demand and skills.
Designing for AI uncertainty
I mapped every combination of available, missing and insufficient data, then defined when Fabi should answer, qualify its response, ask for more context or hold back.
What if the most valuable AI in job search wasn’t for searching at all?
I shifted the idea towards recruiting: an agent prepared with candidate context, able to handle early qualification so recruiters could focus on deeper conversations.
Fabi, reimagined for 2026: From search answers to first conversations.
I revisited the idea independently, this time from the recruiter’s side: a voice agent that could handle early qualification and make more room for deeper human conversations.
How the system connects
A system map links candidate information and consent to matching, Fabi’s qualifying call and recruiter follow-up. It locates each decision before showing the interfaces.
Orchestrate the agent. See every decision.
I designed the workflow editor to put recruiters’ expertise into qualification and handoffs. Coloured nodes, visible routes, plain-language conditions and few but meaningful icons make that logic easier to inspect.
See the whole operation. Human attention where it matters.
I brought conversation trends, candidate questions and cases needing human review into focus. Restrained colour and fewer decorative icons keep the next action clear.