Recruiting is often fragmented across inboxes, spreadsheets, ATS tools, calendars, sourcing platforms, LinkedIn, and disconnected candidate notes. Important context gets lost, follow-ups slip through gaps, and recruiters spend too much time carrying information from one system to another.

For lean hiring teams, this problem is even more pronounced. A founder may be the hiring manager, sourcer, recruiter, scheduler, and follow-up owner at the same time. They may know exactly who they need to hire, but every new search starts cold and the pipeline rarely has the chance to compound.

Jobraker Recruiter is a local-first AI recruiting copilot built to bring that entire journey into one connected workspace. It connects sourcing, candidate evaluation, outreach, pipeline management, interview scheduling, meeting notes, background agents, and analytics into one continuous recruiter workflow.

A sourced profile can become an enriched candidate record, a drafted message, a pipeline card, an interview, a set of meeting notes, and eventually a measurable hiring outcome without losing the context that shaped each decision.

Rather than treating AI as a black box, Jobraker makes its work visible and inspectable. Recruiters can see the signals behind a candidate match, review AI-generated outreach before sending it, surface pipeline bottlenecks, summarize meetings, and receive recommended next actions while remaining in control of every important decision.

The product turns natural-language hiring intent into practical recruiting work: role briefs, enriched candidate profiles, fit signals, outreach drafts, meeting notes, pipeline movement, background tasks, and analytics. It also leaves behind editable records, visible pipeline state, and a Markdown-based knowledge vault that grows with every search instead of hiding context inside an opaque system.

I built Jobraker in Figma Make as a fully interactive prototype using a custom neo-brutalist design system and a “living notebook” interaction model. Major modules transition like turning pages in a recruiting notebook, while internal actions morph like physical paper: candidate cards unfold into profiles, pipeline cards move between stages, charts redraw around new decisions, and AI work appears through visible hand-drawn simulations.

The goal was not to create another static recruiting dashboard. I wanted to explore what happens when recruiting software feels more connected, transparent, intentional, and alive while giving smaller teams the leverage that larger companies usually buy through headcount, expensive software, and disconnected tools.

Jobraker helps recruiters spend less time switching between tools and more time making thoughtful, evidence-backed hiring decisions. Prototype: Jobraker Recruiter Figma Community Link: Jobraker Recruiter Template