The work should decide
Interviews are the noisiest, most bias-prone signal in hiring — and they’re the one most funnels lean on hardest. We think a real, compensated work sample should carry the weight instead. So we built the funnel around it.
FairHire started from a frustration: the most important decisions a company makes — who it hires — are often the least transparent and the most bias-prone. We think that’s a solvable problem, and that the fix is structural: change what the funnel weighs, and make every decision auditable.
Interviews are the noisiest, most bias-prone signal in hiring — and they’re the one most funnels lean on hardest. We think a real, compensated work sample should carry the weight instead. So we built the funnel around it.
You can’t ask a candidate — or a committee — to trust a fairness claim they can’t inspect. That’s why the math is transparent, the decision publishes a trail, and the whole thing is open source. Verifiable beats trustworthy.
The next generation of hiring will be run partly by agents. We’d rather give them a real, permission-checked, audited seat at the table — with the consequential decisions kept human — than staple a chatbot to a sidebar.
FairHire is fairness-first, open-source, and agent-native by design. It assembles reusable building blocks — auth and multi-tenant orgs, an MCP agent spine, collaboration, reporting, and an immutable audit log — and adds only the hiring-specific last mile: the funnel, weighted scorecards, paid trials, and the fairness trail. Small enough to read, honest enough to trust, and free to self-host under the MIT license.
Give every candidate the same standard — and prove it.