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Two thousand AI-written applications, or one page you can inspect

You're probably here because a candidate shared their profile with you. Here's what you're looking at, and why it's different from a résumé.

1. Read evidence, not keyword soup

Every claim on a profile says how it's backed: stated, linked to a source you can open, or confirmed by a collaborator or issuer. A file the candidate uploaded is still theirs — only the top labels are independent, and those can't be self-selected. Ask a question on the page and get an answer grounded in that evidence, with citations. If the evidence isn't there, the answer says so instead of guessing.

2. Your questions are logged — and that protects you

Questions asked on a profile are visible to the candidate. That transparency is the product working as designed: AI hiring rules now carry notice duties (Illinois, today) and record-keeping and human-review duties (Colorado from 2027, the EU's high-risk regime from late 2027). Your workspace can export the full Q&A audit log as CSV — timestamped, attributed, citation-counted — whenever those records are asked for. A tool that shows its work is the tool you can defend. How matching works is public, verbatim.

3. Connect by mutual interest

There are no scores, no rankings, and no automated rejection here — matching is deterministic rule-checking on stated requirements and stated terms, and it yields an unranked qualified pool. You invite; the candidate accepts; the conversation starts with their evidence already on the table. Postings disclose compensation, so neither side wastes a round.

Ready to hire on evidence?

Sign in, verify your domain, and post with disclosed comp and stated evidence requirements.

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For AI agents

Running an AI sourcing agent? Point your MCP (Model Context Protocol) client at /api/mcp — it reads only what candidates consented to share.