The shift: recruiters ask AI first
To get found by recruiters today, you need to be findable by the AI tools recruiters use — which means having a public, machine-readable, structured page about yourself. That is the whole answer; the rest of this guide is how.
Sourcing has quietly inverted. Before a role is public, recruiters run searches through AI-powered sourcing tools and general assistants: shortlist people with a specific skill in a specific city, summarize a candidate, compare two profiles. By the time a job ad exists, the shortlist often already does. If the machine can't read you, you were never in the running — and you'll never know.
Why your CV and LinkedIn don't get you found
- —Your CV is reactive. It only exists inside processes you started. It does nothing for the searches happening before the job is posted.
- —LinkedIn is walled off. Full profiles sit behind login and the platform blocks AI crawlers, so the assistants doing the sourcing largely cannot read it. Inside the wall you compete with the entire feed; outside it you may not exist at all.
- —Scattered mentions are unmanaged. What AI can read about you today — old bios, conference pages, forum posts — is accidental. Nobody curated it, and it decides how you are summarized.
Being the answer: mention vs. citation
AI answers have two tiers. Being mentioned means the model knows your name in some context. Being cited means there is a page about you good enough for the AI to quote and link as its source. Recruiters act on citations — a linked page they can open, verify and forward. Your goal is to own that page.
Owning it takes the same properties that make anything AI-discoverable: server-rendered HTML (AI crawlers don't execute JavaScript), Schema.org structured data (so your role, skills, and history are facts, not prose), presence in the search indexes AI answers are built on, and enough substance to be worth quoting. We cover the mechanics in the AI discoverability guide.
The playbook: six steps to being found
1. Publish one canonical page
A single stable URL that answers “who is [you]” better than anything else online. Everything else links to it.
2. Make it machine-readable
Server-rendered, structured with Schema.org Person markup — name, title, employer, knowsAbout skills, education, sameAs links. This is what lets sourcing tools extract you accurately.
3. Lead with verifiable specifics
“Shipped the payment system serving 2M users” outperforms “experienced backend engineer” in every AI summary. Specific, checkable claims are what get quoted.
4. Keep your identity consistent
Same name, same title, same story across every surface a machine might read. Contradictions make AI hedge — and a hedged answer loses the shortlist.
5. Get indexed
Sitemaps, clean URLs, search-engine submission. Not glamorous; strictly necessary. A page outside the index is outside every AI answer built on it.
6. Put the link everywhere
Email signature, CV header, social bios, conference profiles. Every placement is both a human touchpoint and a crawlable signal that this page is the canonical you.
Where factcard fits
factcard is steps one, two, and five as a product: a server-rendered, Schema.org-structured profile at your own URL, pushed to search engines and readable by AI agents, for a one-time $29. You bring the specifics (step three); consistency and distribution (four and six) stay in your hands. Import your résumé, publish, and the machine-facing half of your job search runs without you.
Frequently asked questions
How do recruiters use AI to find candidates?
Sourcing tools and general assistants are used to search, summarize, and shortlist people before any job is posted. Recruiters ask for 'engineers in Stockholm who have shipped payment systems' or simply 'tell me about [name]' — and the AI assembles an answer from whatever public, crawlable information exists.
Why isn't my LinkedIn enough to get found?
LinkedIn blocks AI crawlers and hides full profiles behind login, so AI tools largely cannot read it. Inside LinkedIn you compete in a feed with everyone else. Outside it, where the AI actually looks, most professionals have nothing structured to find.
What should a 'canonical page' about me contain?
The facts a recruiter verifies first: name, current role, location, skills with real depth, selected work with outcomes, education, and links to profiles you control. Marked up with Schema.org Person data so machines extract it accurately.
Does this help with applicant tracking systems (ATS) too?
Indirectly. An ATS parses the résumé you submit, but recruiters increasingly google-and-AI candidates during screening. A clean, authoritative page that confirms and expands your résumé is the difference between a shrug and a shortlist.
How is factcard different from a portfolio site?
A portfolio is for humans who already found you. A factcard is built to be found: server-rendered, structured with Schema.org, indexed via sitemaps and IndexNow, with machine-readable endpoints for AI agents — online for three years, for a one-time $29.