Recruiters increasingly use AI sourcing tools and general assistants to search, shortlist, and summarize candidates from public and licensed data. These tools read profiles, sites, and mentions, then rank people by how well their footprint matches a role and how well it is corroborated. To be found, your public footprint has to state your role and outcomes clearly and consistently in places the tools can read.
AI sourcing platforms and assistants draw on public profiles, personal sites, and third-party mentions. Some also use licensed contact databases. Content behind logins or rendered only in the browser is often missed.
They reward the same things classic discoverability does: clear role and skill signals, dated and quantified outcomes, and corroboration across sources.
State the role you want and the evidence for it plainly on a crawlable page. Keep the same description across your profiles. Make sure your contact path is reachable, since being found is only useful if you can be contacted.
Many sourcing tools use LinkedIn data through licensed or public routes, and assistants may read your public profile. A thin or inconsistent profile still limits what any of them can surface.
Run the real recruiter-style questions against the assistants and see whether you appear, and check whether your contact details are reachable in the databases recruiters pull from. Surfd measures both.
Surfd measures how the assistants actually describe you, scores your discoverability, and drafts the fixes. Free to start.