How we measure your AI employer visibility

What we check: We check your robots.txt to see whether AI crawlers (GPTBot, ClaudeBot, etc.) can reach your content, and whether you publish a sitemap so they can find it efficiently.

Why it matters: This is the foundational gate. If AI bots can't crawl your site, nothing else matters — your careers content, structured data, and brand story all become invisible to the models candidates are asking.

What we check: We scan your pages for JSON-LD schemas — Organization, JobPosting, FAQPage, and others. These are the machine-readable annotations that tell AI models exactly what your company does, where you're based, and what roles you're hiring for.

Why it matters: JSON-LD is the language AI models use to understand your company. The Princeton GEO study (KDD 2024) and Microsoft's AEO guidelines both identify structured data as a primary driver of AI citation accuracy.

What we check: We check whether a careers or jobs page exists, is reachable by crawlers, and contains rich, specific content — not just a list of links to an ATS.

Why it matters: Content quality is the #1 driver of AI citations according to the Princeton GEO study. A thin careers page with nothing but iframe links gives AI nothing to work with. Detailed content about roles, teams, and culture gives it everything.

What we check: We look for FAQ sections, semantic HTML, answer-first structure, and content organised for easy AI extraction — the kind of formatting that makes it simple for models to pull accurate answers.

Why it matters: Both the GEO study and Microsoft's AEO guidelines show that answer-optimised content — clear headings, direct answers, structured FAQs — dramatically increases the chance AI will cite you accurately rather than guess.

What we check: We check for mentions across platforms candidates and AI models reference — major review sites, LinkedIn, job boards, and others. We cap scoring at 3 platforms so startups aren't penalised for not being everywhere.

Why it matters: Authority signals from multiple independent sources reinforce what AI models say about you. Research from Semrush and Moz shows that multi-platform presence correlates strongly with AI citation confidence.

What we check: We check whether salary data is published — pay ranges in job listings, pay transparency pages, or compensation frameworks. This is scored as a bonus: if no salary data exists, you start at 0 rather than being penalised.

Why it matters: Pay is the most-searched employer topic. Publishing salary data gives AI concrete figures instead of guesses. We weight this lower than other categories because transparency norms vary by region — but companies that publish it are rewarded.

What we check: We check for an llms.txt file — a proposed standard for giving AI models a human-readable site summary. Currently weighted at 0 because research shows zero measurable impact on AI responses.

Why it matters: We include this for transparency. The Senthor study (10M+ requests) and SE Ranking analysis (300K domains) both found no evidence that llms.txt improves AI visibility. If the evidence changes, we'll update the weight.