How AI decides which businesses to recommend
When someone asks ChatGPT, Claude, Perplexity or Google's AI for "the best [your category] in [your town]," a handful of businesses get named — and the rest are invisible. That outcome isn't random. It's driven by six measurable signals. This is the framework we use to score them, and to move a business from invisible to recommended.
Visibility vs Readiness
Two different questions, two different scores. Most "SEO audits" conflate them — and that's why they don't predict whether AI actually recommends you.
Are you in the answer right now?
Whether the AI assistants actually name or cite you when a real customer asks. Mention-driven, measured live across four engines. This is the outcome that wins customers.
Is your site built for AI?
How well your foundations support being recommended — the six signals below, scored 0–100. You can be highly ready yet still invisible: a well-built site the AIs don't yet know or trust.
The gap between them is the work: readiness is what you control; visibility is what it earns you.
What AI weighs to decide
Citability
25% · highest weight- What
- How quotable and self-contained your content is — how easily an AI can lift a clear, factual passage straight into its answer.
- Why
- AI answers are assembled from quotable passages. Thin, vague or purely promotional copy gives the model nothing to use, so it reaches for a competitor who wrote a clear answer.
- Win it
- Answer real questions directly, lead with the conclusion, use headings, lists, statistics and specifics. Write the paragraph you'd want quoted.
Brand
20%- What
- Whether AI recognises you as a real, distinct entity and whether trusted third parties — Wikipedia, Wikidata, Reddit, YouTube, reviews, industry directories — reference you.
- Why
- AI recommends what others vouch for. A business with no third-party footprint reads as unknown and risky to name, however polished its own site.
- Win it
- Earn citations off your own domain: directory listings, genuine reviews, a Wikipedia-grade reference, a consistent entity (same name, address, links) everywhere.
E-E-A-T
20%- What
- Experience, Expertise, Authoritativeness, Trust. Author credentials, a real About page, visible contact details, provenance and a privacy policy.
- Why
- Recommending a business is a trust decision. AI is built to avoid endorsing the untrustworthy, so it favours sites that prove who's behind them.
- Win it
- Name your people and their credentials, show how you know what you claim, make it obvious you're a real, contactable operation.
Technical
15%- What
- Whether AI crawlers can cleanly access and read you — HTTPS, sitemaps, canonical URLs, mobile, security headers, server-side rendering and Core Web Vitals.
- Why
- If the content an AI needs only appears after heavy JavaScript, or the crawler is blocked or timed out, none of the other signals matter — it never sees them.
- Win it
- Serve content in the initial HTML, keep it fast and crawlable, fix the plumbing. (See Foundation below — your platform decides how easily.)
Schema
10%- What
- Structured data (JSON-LD) — Organization, LocalBusiness, Product, FAQ — that states what you are, where you operate and what you offer in machine-readable form.
- Why
- Schema removes guesswork. It hands the AI a clean, unambiguous fact sheet about your entity instead of making it infer everything from prose.
- Win it
- Add valid Organization + LocalBusiness JSON-LD, with sameAs links to your verified profiles. Cheap, fast, high-leverage.
Platform
10%- What
- Readiness for each AI surface — crawler access (robots.txt for GPTBot, Google-Extended, PerplexityBot, ClaudeBot), an llms.txt, and answer-shaped content tuned to how each engine retrieves.
- Why
- Each assistant sources answers differently. Quietly blocking AI crawlers — or never telling them what you do — guarantees you're absent from that surface.
- Win it
- Open the right crawlers, publish an llms.txt, and structure pages as direct answers to the questions customers actually ask the AI.
Your foundation
Underneath the six signals is your platform — and it decides how cheaply you can win them. A modern, controllable stack lets you fix schema, llms.txt, rendering and agent integration in hours. A closed legacy builder caps what you can do at all.
- Modern (Next.js, Astro, custom Cloudflare): full control of rendering, schema and llms.txt; easy to make agent- and GEO-ready.
- Workable (Shopify, Webflow, well-built WordPress): GEO-ready with deliberate setup; some ceilings on control.
- Legacy (Wix, Squarespace, page-builder WordPress): limited control over the exact signals AI relies on — often the single biggest constraint on visibility.
How we score it
An autonomous agent interrogates ChatGPT, Claude, Perplexity and Google's AI with the question your customers actually ask, then audits 30+ signals across the six dimensions above plus your foundation and agent-readiness — producing a live AI Visibility score and an AI Readiness score, with the specific fixes that close the gap. The deep audit adds account-gated signals, component versions and a human review.
See your scores
Run the 60-second scan and find out whether AI recommends you — and exactly which of the six signals is holding you back.