AI search visibility auditor

What GEO and AEO actually mean

GEO (generative engine optimisation) and AEO (answer engine optimisation) are two names for the same shift: optimising not just to rank in a list of blue links, but to be the source an AI assistant pulls from when it writes an answer. The mechanics differ from classic SEO. An answer engine retrieves a handful of passages, synthesises them into prose, and sometimes cites the sources. To be one of those sources, your content has to be easy for a machine to find, parse, trust, and quote. That is what this audit measures.

The two jobs this tool does

1. It measures where you stand. From your brand, your competitors, and your topic, it writes a set of realistic prompts a buyer might type, grouped by intent: broad discovery ("best X in 2026"), brand-specific ("is X any good"), head-to-head comparisons, and the reverse-intent "alternatives to a competitor" queries where you most want to appear. You run each one in the four big assistants and record, in the downloadable grid, whether you were mentioned, whether you were cited with a link, and roughly where in the answer. Done monthly, that grid is your AI-visibility trend line.

2. It tells you why. Paste your page HTML or just the key facts, and the audit scores the content signals that decide whether an assistant can lift an answer from you. It is heuristic, not a crystal ball, but every signal it checks is one you can act on the same afternoon.

What the readiness score looks at

• A clear entity definition. Assistants build an internal sense of what your brand is. One plain sentence ("Acme is a product-analytics tool for mobile teams") does far more than a clever tagline.
• Structured data (JSON-LD). Schema removes ambiguity about what your page describes, so a model does not have to guess.
• FAQ blocks. Real questions with short, direct answers map almost one-to-one onto the way people prompt assistants, and answer engines lift them readily.
• Citable stats. A specific, attributable figure is the kind of thing an assistant likes to quote and credit. Vague claims are not.
• Heading structure. Descriptive subheadings split a page into passages that can each stand alone as an answer.
• Freshness signals. A visible update date and a current year tell a model your page is not stale, which it weighs when choosing what to cite.
• An llms.txt file. A young convention, but a cheap one: a curated map of your best pages at your domain root. Our llms.txt generator builds one for you.

How to use the results

1. Run the audit on your most important page and note the score and the prioritised fixes.
2. Download the scorecard and the tracking CSV, then run your test prompts across the four assistants.
3. Fix the biggest gaps first; entity clarity, schema, and citable stats usually move the needle most.
4. Re-run the prompts monthly and watch whether your mention and citation rate climbs.

An honest caveat

No tool can promise you a spot in an AI answer. The engines change how they retrieve and cite sources often, results vary from one run to the next, and they differ by region and by how the question is phrased. Treat the score as a prioritised checklist of things that genuinely help, not as a guarantee, and always confirm with your own prompt tests.

Privacy

The prompt generation, the audit, and the file downloads all happen in your browser. The content you paste never leaves the page. The only network call is the optional email signup for the tracker sheet.

AI search visibility questions, answered