AEO, AIO & GEO Readiness Audit
Being on page one is no longer the same as being the answer. Answer engines and AI search lift and cite the pages that are easy to extract, well-structured, trustworthy, fresh and quotable. Paste a URL and this tool fetches the live page for you, reads its real HTML — structured data, headings, author signals, dates, outbound citations, word count — checks for an llms.txt, then scores those five things, weights them, and hands you a ranked to-do list so you fix the gaps that move the score most. It is a transparent, heuristic readiness model: it tells you what it found and why each fix matters.
AEO/AIO/GEO readiness is how likely an answer engine, AI Overview or generative model is to surface and cite your page. This tool scores five categories — extractable answers, structured data, authority & E-E-A-T, freshness & coverage, and citability — converts each to a 0–100 sub-score, and combines them into one weighted readiness score with a verdict band. It then ranks every open fix by the overall points it returns, so you start with the change that helps most. Enter the URL of the page you want to audit and press Run audit — the tool fetches and parses the live page, so the scores reflect what an engine actually sees.
AEO, AIO & GEO Readiness Audit inputs and result
llms.txt at the site root. Nothing is stored; this is a heuristic readiness model, not a guarantee of placement.| Category | Weight | Sub-score | Rating |
|---|
How to use this calculator
- Enter the page URLPaste the exact URL of the page you want to audit and press Run audit. The tool fetches the live page through the RGM Tools service and parses its real HTML — audit a single page, not a whole site, because readiness is page-and-query specific.
- Let it read the live pageThe audit inspects the page as it stands today: JSON-LD schema types, heading structure, author and date signals, lists and tables, outbound citations, word count, robots/canonical, and whether the site publishes an llms.txt. You earn credit only for signals that are genuinely there.
- Read the weighted score and bandThe big number is your overall readiness out of 100; the band tells you whether an engine has reason to cite the page yet. The scorecard shows which of the five categories is dragging it down.
- Work the prioritized fix list top-downEvery open fix is ranked by the overall points it returns. Start at the top: the first item is always the single change that lifts your readiness most.
- Re-run and exportAfter you ship the fixes, run the audit again to confirm the gain, then export the scorecard and fix list for the next review or hand-off.
RGM Expert Says
The honest starting point for AI search is that there is no secret file or markup that gets you cited. Google has said plainly that succeeding in AI Overviews and AI Mode runs on the same foundations as Search: helpful, structured, trustworthy content. So this audit does not chase tricks. It scores the five things that actually correlate with being lifted into an answer, and it weights authority and extractability highest because that is where most pages lose.
What we have learned running these audits is that the biggest wins are usually the dullest. An answer-first opening sentence, a named expert author with a real bio, and a few cited statistics will move a page further than any amount of schema polishing. The Princeton GEO study put numbers on this: citing sources, adding statistics, and adding quotations were among the highest-impact tactics, while keyword stuffing did nothing. We built the prioritized list so the dull, high-impact work rises to the top instead of the shiny, low-impact work.
Treat the score as a readiness check, not a guarantee. A 90 means an engine has every reason to trust and quote you; it does not promise the citation, because you still have to be the most useful answer on the topic. Pair this audit with our citation-probability checker to see how a specific page reads to a model, the query fan-out generator to map the questions you should cover, and the FAQ-schema and llms.txt generators to ship the structured pieces. Audit, fix, re-audit — that loop is the whole game.
How it works
The scoring is a transparent weighted average. Each question earns full, half or no credit; questions roll up into a category sub-score; the five categories combine by weight into the overall readiness number.
- Category weights — Extractable answers 25, Structured data 20, Authority & E-E-A-T 25, Freshness & coverage 15, Citability 15. Authority and extractability lead because trust and liftability drive citations more than markup does.
- Field credit — full, half or no credit per question, so partial implementations score fairly rather than all-or-nothing.
- Fix priority — the overall points a single fix returns, used to rank the to-do list so the highest-leverage change is always first.
The weights are RGM’s, informed by Google’s own AI-search guidance (E-E-A-T and structure over special files) and the empirical tactic rankings in the Princeton GEO: Generative Engine Optimization study. They are a defensible default, not a law of nature — adjust them if your category weights authority or freshness differently.
Why readiness, not rank, decides who gets cited
Classic SEO asks whether you rank; AI search asks whether you are the answer. When an engine assembles an AI Overview or a chat reply, it pulls sentences, facts and structure from a handful of sources and names a few of them. Pages that bury the answer, hide their structure in messy markup, or carry no visible author rarely make that shortlist, even when they rank well in the blue links.
Google has been explicit that there is no separate machine-readable file or markup you must add to appear in its AI features — the same quality, structure and E-E-A-T signals that help Search help AI Overviews too. That is good news: it means the work is durable. The same answer-first writing and trustworthy sourcing that earns a featured snippet also earns a citation in an AI answer, so you are not maintaining two playbooks.
The independent research points the same way. The Princeton GEO study tested nine tactics across thousands of queries and found that citing sources, adding statistics and adding quotations lifted visibility in generative answers by up to roughly 40%, while keyword stuffing produced no benefit. This audit turns those findings into a score you can act on, and ranks the fixes so the proven, high-impact moves come first.
What a readiness score means in practice
These bands are RGM’s interpretation, calibrated to the categories above. Treat them as a maturity guide, not a promise of placement — readiness earns the right to be considered, but being the most useful answer still wins the citation.
| Readiness band | Score | What it means |
|---|---|---|
| Strong | 80–100 | Extractable, well-structured, trustworthy and quotable. An engine has every reason to lift and cite this page; focus shifts to staying fresh and beating competing answers. |
| Solid | 60–79 | Citable, with clear gaps. Usually one weak category — often authority or schema — is capping the score. Close the top fixes to break into the strong band. |
| Partial | 40–59 | Real gaps remain. The page may rank but gives engines little to extract and trust. Expect to be passed over for cleaner, better-sourced competitors. |
| Weak | 0–39 | Not ready. Missing answer-first structure, author trust and citable material. Rework the page from the prioritized list before expecting any AI-search visibility. |
What the practitioners and researchers say
There are no additional requirements to appear in AI Overviews or AI Mode — the best practices for SEO continue to apply, because these features are rooted in Google’s core ranking and quality systems.
Citing sources, adding quotations and adding statistics were among the most effective tactics, improving visibility in generative answers by up to about 40%; keyword stuffing produced no benefit.