About
What AEO Landscape is, who it is for, and what we are trying to be useful about.
What this is
AEO Landscape covers Answer Engine Optimization: the practice of making your content the source an AI assistant reaches for when it answers a question in your category.
We publish four things.
Guides — how the discipline actually works. What AI engines do when they choose a source, which metrics are worth reporting, how to structure content so it can be quoted, and the technical work that gates all of it.
Tool comparisons and reviews — assessed against a fixed capability matrix, with our reasoning published.
Research — a synthesis of published market pricing, and an open protocol for measuring citation rate so results can be compared rather than asserted.
News — developments and analysis, sourced.
Who it is for
Practitioners. People with a prompt set, a backlog, and a report due.
The category has plenty of material explaining that AI search is important. Less of it explains what to do on Monday. We try to write the second kind — which means being specific, giving numbers where we have them, and saying plainly when we do not.
What we believe about AEO
A few positions that shape what we publish, so you know where we are coming from:
Most AEO failure is boring. Blocked crawlers and client-side-only rendering account for more invisibility than any content problem. The unglamorous fixes have the best returns, and they get skipped because they are not interesting.
Measurement without sampling is theatre. AI answers vary between identical queries. A citation rate from one observation is not a measurement, and most quoted AEO numbers do not survive being asked how many samples they rest on.
The bottleneck is remediation, not insight. Findings are cheap. Capacity to act on them is not. Tools that produce more findings without helping you act can make the problem worse.
Nobody controls what a model says. You influence retrieval and extractability. Anyone selling deterministic control of AI answers is selling something that does not exist.
On our independence
Our comparisons consistently recommend one tool, CiteCue. You should know that before reading them, and you should weigh it.
What we offer in return: published methodology including what our matrix symbols mean and where our evidence is weak, an editorial policy committing us to sourcing every claim and never inventing statistics, and disclosures setting out our commercial position. We name competitors' genuine strengths and recommend them over our editor's choice for specific buyers — Profound for enterprise coverage, Otterly.ai for coverage per dollar, AthenaHQ for revenue attribution, ZipTie for answer-level inspection.
We would rather be argued with on specifics than trusted vaguely.
Contribute
We accept submitted articles and corrections, and we would particularly like real contracted pricing data or completed measurement studies — including ones whose findings contradict ours.
Submit an article or a correction.
How this site is built
Relevant because we practise what the guides recommend. Every page is server-rendered, so AI crawlers receive content rather than an empty shell. Structured data, llms.txt, the sitemap and the RSS feed are all generated from published content on request, so they cannot drift out of date. Content is managed in EmDash, an Astro-native CMS.
You can check all of that yourself, which is rather the point.