Blog
GEO and AEO from first principles to real-world practice. How to build content that AI answers cite.
Read →What is GEO: how to get cited in the answer, not just ranked in search
People have started asking ChatGPT instead of typing into a search box. If your brand name isn't in the answer the AI synthesizes, ranking #1 in search means nothing at the moment the decision is made. GEO is the work of earning that citation.
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Read →AEO vs SEO: from the age of links to the age of answers
If SEO was the fight to win links in search results, AEO is the fight to land your brand inside the answer the AI produces. We break down how the goals, core metrics, content structure, measurement, and unit of competition diverge across five axes, then point to five things a marketer can apply first thing tomorrow.
Read →What AI chooses to cite: why only one article makes the answer when the topic is the same
Generative engines don't pick the best-written article; they pick the one that's easiest to lift straight into an answer. We put two versions of the same content side by side and look at what earns a citation and what blocks it.
Read →The AI engine map: from ChatGPT to AI Overview, where to focus first
Ask the same question and ChatGPT rarely shows its sources. Perplexity footnotes nearly every sentence, and AI Overview floats an answer above the search results. Every engine sees the web differently and handles sources differently. Try to "look good to AI" without understanding those differences and you'll pour effort into the wrong place.
Read →What citation rate really is: how to read GEO's core metric correctly
Citation rate is the share of tracked questions where the answer drew on you as a source. Without nailing the definition in one line, every team calls a different number by the same name. We go a level deeper: the formula, the breakdown by engine and question group, the bar for what counts as a citation, and the measurement traps that make you misread the number.
Read →How to read Share of Voice: presence and citation rank within a single answer
Inside one ChatGPT or Perplexity answer, how much and how early do we get named compared to competitors? We cover how to read Share of Voice and citation rank, what decisions to tie those numbers to, and where teams most often slip.
Read →Why you need multi-LLM tracking
Ask the same question and ChatGPT, Perplexity, and Google AI Overview give different answers, because their training cutoffs, whether they search, and their model tendencies all differ. We cover why the answers diverge, what you miss by watching just one, and in what order to read multiple engines and set priorities.
Read →Tracking AI Overview: why a surface different from chatbots needs its own view
Plenty of teams track ChatGPT citations but miss Google AI Overview, yet the two are separate surfaces: different triggers for how an answer forms, different shapes of incoming questions, different logic for choosing sources. We cover why you should measure them separately, how to track each, and the limits that today's tracking has to honestly admit.
Read →The structure of citable writing: designing for excerptability with a question-answer-evidence pattern
Generative engines don't read an article end to end; they quickly hunt for the one paragraph they can lift to build an answer. We line up before-and-after versions of the section structure and sentence techniques that make that paragraph easy to pull.
Read →Verifiable claims get cited: the practice of designing authority signals
Generative engines tend to cite a claim they can verify over one that's merely well written. We lay out, in practical terms, how to design authority signals: back claims with evidence, favor primary sources, publish your own data, and mark sources so both people and machines can read them.
Read →Entity clarity: how to make AI treat your brand as a single entity
Generative engines treat a brand not as a keyword but as an entity. So when you standardize how you're named, fix a one-sentence definition, and connect Organization schema and relationships, the model has stronger grounds to bind you into one clear thing. We unpack that in five steps.
Read →The complete llms.txt guide: what it is, why it appeared, and how much to expect
A hands-on llms.txt guide for anyone who only knew robots.txt and sitemap.xml. We cover how to write it, examples, what to include and leave out, and realistic expectations that neither overhype nor underrate it.
Read →Designing a weekly improvement loop: how to actually run measure, gap, publish, remeasure
What moves GEO isn't a one-time optimization but a loop that turns every week. We cover why a weekly cadence, what to log, how to prioritize gaps and judge impact, all centered on an operating rhythm.
Read →Where to place your best writing: a GEO publishing strategy
When a great article lives only on your own blog, AI tends to read it as one company's self-promotion. Citations begin only when the same fact is scattered across several surfaces with different perspectives. We cover the next step after writing, from why your own site isn't enough, to multi-domain consensus, fast indexing, and post-publish measurement.
Read →7 ways to raise your AI citation rate
Seven practical tactics that get ChatGPT, Perplexity, and Google AI Overview to draw on your brand as a source when they build an answer. We cover why each tactic works and a step-by-step way to apply it to your content today.
Read →From measure to remeasure: a 4-week GEO closed-loop case study
This is a representative scenario built by combining several cases, not a named customer. We follow week by week how a GEO closed loop turns over four weeks: noticing the problem, measuring a baseline, finding gaps, publishing content, and measuring again. The numbers in the piece are illustrative values to show the flow, not actual measurements.
Read →The GEO maturity model: what stage is your organization at right now?
Everyone knows they "should be doing GEO," but most have no idea where they actually stand. Split it into four stages, from unaware to closed loop, and the next move becomes clear.
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