AEO and GEO: What Order Should You Actually Work In?
The difference between AEO and GEO, a practical 7-step order of work, and the mistakes teams repeat most.
People searching for “how to do AEO and GEO optimization” tend to be in the same spot. Search traffic isn't what it used to be, and when they ask ChatGPT or Gemini about their own company, the answer is either wrong or mentions only competitors. What nobody tells them clearly is where to start. This post lays out the order of work, a checklist, and the mistakes that keep repeating.
How are AEO and GEO different?
AEO (Answer Engine Optimization) is mostly about whether your documents are in a shape that can be quoted as an answer. GEO (Generative Engine Optimization) is about whether the model even considers you a candidate when it talks about your topic. The work overlaps heavily, which is why the two are usually handled together.
AEO therefore centers on assets you control: page structure, markup, sentence form. GEO extends to scattered external mentions, consistent brand naming, and third-party documents. Do only one and you end up either with tidy pages nobody recalls, or with mentions that describe you inaccurately.
What order should the work actually follow?
The first step is not fixing anything — it's measuring the current state. If you don't record how the major AI tools describe your brand today, you'll have no basis to claim improvement later. The sequence is measure → structure → content → external consistency → measure again.
- Baseline audit: pick 20–30 questions your customers would really ask, run the exact same wording through ChatGPT, Gemini and Claude, and save the answers verbatim. Prioritize near-purchase questions like “best pilates studio in X neighborhood” or “alternatives to X software.”
- Machine readability: if body copy only appears after JavaScript rendering, the page is close to blank for many collectors. Use “view page source” and check whether your sentences are actually there.
- Structured data: declare company, products, authors and contact details with JSON-LD such as Organization, Product, Article and FAQPage.
- Write quotable sentences: put a two-to-three sentence direct answer immediately under a question-style heading, with numbers, conditions and ranges inside the sentence. “Websites from KRW 2.7M, about two weeks” gets quoted; “contact us for pricing” does not.
- Entity consistency: use the same company name spelling, scope of business, address and contact info across your site and every external profile. Inconsistent naming stops a model from treating you as one entity.
- Crawling policy: use robots.txt and llms.txt to state which paths should be read and where the key documents live.
- Re-measure: rerun the same question set from step 1 and compare. Change the questions and the comparison becomes meaningless.
What mistakes do teams make most?
Failures usually come from sequence and expectations, not technology. The most dangerous assumption is that this is a one-time build. Generative answers keep shifting with model updates and external mentions, so periodic re-measurement is effectively the core of operations.
- Adding keywords without building answer-shaped copy — a conclusion buried after a long intro rarely gets cited.
- Building with no measurement — without a before/after, there's nothing to discuss.
- JSON-LD that contradicts what's on screen — that erodes trust rather than helping.
- Proposals promising specific placements or rankings — nobody controls another company's model output.
- Piling up blog posts while leaving core company and product pages thin — the model has no material to describe you with.
How Saenru approaches it
This is Saenru's own blog, so here's our approach. The Saenru site itself is an AEO build case: raw HTML with JSON-LD and llms.txt embedded. We run things on our own site first before applying them to anyone else's.
Our AEO and GEO consulting starts with a measured diagnostic report across ChatGPT, Gemini and Claude at KRW 190,000 (5 business days). Build packages start at KRW 3.6M and monthly operations at KRW 1.47M per month; the diagnostic fee is fully credited if you sign a build contract (all prices exclude VAT). If the site structure itself needs rebuilding, Saenru Studio handles websites from KRW 2.7M (about two weeks) and web app MVPs from KRW 8.7M (4–6 weeks), transferring the finished work as a full GitHub repository.
Taking only the diagnostic report and fixing things in-house is also a perfectly reasonable choice. The seven steps above can be walked without an agency, and simply logging what you changed makes every later decision easier.