What is the difference between SEO and AEO?

SEO optimizes a page to be retrieved and ranked in a list of results. AEO optimizes a passage to be retrieved, synthesized into an AI assistant's answer, and cited as the source. AEO depends on answer-first structure, entity clarity, structured data, and explicit assistant crawler access, and it is measured in citation share rather than ranking position.

Is AEO just SEO with a new name?

They overlap but they are not the same. SEO optimizes for a page being retrieved and ranked. AEO optimizes for a passage being retrieved, synthesized, and attributed. The content structure, the schema, and the measurement are all different.

How do you measure AEO when there is no ranking?

We define a prompt set that mirrors real buyer questions, run it across the major assistants on a schedule, and track whether you appear, in what position within the answer, in what sentiment, and against which competitors. Referral traffic from assistant surfaces is tracked separately.

Do I have to let AI crawlers scrape my site?

To be cited, yes. An assistant cannot quote a page it was never allowed to read. Some brands make a deliberate choice to block them. That is a valid position, and it also removes AEO from the table.

Does FAQ schema still matter now that the rich result is gone?

For rich results in Google, largely not. For answer engines, it still matters. Question and answer markup is a clean, unambiguous signal about which passage answers which question, and assistants consume it.

Will AI-written content get cited?

Undifferentiated generated content usually will not, because it restates what the model already knows. What earns a citation is first-hand experience, original data, and attributable expertise. We use AI to accelerate research and structure, not to replace the source.

How long before we show up in answers?

Faster than classic rankings in many cases. Access and structure fixes on pages that already have authority can surface within weeks. Building citation share on competitive questions from a standing start takes several months.

Pillar two

Become the source the assistant cites

A growing share of buying research never reaches a results page. Someone asks an assistant, gets a synthesized answer, and clicks one or two citations. AEO is the work of being in that answer.

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What this is

Different surface, different unit of retrieval

Classic search retrieves pages. Answer engines retrieve passages, then decide which ones to attribute. Optimizing for the first does not automatically win the second.

An assistant assembling a response is choosing between passages, not between domains. It rewards a paragraph that answers the question completely in isolation, and it discards prose that only makes sense after four scrolls of setup. That single difference reshapes how a page should be written.

Trust resolution is the second difference. The model needs to know who is speaking. Consistent entity data, real named authors with credentials, corroborating references across the web, and structured data that agrees with the visible page all feed that judgment. Anonymous, unattributed content is cheap to generate and gets treated accordingly.

Access is the third. If your content only exists after JavaScript runs, or your robots file quietly blocks the assistant crawlers, none of the writing matters. We fix access first, then structure, then authority.

The last difference is measurement. There is no rank tracker for a conversation. We track prompt-level visibility, citation share against named competitors, and the referral traffic that arrives from assistant surfaces, then feed that back into which questions get answered next.

A fit when

Your buyers research with assistants before they ever open a results page.
You have genuine expertise that can be attributed to named people.
You want to be early on a surface most competitors have not instrumented.

Not a fit when

You want AI-generated volume published without review.
Your category is transactional enough that nobody asks a question before buying.
You cannot allow assistant crawlers for policy reasons.

What is inside

Six programs under the AEO pillar

Access and structure come first. Citation tracking only means something once there is something to cite.

AI search visibility

How assistants retrieve, ground, and select sources, and the work that makes your pages eligible to be named in the answer.

Explore ai search visibility

llms.txt implementation

A maintained machine front door describing what your organisation does and where the canonical pages live. This site runs one at /llms.txt.

Explore llms.txt implementation

Entity optimization

Disambiguation, knowledge graph presence, sameAs and NAP consistency, and the corroboration that resolves two mentions into one entity.

Explore entity optimization

Schema markup

Structured data as machine-readable truth, generated from a shared component library rather than hand-written blobs, then validated.

Explore schema markup

AI citation tracking

Prompt tracking, citation share, sentiment, and competitor comparison, with honest limits on what this young discipline can measure.

Explore ai citation tracking

Answer engine audit

The audit deliverable. What we test, which engines we test against, what the report contains, and the remediation plan that follows.

Explore answer engine audit

How we run it

How an AEO program runs here

Five phases, starting with the questions your buyers already ask an assistant instead of the keywords a tool suggested.

Step 1: Prompt research
We build the real prompt set, run it across the major assistants, and record who gets cited today and in what tone.
Step 2: Access and structure audit
Crawler policy, rendering, schema coverage, and entity consistency reviewed against what the assistants can actually reach.
Step 3: Answer architecture
Each priority question is assigned to a page and rewritten answer-first, with schema generated from the same content.
Step 4: Corroboration
Author credentials, third-party references, and consistent entity signals so the answer has a verifiable source behind it.
Step 5: Monitor and expand
Monthly re-run of the prompt set. Gained citations get reinforced, lost ones get diagnosed, new prompts enter the set.

What is answer engine optimization?

Answer engine optimization is the practice of structuring content, entity data, and site access so AI assistants such as ChatGPT, Perplexity, Gemini, and Google AI Overviews retrieve your page, use it in a synthesized answer, and cite you as the source.

The retrieval unit is a passage, so each answer must stand alone without the surrounding page.
Structured data and consistent entity signals tell the model who is answering and whether to trust them.
Assistant crawlers must be allowed and served server-rendered HTML or none of the content is reachable.
Success is measured in citation share across a defined prompt set, not in ranking positions.

The difference

Where AEO diverges from classic SEO

Both matter. Confusing one for the other is why most brands are invisible on the newer surface.

Classic SEO

Unit retrieved: A page in a ranked list
Winning shape: Comprehensive page covering a topic
Trust signal: Links and domain authority
Structured data role: Eligibility for rich results
Access requirement: Googlebot, renders JavaScript eventually
Measurement: Positions, impressions, clicks

AEO

Unit retrieved: A passage inside a synthesized answer
Winning shape: Self-contained answer that survives extraction
Trust signal: Entity consistency, authorship, corroboration
Structured data role: Direct machine-readable answer mapping
Access requirement: Assistant crawlers, often no rendering at all
Measurement: Citation share, sentiment, prompt coverage

Classic SEO compared with answer engine optimization
Dimension	Classic SEO	AEO
Unit retrieved	A page in a ranked list	A passage inside a synthesized answer
Winning shape	Comprehensive page covering a topic	Self-contained answer that survives extraction
Trust signal	Links and domain authority	Entity consistency, authorship, corroboration
Structured data role	Eligibility for rich results	Direct machine-readable answer mapping
Access requirement	Googlebot, renders JavaScript eventually	Assistant crawlers, often no rendering at all
Measurement	Positions, impressions, clicks	Citation share, sentiment, prompt coverage

Extraction mechanics

What makes a passage citable

Five properties. A page missing any one of them tends to get read and then passed over.

Step 1: Direct answer first
The answer sits in the first paragraph under the heading, phrased as a complete sentence that survives being lifted out of the page.
Step 2: Question-shaped headings
Headings match how people ask, not how marketers write. An assistant matching a prompt to a passage finds the question already written.
Step 3: Schema that agrees with the page
FAQPage, QAPage, Article, and Organization markup rendered server side and consistent with the visible text. Disagreement gets the markup ignored.
Step 4: Entity clarity
Consistent name, address, phone, sameAs references, and author credentials so the model can resolve who is speaking and why it should trust them.
Step 5: Crawler access
GPTBot, ClaudeBot, PerplexityBot, and Googlebot allowed explicitly, content delivered server rendered, and llms.txt published as a machine-readable front door.

Straight answers

The three questions every prospect asks about AEO

Which AI assistants matter for a Denver business?

ChatGPT, Google AI Overviews, Perplexity, and Gemini account for the large majority of assistant-driven research in the United States today. Claude matters for technical and professional categories. We baseline all of them and prioritize by where your buyers actually ask.

Does AEO help local businesses?

Yes, and often more than national brands. Assistants answering local questions lean on consistent entity data, reviews, and genuinely local pages. Those are the same signals a local SEO program builds, which makes local businesses unusually well positioned.

What is llms.txt and do we need one?

It is a plain text file at the root of a site that summarizes what the organization does and points to the pages worth reading. It is a low-cost front door for assistant crawlers. We publish and maintain one on every site we build.

Can we do AEO without changing the website?

Partially. Content restructuring and schema can be added to most platforms. What cannot be worked around is a site that only renders in the browser, because many assistant crawlers never execute JavaScript and see an empty page.

Answer library

The questions this program is built to win

Every one of these is a live page on this site, written answer first so an assistant can lift it whole. That is the format we build for clients.

Cross-pillar

AEO sits on top of the rest of the program

Citations are downstream of structure, speed, and proof. These four connections carry most of the weight.

Assistants lean heavily on pages that already rank, so the topical and authority work in SEO is the substrate AEO builds on rather than a separate track.

Passages only exist for a crawler when the HTML arrives complete, which is exactly what application frontends guarantee with server rendering on the first request.

Running a prompt set every month by hand does not scale, so monitoring is automated under AI and automation and the results land in a dashboard instead of a spreadsheet.

Assistant referrals are small in volume and high in intent, so they need the attribution built in analytics or they get miscounted as direct traffic and dismissed.

Models corroborate entities across the open web, which makes the presence and consistency delivered by social part of how a brand resolves as trustworthy.

Questions

AEO questions we get asked

Find out who the assistants cite in your category today

We run your prompt set and show you the answer before anything is signed.

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