AEO and GEO Agency: Get Cited by AI Search, Not Skipped.
AI-SEO, Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), and LLM Optimization (LLMO) are four names for one discipline. The search results page isn't a list anymore. It's an answer. Sequence's operating model is built for that shift across Google AI Overviews, Google AI Mode, ChatGPT, Perplexity, Claude, Microsoft Copilot (formerly Bing Chat), and Gemini. It's the framework behind Lululemon's $1.3B+ five-year growth engine.
Why AI answers are replacing the ten blue links.
Google AI Overviews, ChatGPT Search, Perplexity, and Claude have changed what winning search means. The ten blue links now compete with a synthesized response generated above them, and they frequently lose.
AI-SEO, AEO (Answer Engine Optimization), GEO (Generative Engine Optimization), and LLMO (LLM Optimization) describe the same discipline under four different names. All four mean optimizing content to be cited, quoted, and recommended by AI answer engines. The playbook is the same whichever term the buyer searches:
- Structure content so large language models (LLMs) can lift clean, attributable passages.
- Build entity authority so models recognize the brand as canonical.
- Scale long-tail content so the engine has something quotable for every conversational query.
This discipline targets seven AI engines: Google AI Overviews, Google AI Mode (formerly SGE), ChatGPT Search, Perplexity, Anthropic Claude with web search, Microsoft Copilot (formerly Bing Chat), and Google Gemini. Each engine retrieves and synthesizes differently. The signals that make a source citation-worthy are consistent across all of them.
The brands that show up in that answer, get cited in it, or own the source pages it pulls from are the ones capturing the demand. Brands that don't get cited become invisible on every query that gets answered without a click. Traditional SEO optimizes for the link list. AI-SEO optimizes for the answer.
| Dimension | Traditional SEO | AI-SEO · AEO · GEO · LLMO |
|---|---|---|
| Target | Rank #1 to 10 in the blue links | Get cited or quoted in the AI response itself |
| Query type | Short keyword strings ("running shoes") | Conversational full-question prompts ("best lightweight running shoes for flat feet under $150") |
| Content unit | Pages optimized for a primary keyword | Atomic, quotable passages a model can lift cleanly |
| Authority signal | Backlinks and domain rating | Entity recognition, brand mentions in training data, schema-defined relationships |
| Measurement | Rank position and organic clicks | Citation share, AI Overview presence, branded mention frequency in LLM outputs |
| Cost of doing nothing | Slow erosion of rankings | Invisible on every query that gets answered without a click |
Which AI engines does Sequence optimize for?
Whatever the buyer searches, the work is the same: make the brand the answer in every generative engine. Each engine retrieves and synthesizes differently. Our framework covers all seven.
Google AI Overviews
The synthesized answer that appears above the ten blue links. Source citation is the goal. Built on Gemini, served to Google Search users globally.
Google AI Mode
The full conversational AI search experience inside Google. Replaced SGE (Search Generative Experience) as Google's flagship AI search surface.
ChatGPT Search
OpenAI's web-connected search. It pulls from real-time retrieval and GPT training data. Citation chips appear inline with the synthesized answer.
Perplexity
Perplexity is citation-first by design: every answer ships with numbered source attributions. Every Perplexity answer carries numbered citations. Google rank is the strongest input to Perplexity source selection.
Anthropic Claude
Claude with web search retrieves from current web indexes. Strong source attribution. Claude has strong enterprise adoption, so Claude citations matter on B2B queries.
Microsoft Copilot (formerly Bing Chat)
Microsoft's GPT-powered search across Bing, Edge, and Windows. Visible citation footnotes on every answer. Often surfaces sources Google AI Overviews misses.
Google Gemini
Google's standalone conversational AI (gemini.google.com). It's a different surface from AI Overviews, but the citation signals are the same. It matters most on Workspace and Android queries.
SGE / Legacy Reference
Search Generative Experience (SGE) was Google's pre-AI-Mode beta. Many brands still benchmark against SGE-era data. Our framework covers the migration from SGE-baselined content to AI Mode and AI Overviews.
How does Sequence's AI-SEO model work across industries?
Every AI-SEO engagement rests on three principles. Industry doesn't change them. A travel agency, a home-services brand, and a global retailer all need the same capability stack. What changes is the catalog, the query landscape, and the speed at which we can ship. The principles stay.
Citation-ready content
We structure content so an LLM can lift a clean answer without ambiguity: bullet definitions, comparison tables, and explicit attributions. Be the source AI quotes, not just a result it skips.
Entity authority
Entity authority comes from schema markup, consistent brand mentions on high-trust domains, and structured data that lets models recognize a brand as the authoritative entity for a category, not just a keyword match.
Long-tail at scale
Conversational AI rewards depth. We build dynamic content that targets the full question, not just the keyword. We scale it across the catalog so the long tail compounds instead of fragments.
What are the three pillars of AI-SEO?
Foundations first. Then growth. Then competitive advantage.
Technical
Technical work lets search engines and LLM crawlers discover, crawl, and index every page correctly in every market. It's the plumbing that lets every other layer compound.
- Core Web Vitals & site health
- Hreflang & locale accuracy
- Index coverage & crawl optimization
- Redirect chain remediation
- JS/CSS bloat reduction
Content
Content drives what the site ranks for and what AI answer engines cite. Targeting strategies built around full-funnel search intent, from head terms to long-tail conversational queries.
- Head, mid & long-tail targeting
- Category, guide & dynamic pages
- Full-funnel content architecture
- Citation-ready block production
- FAQ & HowTo schema at scale
Authority
Authority builds competitive credibility. It covers the signals that reinforce relevance in key categories and the entity work that makes LLMs treat a brand as the canonical source.
- Domain authority building
- Content credibility signals
- Competitive share of voice
- Entity schema (Organization, Brand)
- High-trust domain mention strategy
$1.3B+ in incremental organic revenue over five years.
Lululemon turned a brand-dependent search profile into a full-funnel growth engine. In that engagement, long-tail content at scale was the single largest revenue lever.
Brand was the entire engine. It was also the entire problem. Only 2% of organic traffic came from non-brand search. 5.2M international visitors annually landed on the wrong regional site. Long-tail coverage was X% of the average category leader's. The top line looked healthy on a brand-search graph, so nobody was talking about the ceiling.
The AI-SEO framework travels across industries.
A $200M travel agency competes for "best 7-day Italy itinerary under $4K." A $75M landscaping company competes for "when to overseed a fescue lawn in zone 7a." Lululemon competes for "womens size 6 black yoga leggings." All three are long-tail, conversational, intent-rich queries that AI systems answer directly. The technical and content infrastructure required to win those queries is the same regardless of vertical.
Finding the leaks before pouring more traffic in.
A $200M travel agency had strong brand authority and hidden technical debt that capped the organic ceiling on every high-intent booking query. 98% of organic traffic was branded. Beneath a clean front-end, the technical stack was leaking equity through redirect chains, broken links, and Core Web Vitals failures. Non-brand visibility was effectively zero, and the site told Google it was harder to crawl than it needed to be.
From audit to action in 90 days.
A $75M landscaping company had seasonal demand spikes and a website that couldn't capture them. Strong brand authority and a healthy referring-domain profile were masking technical debt and an on-page strategy that hadn't evolved with the catalog. Organic was performing despite the site, not because of it. We turned a 40-page audit into a sprint plan engineering could ship before peak season.
Eight disciplines. One AI-SEO team. No black boxes.
Everything required to fix the foundation, scale the content engine, and build entity authority that AI answer engines recognize, all under one engagement.
Technical AI-SEO Audits
Full technical health audit against our AI Overview (AIO) readiness rubric: Core Web Vitals, redirect chains, broken links, JS/CSS bloat, schema coverage, hreflang accuracy, index coverage, and crawl efficiency. Every issue is scored by severity and sequenced by ROI, so engineering ships the highest-equity fixes first.
- 19+ point technical health checklist scored 0 to 2 per signal
- Severity ordering: Critical → High → Medium → Low, with quick wins flagged separately
- 30/60/90 sprint plan with engineering hour estimates
- Pre-engagement baseline measurement for delta tracking
- Weekly dashboard from Day 1, not quarterly surprises
Citation-Ready Content
FAQ blocks, definition paragraphs, comparison tables, and direct-answer openings. Production-ready content that LLMs can lift cleanly.
FAQ · Definition · CompareSchema and Entity Authority
FAQPage, HowTo, Article, Organization, and Service schema. Entity-defining structured data that makes brands canonical in their category.
JSON-LD · EntityLong-Tail Content at Scale
Dynamic page architecture targeting the full conversational query, not just the keyword. It's the model behind Lululemon's $116M long-tail line.
Dynamic · Long-tailInternational and Hreflang
Locale routing, regional content variants, and hreflang implementation. The Lululemon 5.2M-user re-routing playbook.
i18n · HreflangCore Web Vitals and Performance
Largest Contentful Paint (LCP), Interaction to Next Paint (INP), and Cumulative Layout Shift (CLS) optimization. Code-splitting, PurgeCSS rollout, image optimization, and render-blocking removal. Page experience signals feed the ranking that AI answer engines retrieve from.
LCP · INP · CLSLLM Citation Monitoring
We track AI Overview presence, ChatGPT citations, Perplexity source share, and Claude attributions for branded and non-branded queries, at keyword-cluster level, every month.
AIO · ChatGPT · PerplexityExecutive Reporting and QBRs
Weekly tactical dashboard, monthly deep dives, and quarterly executive business reviews against the multi-year arc. Measured monthly, not annually, so quarterly business reviews aren't where the surprises happen.
QBR · Dashboard · MonthlyWhat does an AI-SEO engagement look like?
Audits without execution timelines die in a shared drive. Every engagement is framed as 30/60/90 because it forces three things: prioritization, accountability, and a measurable Day 30 delta the client can see.
Audit
Full AI-SEO audit against the readiness rubric: technical health, content architecture, citation-readiness, entity authority signals, and the LLM citation baseline. Severity-scored remediation plan. No fluff.
Plan
30/60/90 sprint plan with engineering hour estimates per fix. ROI-sequenced: redirect chains and Core Web Vitals first (highest equity per dev hour), then on-page citation blocks, then long-tail content engine, then entity authority.
Ship
Foundation fixes go live in Days 1 to 30. Content engine activation Days 31 to 60. Long-tail dynamic pages and authority work Days 61 to 90. Weekly dashboard from Day 1, not quarterly surprises.
Compound
Phased, multi-year arc. Monthly reporting against the long-term plan. Quarterly business reviews. The Lululemon arc runs 2022 to 2027 because real AI-SEO growth compounds over years, not quarters.
AI-SEO and GEO: your questions answered
What is AI-SEO?
What is AEO (Answer Engine Optimization)?
What is Generative Engine Optimization (GEO)?
Are GEO, AEO, AI-SEO, and LLMO the same thing?
How do I optimize for Google AI Mode?
- Direct-answer paragraphs in the first 100 words
- FAQPage schema with 5+ Q&A pairs
- Comparison tables that name both entities being compared
- Proprietary stats per major section
- Entity-authority schema (Organization with sameAs, award, founder, and aggregateRating)
How do I rank in Perplexity, Claude, and Microsoft Copilot (formerly Bing Chat)?
What is the difference between SEO and AI-SEO?
How do I get cited in Google AI Overviews?
- Open every page with a direct-answer paragraph that defines the topic in 40 to 70 words.
- Rephrase H2s and H3s as question-intent headers ("What is X", "How does X work", "X vs. Y").
- Ship FAQPage and HowTo schema with 5+ Q&A pairs per page.
- Build comparison tables that name both entities being compared in the header row.
- Add at least one proprietary stat, data point, or named entity per major section.
How do I get cited in ChatGPT, Perplexity, and Claude?
What is entity authority for AI search?
sameAs social profiles, award, founder, and aggregateRating fields. LLMs use this structured data to disambiguate brands and decide who to cite.Does AI-SEO work for ecommerce?
How long does AI-SEO take to show results?
Does Sequence work with enterprise brands?
How is Sequence different from a traditional SEO agency?
Three problems. One operating model. Let's audit yours.
A leaky technical foundation. An audit gathering dust without a sprint plan. A brand-dependent traffic profile that nobody is treating as a structural risk. We've shipped against all three. The work is reproducible because the framework is. Technical first. Content as the growth engine. Authority as the moat. Measured monthly, not annually.
Let's audit yours →