AI SEO is the umbrella discipline of optimizing a brand for AI-driven discovery. GEO, AEO, LLM SEO and Agentic SEO are four narrower disciplines inside it, each targeting a different piece of the AI stack. At WebFlur we ship all four for B2B clients — this pillar breaks down what each one actually is, how they overlap, and how to pick the one to start with.
The category is 24 months old, the vocabulary is still stabilising, and every second LinkedIn post uses two of these acronyms as synonyms. They aren't. Below is the working taxonomy we use in every WebFlur audit — starting from the definitions, then the comparison table, then the decision framework.
How it works
AI SEO is the broadest category: any work that makes a brand more findable, parseable, or citation-eligible by an AI system. It contains GEO, AEO, LLM SEO, and (as a delivery model) Agentic SEO. In practice, when someone says "we need AI SEO," they usually mean the whole stack — schema, answer-first content, entity anchoring, a2a endpoints — layered on top of a working traditional SEO baseline.
What it targets
Every AI surface a buyer might touch: ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, Google AI Mode, voice assistants, and internal AI procurement agents your buyers may run themselves. AI SEO is the bucket; the specific engines are the targets.
How to start
Do the shared foundation first — the same infrastructure lifts every engine. That means: FAQPage + Article + HowTo JSON-LD schema, a ≤60-word "X is Y" opener on every page, Organization sameAs URIs pointing to LinkedIn / Wikipedia / Wikidata, and an AI-permissive robots.txt. Only then pick the specific discipline (GEO or AEO or Agentic SEO) to layer.
How it works
Agentic SEO is not a separate optimization target — it's how the work gets done. A planner agent proposes changes, an executor agent ships them, a verifier agent checks whether ChatGPT and Perplexity started citing the page, and the loop iterates. When people say "AI-assisted SEO" they mean a human clicks approve between steps. When they say "Agentic SEO" they mean the agent runs to completion without permission gates.
What it targets
The same targets AI SEO targets — this is a delivery model, not a channel. It only pays off at a scale where per-page human tuning stops fitting the calendar: 20+ pages a week, programmatic SEO at 500+ URL scale, always-on citation monitoring across six engines, or continuous schema-drift correction.
How to start
Do not adopt Agentic SEO as your first move. It multiplies the quality of your existing content architecture — it does not create quality from nothing. Get the foundation right (schema, answer-first structure, entity anchoring) manually first. Then introduce one narrow agent loop — usually citation monitoring or the schema-audit-and-fix loop — with an explicit human review gate for the first quarter. Our companion piece what is Agentic SEO and how does it actually work is the deeper dive.
How it works
GEO is the narrow discipline of getting a brand cited inside LLM-generated answers — ChatGPT, Perplexity, Claude, Gemini. The academic root is the 2023 arXiv paper defining GEO. Where traditional SEO optimizes for a rank position, GEO optimizes for source selection inside a synthesised answer. Two different games.
What it targets
Any LLM-native surface: ChatGPT (retrieval + training data), Perplexity (real-time fetch), Claude (structure-weighted), Gemini (Google ecosystem). GEO is the shared discipline; the per-engine mechanics are the specialisation, broken down in our pillar guide how to get cited across all six AI answer engines.
How to start
Ship the three-schema stack (FAQPage + Article + HowTo), rewrite every page with a ≤60-word definitional answer at the top, and populate your Organization schema's sameAs with LinkedIn + Wikipedia + Wikidata URIs so the model can disambiguate your brand as an entity. Then push external citation density on G2, Capterra, and 2–3 industry publications — LLM engines weight source frequency across the open web.
How it works
AEO optimizes for surfaces that render a single synthesised answer, not a ranked list — Google AI Overviews, Google AI Mode, voice-assistant answers, featured snippets. Where GEO tries to get named inside a source list, AEO tries to be the passage that gets extracted as the answer itself. The mechanics are closer to featured-snippet optimization than to classic SEO — chunk-extractable answer blocks, question-shaped H2s, tight schema.
What it targets
Google AI Overviews, Google AI Mode, Alexa, Google Assistant, Siri. Since the September 2026 AI Overviews auto-expand rollout — covered in our breakdown of the auto-expand change — AEO is by far the highest-volume AI SEO surface for B2B queries.
How to start
Bump sitemap.xml <lastmod> on every content edit (AIO reads it directly). Ship the three-schema stack. Phrase every H2 as a complete user question. Break every H2 section into a self-contained 75–150 word answer block. Ranking positions 4–8 in Google Search regularly out-cite positions 1–3 in AIO if the lower-ranked pages have answer-first structure and the top-ranked pages bury the answer under a story.
How it works
LLM SEO is a synonym for GEO. Some practitioners use LLM SEO to emphasise that the true optimization target is the model's training-data corpus (which decides what ChatGPT knows about your brand before any retrieval) rather than the runtime retrieval pipeline. Whether you use LLM SEO or GEO, the mechanics are the same. Do not waste time arguing about the label — pick one and be consistent.
What it targets
Same as GEO: any LLM-native answer engine.
How to start
Same playbook as GEO. The one nuance if you use the LLM SEO framing: prioritise being cited by the sources LLMs train on — Wikipedia, arxiv, GitHub README files, well-crawled industry publications — because that shapes what the model "knows" about you before a single query is run.
Side-by-side comparison — targets, signals, tooling
The five terms above are not a hierarchy and not synonyms. They overlap on foundation work (roughly 70%) and diverge on the specific engines they target. This is the WebFlur cheat-sheet we use in every audit to decide which discipline gets emphasis.
| Discipline | Primary target surface | Strongest lever | Delivery model | Relationship to classic SEO |
|---|---|---|---|---|
| AI SEO | Every AI-driven surface (umbrella) | Shared foundation (schema + answer-first) | Human-driven, optionally AI-assisted | Built on top; doesn't replace |
| Agentic SEO | Same as AI SEO (delivery model, not target) | Autonomous multi-agent loops | Autonomous AI agents with human guardrails | Multiplies existing quality — needs baseline |
| GEO | ChatGPT, Perplexity, Claude, Gemini | Third-party citation density + schema | Human-driven, AI-assisted at scale | Adjacent — different game, different metrics |
| AEO | Google AI Overviews, AI Mode, voice | Answer-first structure + schema stack | Human-driven, template-heavy | Deep dependency — AIO reads Google's index |
| LLM SEO | Same as GEO | Training-data corpus inclusion | Human-driven, corpus-focused | Adjacent — same as GEO |
The single most important read from the table: only two of the five (GEO and AEO) are true target-facing disciplines. AI SEO is the umbrella. Agentic SEO is a delivery model. LLM SEO is a synonym for GEO. That mental model alone eliminates the confusion in most vendor conversations.
Step-by-step: which discipline should you start with?
The answer depends on where your buyers actually are. The framework below is the 6-step decision sequence we walk every WebFlur prospect through on the first call — ordered by ROI.
- Start with your buyer's channel, not the buzzword. Ask 5 recent customers where they first heard your name. If it was ChatGPT or Perplexity, GEO comes first. If it was AI Overviews on Google, AEO. If it was an internal AI procurement agent doing vendor shortlisting, Agentic SEO readiness (A2A endpoint, machine-readable positioning). Skip the labels and follow the answer channel.
- Audit your existing SEO baseline. AI SEO builds on traditional SEO — it does not replace it. If your site does not rank in Google Search for its own brand name, fix the classic SEO fundamentals first (site speed, canonical, sitemap, on-page structure). Every AI surface is a downstream consumer of that baseline.
- Ship the shared foundation. Regardless of which discipline you prioritize, four things are required across all of them: FAQPage + Article + HowTo JSON-LD schema, a definitional ≤60-word first paragraph on every page, entity anchoring via Organization
sameAs, and a permissive robots.txt for AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot). - Layer GEO on top for LLM-native answers. If your buyers use ChatGPT / Perplexity / Claude / Gemini, GEO is the layer. Add per-H2 extractable 40–80 word answer blocks, tighten your entity mentions, push external citation density (G2, Capterra, 2–3 industry publications).
- Layer AEO on top for Google surfaces. If your buyers use Google (any surface), AEO is the layer. Bump
sitemap.xml<lastmod>on every content edit, ship the three-schema stack, phrase every H2 as a complete user question, and treat AIO's answer-first extraction as the primary optimization target. - Add Agentic SEO when scale demands it. Autonomous agents make sense once you have 20+ pages needing per-week attention, or once you need to publish across 50+ programmatic pages. Do not adopt it as your first move; it multiplies existing content quality, not creates it. Start with one narrow agent loop (citation monitoring is the usual first target) with a human review gate.
Cargoflow Inc. went from zero AI citations to 340+ per month in 90 days by running exactly this sequence: shared foundation first, GEO second (Perplexity picked up within a week), AEO third (AIO followed the schema deploy), Agentic SEO fourth (citation monitoring loop only, at day 60). The full breakdown is in the Cargoflow case study.
Is traditional SEO dead now that AI search exists?
No — but its role has changed. Traditional SEO is now the foundation layer. AI SEO is the extraction layer that sits on top.
Google AI Overviews, AI Mode and Gemini all pull from Google's index. A page that does not rank in traditional Google Search will not appear in AI Overviews either — the index is the source. So classic SEO fundamentals (crawlability, canonicalisation, sitemap hygiene, on-page structure, link authority) are still load-bearing. What has changed is that ranking alone is no longer enough. A ranked page also has to be extractable, entity-anchored, schema-marked, and answer-first for a click to convert into a citation.
The buyer piece of this is where the shift is starkest: buyers now form their vendor shortlist inside AI assistants before opening a browser tab. We covered the mechanics in how buyers are already using AI to choose vendors. If your brand isn't cited inside the AI's answer, you don't get evaluated — no matter what your Google rank is.
The vocabulary here will shift again within 12 months. New engines (like GPT-6 Astra and Ox Alpha) reshape citation dynamics faster than the labels can settle. The underlying mechanics — extractable content, entity clarity, schema — hold. The acronyms will change.
How WebFlur combines all five in practice
In a typical WebFlur engagement, all five disciplines get sequenced together over a ~90 day arc. The pattern:
- Weeks 1–2: Baseline audit across AI SEO surfaces + fix classic SEO gaps.
- Weeks 3–4: Ship the shared foundation — three-schema stack, answer-first opener on every page, Organization
sameAs, AI-permissive robots.txt. - Weeks 5–6: Layer GEO — external citation density, per-H2 answer blocks, per-engine tuning.
- Weeks 7–8: Layer AEO — sitemap discipline, question-shaped H2s, AIO citation verification.
- Weeks 9–12: Layer Agentic SEO where scale demands it — one narrow agent loop first, expand only after human review shows the loop holds.
The full 30-day sprint version — condensed for teams that need to ship faster — is documented in the 30-day agentic presence sprint. That piece walks through the exact weekly deliverables for a compressed timeline.
"We need to pick one." — no, you need to sequence them. The five disciplines are complementary, not competing. Picking one and ignoring the others is how sites end up cited in Perplexity but invisible in AI Overviews (GEO without AEO) or vice versa.
- arXiv — Generative Engine Optimization: The 2023 academic paper that first defined GEO as a discipline.
- Google — AI Overviews documentation: Google's own guidance on how AI Overviews select and cite source content.
- Schema.org — FAQPage: The structured-data specification behind the FAQ layer that AI answer engines extract.
