--- title: "AI SEO, Agentic SEO, GEO & AEO — *what they are and how they work*" canonical_url: https://webflur.com/blog/ai-seo-vs-geo-vs-aeo last_updated: 2026-10-15 author: Pankaj description: "AI SEO, Agentic SEO, GEO, AEO and LLM SEO explained — what each discipline is, how they overlap, and how to pick the right one for B2B in 2026." --- # AI SEO, Agentic SEO, GEO & AEO — *what they are and how they work* **Last updated:** Oct 15, 2026 **Author:** Pankaj — Founder, WebFlur **Canonical URL:** [https://webflur.com/blog/ai-seo-vs-geo-vs-aeo](https://webflur.com/blog/ai-seo-vs-geo-vs-aeo) --- [Back to Blog](../blog.html) AI SEO Foundations · Pillar # AI SEO, Agentic SEO, GEO & AEO — *what they are and how they work* The five disciplines every B2B founder confuses — AI SEO, Agentic SEO, GEO, AEO, LLM SEO — defined side-by-side, with a comparison table and a decision framework for picking the right one. Cross-engine Umbrella term Vocabulary still stabilising #### 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. AS Agentic SEO A delivery model — autonomous AI agents running the SEO loop with minimal human input Autonomous execution Multi-agent orchestration Requires strong human guardrails #### 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](/blog/what-is-agentic-seo) is the deeper dive; the sibling breakdown [AI-assisted SEO vs Agentic SEO — the autonomy line](/blog/ai-assisted-vs-agentic-seo) walks through the 6-dimension comparison and the 5-criteria scorecard we use to decide which delivery model actually fits. The mechanics of the loop itself — perceive, plan, act, verify, log — are unpacked in [how AI agents autonomously execute SEO workflows](/blog/how-ai-agents-execute-seo-workflows), with a real pseudoflow and the 7-step handoff protocol we run before wiring any agent to a live site. GEO GEO — Generative Engine Optimization The discipline for getting cited inside LLM-native answer engines LLM-native Citation-explicit engines Third-party density heavy #### 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](https://arxiv.org/abs/2305.11206). 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](/blog/how-perplexity-claude-chatgpt-cite). #### 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. AEO AEO — Answer Engine Optimization The discipline for getting extracted into answer-box formats Answer-box formats Passage extraction driven Schema-sensitive #### 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](/blog/google-auto-expanding-ai-overviews) — AEO is by far the highest-volume AI SEO surface for B2B queries. #### How to start Bump `sitemap.xml` `` 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. LLM LLM SEO A synonym for GEO with the emphasis on training-data inclusion Same as GEO Training-data emphasis Term in flux #### 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. For the dedicated deep-dive on this training-corpus vs retrieval split and the six corpus-side ranking signals from our 45-site B2B audit, see [What is LLM SEO? The 2026 corpus-vs-retrieval guide](/blog/what-is-llm-seo). ## 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. 1. **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. 2. **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. 3. **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). 4. **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). 5. **Layer AEO on top for Google surfaces.** If your buyers use Google (any surface), AEO is the layer. Bump `sitemap.xml` `` 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. 6. **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. Case study — what this looks like at scale 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](/blog/cargoflow-ai-citations-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](/blog/buyers-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 honest hedge The vocabulary here will shift again within 12 months. New engines (like [GPT-6 Astra](/blog/gpt-6-astra) and [Ox Alpha](/blog/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: 1. **Weeks 1–2:** Baseline audit across AI SEO surfaces + fix classic SEO gaps. 2. **Weeks 3–4:** Ship the shared foundation — three-schema stack, answer-first opener on every page, Organization `sameAs`, AI-permissive robots.txt. 3. **Weeks 5–6:** Layer GEO — external citation density, per-H2 answer blocks, per-engine tuning. 4. **Weeks 7–8:** Layer AEO — sitemap discipline, question-shaped H2s, AIO citation verification. 5. **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](/blog/30-day-agentic-presence-sprint). That piece walks through the exact weekly deliverables for a compressed timeline. Common misconception "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. Sources & further reading - [arXiv — Generative Engine Optimization](https://arxiv.org/abs/2305.11206): The 2023 academic paper that first defined GEO as a discipline. - [Google — AI Overviews documentation](https://developers.google.com/search/docs/appearance/ai-overview): Google's own guidance on how AI Overviews select and cite source content. - [Schema.org — FAQPage](https://schema.org/FAQPage): The structured-data specification behind the FAQ layer that AI answer engines extract. --- *This is the machine-readable Markdown alternate of [AI SEO, Agentic SEO, GEO & AEO — *what they are and how they work*](https://webflur.com/blog/ai-seo-vs-geo-vs-aeo). Written for AI agents (ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews) that prefer markdown over HTML. For the fully-designed reading experience visit the canonical URL.*