Your buyers are asking ChatGPT, Claude, Gemini and Perplexity which vendor to shortlist — before they ever reach your website. Generative Engine Optimization is the work of making sure your brand is the answer they get.
Generative Engine Optimization (GEO) is the practice of structuring a brand's information, content and third-party footprint so that large language models — ChatGPT, Claude, Gemini, Copilot and Perplexity — retrieve it, understand it correctly, and name the brand when a user asks for a recommendation.
It differs from SEO in what it optimises for. SEO competes for a position in a ranked list of links that a user then clicks. GEO competes to be included in a generated answer that a user reads instead of clicking. There is no results page, no position one, and no keyword bid — the assistant either mentions you or it does not.
The mechanics differ too. Ranking algorithms weigh links, relevance signals and page authority. Generative models work from a combination of what they absorbed in training and what they retrieve live, then synthesise. That makes GEO depend on entity clarity — is it unambiguous who you are — factual consistency — does every source agree — retrievability — can crawlers reach and parse your content — and third-party corroboration — does anyone other than you say it.
For B2B this matters because a growing share of vendor shortlisting now happens inside an assistant conversation rather than a search results page. A buyer who asks which agencies run account-based marketing for SaaS companies receives three to five names. If yours is not among them, the buyer never learns you exist — and no amount of Google ranking corrects that.
Frequently discussed as one discipline with three names. They are not.
| Dimension | SEO | AEO | GEO |
|---|---|---|---|
| Where it appears | Google and Bing results pages | AI Overviews, Perplexity, Copilot | ChatGPT, Claude, Gemini conversations |
| Buyer behaviour | Types a query, clicks a link | Reads a summary, often does not click | Asks for a recommendation, receives names |
| Unit of success | Ranking position, organic sessions | Citation in the answer box | Mention rate across a prompt set |
| Primary lever | Relevance, authority, backlinks | Structure, schema, direct answers | Entity clarity and third-party corroboration |
| Results are | Broadly stable and reproducible | Fairly stable, query-dependent | Non-deterministic — vary by user and session |
| Time to impact | 3–6 months | 6–12 weeks | 8–16 weeks |
| Can you pay for placement | Yes, Google Ads | Not directly | Not in the answer, only ad slots beside it |
The three share infrastructure — clean architecture, schema, fast pages, credible content — which is why running them together costs far less than running them separately. The tactics that win each one are genuinely different.
A baseline prompt set of 30–100 buyer questions run across ChatGPT, Claude, Gemini and Perplexity, recording who gets named and what is said about you.
One canonical fact set, propagated across site copy, schema, LinkedIn, directories, review platforms and Wikidata where warranted.
Organization, Service, FAQPage, Article, BreadcrumbList and Person markup deployed site-wide and validated.
Existing pages rewritten definition-first with question-shaped headings, fact tables and comparison tables models can extract cleanly.
Deep, citable reference pages on the questions your buyers actually ask — assets that earn citations rather than chase keywords.
robots.txt, llms.txt, CDN and firewall rules audited so GPTBot, ClaudeBot, PerplexityBot and Google-Extended can reach your content.
Directory listings, review platform profiles, podcast and publication placements that create independent references to your entity.
Prompt set re-run monthly: mention rate, sentiment, competitor share of voice, and assistant referral traffic in GA4.
Scoped rather than list-priced, because the work depends almost entirely on the state you start from.
| What drives the cost | Lower effort | Higher effort |
|---|---|---|
| Starting state | Schema exists, facts consistent, crawlers allowed | No schema, contradictory facts, crawlers blocked |
| Site size | Under 30 pages | 100+ pages needing restructure |
| Entity footprint | Few off-site listings to correct | Years of inconsistent directory and profile data |
| Content production | Strong existing content to restructure | Cornerstone guides written from scratch |
| Prompt set size | 30 prompts, two assistants | 100+ prompts, four assistants, multiple markets |
| Corroboration | Already listed and reviewed in your category | Building third-party presence from nothing |
Every engagement starts with a free AI visibility audit. You get the baseline number and the ranked fix list whether or not you hire us — several companies have taken that list and executed it in-house, which is a legitimate outcome.
This works well in some situations and badly in others. Here is an honest filter before you commit budget.
GEO is the practice of structuring a brand's content, data and third-party footprint so AI assistants such as ChatGPT, Claude, Gemini and Perplexity retrieve it, understand it correctly, and name the brand when a user asks for a recommendation. Where SEO competes for a position in a list of links, GEO competes to be included in a generated answer the user reads instead of clicking.
AEO targets answer engines that summarise and cite sources — Google AI Overviews, Perplexity, Copilot — and the win condition is having content extracted and cited. GEO targets conversational assistants where the win condition is your brand being named in a recommendation, often with no link at all. AEO is largely about content structure; GEO adds entity consistency and third-party corroboration on top.
Eight to sixteen weeks for retrieval-based visibility. Fixes that affect live retrieval — crawler access, schema, factual corrections, new cornerstone content — can register within four to eight weeks. Changes that depend on a model's training data take far longer and cannot be scheduled, because they only take effect at the next training cycle, which no vendor controls.
No, and neither can anyone else. There is no ranking to buy, no index to submit to and no placement to secure. What can be committed to is measurable improvement in mention rate across a fixed prompt set, tracked monthly. If a vendor guarantees inclusion in AI answers, they are describing something that does not exist.
No. Traditional search still drives the majority of B2B discovery traffic, and generative engines lean heavily on the same web content that ranks well organically. GEO is additive: it protects you in the research layer where an increasing share of shortlisting now happens. The two share infrastructure, so running both costs considerably less than running them separately.
Through a fixed prompt set. We build thirty to a hundred questions your buyers actually ask, run them monthly across four assistants, and record whether you were named, which competitors were named, whether your URLs were cited, and whether what was said about you was accurate. We also segment GA4 referral traffic from assistant domains.
The correction path is to publish the accurate version prominently, mark it up in schema, and get it corroborated by independent third-party sources until retrieval favours the correct information. There is no delete button and no support ticket. This is why factual consistency across your own properties is the first thing we fix — most inaccuracies originate from a contradiction the brand published itself.
Yes, if you want to be retrieved. GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot and Google-Extended need permission in robots.txt and must not be blocked at CDN or firewall level. A surprising number of sites block them unintentionally through a security plugin default and never discover it. This is the first item in every audit because it is free to fix and total in its effect.
llms.txt is a proposed convention — a plain-text file at your site root listing important pages in a form easy for language models to parse. Adoption by major model providers is not universal or guaranteed. We implement it because it takes under an hour and costs nothing, but we do not present it as a significant lever. Crawler access, schema and factual consistency matter far more.
Figures and claims on this page are drawn from FlairLytics client engagements and verified platform documentation. Content is reviewed on a fixed cycle and updated when the underlying facts change.
Wins citations inside AI Overviews and Perplexity. Shares infrastructure with GEO.
Still the largest source of B2B discovery traffic and the content foundation GEO retrieves from.
Paid placement in ChatGPT conversations alongside the organic recommendation.
Third-party corroboration is a GEO lever. PR and thought leadership create the references models look for.
Entity clarity starts with knowing exactly what you are and who you serve.
AI-referred visitors arrive further along in their research and need pages that convert a warmer reader.
We will build a prompt set for your category, run it across ChatGPT, Claude, Gemini and Perplexity, and send you the baseline — who gets recommended, where you appear, and what the models get wrong about you.
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Data-Driven B2B Growth Marketing. Elevate Lead Generation, ABM & Demand Generation
