Generative Engine Optimization |Retrieve · Ground · Cite
A generative engine answers a question by retrieving a small set of sources it considers reliable, then writing an answer grounded in them. Every other page that could have answered is simply absent. GEO is the work of making your brand one of the sources that gets retrieved, understood correctly, and named.
Crawl › Retrieve › Ground › Cite › TrustEach stage decides whether a brand reaches the next one.
Live prompts across ChatGPT, Gemini, Perplexity and AI Overviews · yours to keep either way
What is GEO
A generative engine answers your question directly, in a few sentences, instead of handing you ten links to check yourself. To do that, it reads what you're asking, pulls a small set of sources it trusts, and writes an answer based on them. GEO is the work of making sure your brand is one of those sources: found, understood correctly, and named in the answer it gives.
It isn't optional anymore, because the click is disappearing. When the engine answers the question itself, a page that only ranks well doesn't show up lower down. It doesn't show up at all. GEO doesn't replace SEO: it's the same foundation, held to a stricter standard, plus the extra work that decides which eligible page actually gets named.
Same question. Two different kinds of visibility.
Not the same game
The two share a technical foundation and then part company on almost every measurable. Optimising well for one improves the odds on the other without settling them, which is why the two need related but separate strategies.
| Dimension | Traditional SEO | Generative engine optimization |
|---|---|---|
| Objective | Rank as highly as possible on the results page for a query. | Be named as a source inside the answer the model generates. |
| Success metric | Position, impressions and click-through rate. | Citation frequency and share of answer, measured per prompt and per platform. |
| Content built for | Keyword relevance, topical depth and earned links. | Extractable claims, unambiguous entities and verifiable experience signals. |
| Primary reader | A crawler, then a human choosing which link to open. | A language model assembling an answer on a human's behalf. |
| Where it appears | A link in a ranked list the user still has to select. | The answer itself, read before any link is considered. |
| Competitive set | Around ten results per query, and the set is visible to everyone. | Three to five cited sources, selected at the moment the answer is generated. |
The overlap is real. Clean crawling, fast rendering and precise structured data serve both, and work spent there is never wasted. The divergence is in what the work is aimed at: one competes for a position, the other competes to be the source an answer is built from.
Track record
The team behind Wegile DGTL has worked together for over a decade, running the complete marketing engine at its parent company, a software development company, and delivering campaigns for its clients before that. GEO is a young discipline and we treat it as one: the audit shows you a measured starting point rather than a projection, and every claim we make about movement is tied to a prompt, a date and a recorded answer.
Home Services · California · Landscape & Christmas Lighting · Landscaping · Turf Installation · Irrigation · Outdoor Lighting
ROMI in 2 years
ROAS (ad spend only)
Lower cost per lead
Lower customer acquisition cost
Elevated Seasons came to us after a previous agency spent their budget and delivered almost nothing. Sound familiar? We rebuilt the brand, launched Google Search and Performance Max, and ran the SEO strategy that took their revenue from a near standstill to consistent growth, still climbing two years in.
"I have been working with them for 2 years now and I am extremely satisfied with our working relationship. Communication has been clear, they are responsive and they follow through on what they say and do. They have been a crucial part in the growth of our business and we plan to stay partnered with them for many years to come."★★★★★ 5.0 · Nathan Murrell, Elevated Seasons
Digital Fitness & eCommerce · Meta Ads
Books sold
Time to sell out the run
Deliciously Fit is a digital recipe book with hundreds of high-protein recipes built for weight-loss and GLP-1 audiences. Our team ran the Meta Ads strategy behind the launch, selling 2,000 copies in under three months.
"I've been working with them for nearly 10 years, and they've been an incredible partner every step of the way. From developing my fitness app and nonprofit app, to helping maintain and grow my website, their team has consistently delivered with professionalism and precision. They're responsive, reliable, and deeply knowledgeable, always guiding projects with care and expertise. I trust them fully and plan to continue working with them for years to come."★★★★★ 5.0 · Chris Powell, Deliciously Fit
Startups · Brand Building & Content Strategy
Brand built from scratch
Positioning to content
Worth ranking for
An early-stage brand that needed a market identity before it needed more traffic. Our team leads the brand-building and content strategy work that gives the SEO and paid campaigns something worth ranking: positioning, market identity, and an SEO foundation built in from day one.
"[PLACEHOLDER QUOTE, REPLACE BEFORE PUBLISHING] Working with this team gave our brand a real identity and a clear direction, something we didn't have before. They understood our vision quickly and translated it into a strategy we could actually execute."★★★★★ 5.0 · Mihir, The Growth Alchemist
Scope of work
Six workstreams, run in the order the dependencies run. Entity work comes before content work, because a model that cannot reliably identify your brand has nothing to attach a claim to, and content published into that gap gets absorbed without attribution.
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Each workstream produces something inspectable: a prompt list, a schema diff, a rewritten passage, a citation log with dates against it. You should be able to see what changed, and see the answer that changed with it.
Platform coverage
A single optimisation does not transfer cleanly across all of them. Each platform has its own crawler, its own retrieval behaviour and its own tolerance for ambiguity, so each one is tested on its own terms rather than by proxy.
Retrieves through its own index alongside Bing, and favours sources whose claims are quotable without rewriting. GPTBot access and server-rendered HTML are the gating factors here.
Grounded largely in pages that already rank, so classical SEO carries directly. Googlebot-Extended access and passage-level clarity decide which of those ranking pages gets quoted.
Cites heavily and visibly, with a clear preference for recent, well-sourced pages. In practice the most responsive platform to structural, schema and freshness work.
Lean on entity understanding and corroboration across sources they already trust. Agreement between your own site and third-party references matters more here than volume of content.
Tooling
Not a performance claim, just the software behind the work, so you know what is running against your site. Automated findings are cross-checked against your own Search Console data and against manual prompt testing before they reach the fix list, because a citation report generated entirely by software tends to miss the answers a real user would see.
Our process
Five stages, run in that order. The order carries the work: restructuring content before confirming a platform can retrieve it produces better pages and no change in the answer.
Your priority questions run as live prompts across ChatGPT, Gemini, Perplexity and AI Overviews, recording who is cited on each one, which page was used, and which competitors appear alongside. The baseline is itself a deliverable: without a recorded starting point, later reporting has nothing to compare against.
How each platform's crawler actually sees the site, what the models currently believe the brand to be, and where those two disagree. Access rules, rendering and entity signals are corrected first, because content work applied on top of a blocked or misidentified site produces nothing measurable.
Key pages rewritten so each claim survives being extracted on its own, with heading structure and schema aligned to how the passages will be used. This is editing for machine comprehension, and it tends to improve the page for readers at the same time.
Credentials, authorship, sourcing and dates made explicit on the page, and the third-party references models already retrieve from brought into agreement with what the site says. A model resolves conflicting facts by trusting the source it knows better, so consistency is the work.
Scheduled rechecks across every platform in scope, reported at prompt level. Model updates move answers without notice, and a citation held last month is not a citation held today, so this runs as a monitoring discipline rather than a project with a finish line.
Want to know where you are cited today?
The audit runs your real buying questions across every platform in scope and reports exactly what comes back, whether you continue with us, hand it to your own team, or keep it on file.
GEO questions
The questions in-house SEO teams, content strategists and founders ask before commissioning generative engine optimization, answered plainly and in full.
It starts with a citation audit: your priority questions run as live prompts across ChatGPT, Gemini, Perplexity and Google AI Overviews, recording who is cited on each one and from which pages. That baseline is what everything afterwards is measured against.
From there the work runs in four parts. Retrieval access, so each platform's crawler can read your pages at all. Entity definition, so a model can identify your brand without guessing. Content and schema restructuring, so your claims survive being extracted from the page. Authority and corroboration, so the sources a model already trusts agree with what your site says. Citation tracking then runs on a schedule, because answers move when models update.
Ranking well helps, particularly with AI Overviews and AI Mode, which lean heavily on what already ranks. It is not sufficient on its own. Retrieval selects a handful of sources from the candidate set rather than ordering all of them, and the selection favours pages whose claims can be lifted cleanly and whose entity signals are unambiguous.
The common pattern is a site that ranks in the top three for a term and is absent from the generated answer for the same question, because a competitor's page states the relevant fact in a single extractable passage and theirs spreads it across four sections.
At prompt level. For each question in scope we record whether your brand is cited, whether it is named in the answer text as opposed to only linked underneath, which page was used, and which competitors appear alongside you. Tracked over time, that gives citation frequency and share of answer across each platform.
Downstream, branded search volume, direct traffic and referral traffic from AI platforms in GA4 give a partial view of the commercial effect. Referral attribution from generative engines is still incomplete across the industry, and we report it as an indicator rather than a settled number.
It depends on which layer the fix sits in. Retrieval and access corrections, such as opening a blocked user agent or server-rendering content that previously required hydration, can change what a platform is able to cite within its next crawl cycle. Perplexity tends to reflect structural and freshness work fastest.
Entity and authority work compounds over months, because it depends on third-party sources and on model training and retrieval cycles rather than on your site alone. Content restructuring usually sits between the two. We report movement per prompt rather than as a single timeline, since the platforms do not move together.
No, and an agency presenting it that way is worth a second look. Retrieval still favours pages that are crawlable, quick to render and clearly structured, which is the same technical foundation ranking depends on. AI Overviews in particular are grounded largely in ranking pages.
The accurate framing is that GEO extends SEO rather than succeeding it. The technical layer is shared, held to a stricter tolerance because most AI crawlers do not execute JavaScript. What is genuinely additional is the entity, corroboration and prompt-targeting work that decides which of several eligible pages gets named.
A baseline. Ask which prompts they tested, what the answers were before the engagement started, and how they will report the same prompts later. Work without a recorded starting point cannot be evaluated, and a proposal that opens with a retainer rather than an audit is describing an arrangement rather than a plan.
Then ask what they will actually change: which pages, which schema, which third-party sources, and who writes and ships the change. Specific answers to those questions separate a discipline from a service page with new keywords on it.
One last thing
We run your priority questions through ChatGPT, Gemini, Perplexity and AI Overviews, record who is cited on each one and which page they were cited from, and hand back the findings with the fixes ranked by what they are most likely to move. Work through it with us, hand it to your own team, or keep it on file. The document is yours regardless.
Get your free GEO auditLive prompt testing · citation baseline · no obligation
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