Generative engine optimization needs a method, not another dashboard
Generative Engine Optimization (GEO) is the practice of getting a brand described accurately and favourably in answers produced by AI assistants such as ChatGPT, Gemini, Perplexity and Copilot. SEO optimises for rank against a keyword. GEO optimises for how a model retrieves, synthesises and phrases an answer when a person asks a question in their own words.
Your brand already has a shadow reputation
About 5.5 billion people are online, roughly 68% of the world, on the ITU's 2024 estimate. Google has said AI Overviews reach more than 1.5 billion users a month. OpenAI has put ChatGPT at around 800 million weekly users. In enterprise software categories, a serious share of the buying journey now starts with a question typed into one of those boxes rather than into Google's search bar.
Most of those people will never see your website. They see a paragraph a model wrote about you, assembled from a Reddit thread, a comparison post someone else published, a review site, and whatever your landing page said eighteen months ago.
That paragraph is your shadow reputation. Marketers are losing sleep over it because they can neither see it nor edit it.
So can a brand actively curate its presence inside generative AI? Yes. Is the market currently selling a scientific way to do it? Mostly no.
What most answer engine optimizers actually do
The playbook is close to universal:
- Ask for your domain, take a short brand description, scrape your landing page, and have a model invent prompts.
- Show a dashboard scoring you and your competitors against those invented prompts.
- Hand over an AI generated strategy with no evidence behind it.
It does not matter whether the company doing this is valued at a billion dollars or a few hundred million. If the product is a dashboard plus a made up strategy, the brand that bought it has hired a marketing quack.
How GEO differs from SEO
| SEO | GEO | |
|---|---|---|
| Unit of targeting | 40 to 50 keywords can cover a sector | Thousands of prompts phrased in natural language |
| Where the target list comes from | Keyword volume tools | Real human conversations |
| What you measure | Rank, clicks, impressions | Visibility, share of voice, sentiment, position, word count |
| Where the answer is sourced | Your page | Your page, plus Reddit, review sites, press, and competitor pages |
| Refresh cycle | Quarterly | Weekly or faster, because models and competitors both keep moving |
| Failure mode | You rank on page two | The model describes you wrongly, or leaves you out entirely |
The second row is the one people underestimate. Princeton's GEO research (KDD 2024) found that adding cited sources lifted visibility in generated answers by around 40%, adding statistics by 37%, and keyword stuffing actually cost about 10%. The mechanics are genuinely different, not a reskin of SEO.
Four capabilities of a scientific GEO program
1. Prompts should come from real human conversations
A sector can be described in 40 to 50 keywords. It cannot be described in 40 to 50 prompts, because prompts carry intent, context, constraints and phrasing that shift by persona and by week.
So the prompt set has to be discovered rather than invented. Mine the places your buyers already talk: relevant subreddits, X threads, community forums, review site complaints, and your own sales call transcripts. Add the marketing hypotheses your team is willing to defend. Run all of it through an engine tuned to pull out high signal prompts and file them against a customer persona.
An invented prompt tells you how a model answers a question nobody asked.
2. Visibility alone is not a metric set
Tracking rank was enough for SEO. It is not enough here, because brands compete on several dimensions inside a single generated answer.
At the first level, measure visibility, share of voice, sentiment, position within the answer, and word count. A brand mentioned last in eleven words with lukewarm framing has a very different outcome from one described first across sixty words.
At the second level, measure the supply side of the answer: which domains and URLs the model retrieved, and what style of content earned the citation. That is where the actionable part lives. If four of your competitors' mentions come from one comparison site you have never heard of, that is your next week of work.
These are raw inputs. On their own they are trivia.
3. Strategy has to be interwoven with the brand's strategy
AI slop is what happens in the absence of taste. In enterprise solutioning, taste means something specific: a considered strategy, real targets, and honest awareness of the constraints the organisation operates under.
Prompts and metrics tell you what the AI search layer currently thinks of you. Turning that into insight is one step. Contextualising the insight against the company's actual strategy is the harder one, and it requires the system to know:
- the ideal customer profile being targeted
- brand guidelines and tone
- the product's real differentiators
- the constraints, including where aggressive marketing on one brand would damage another in the portfolio
The job is to work the white space, storyboard the options, and help the marketer produce an AI strategy that is synchronised with the strategy they already have. Anything less produces confident recommendations that no CMO can sign.
4. Execution runs on a long horizon agentic loop
GEO is like practising yoga in the eye of a storm. Model providers keep retuning retrieval. Competitors keep publishing. The environment never settles. Doing this manually is like being asked to bend all four elements at once, which is a fine premise for a cartoon and a poor operating model for a marketing team.
The superpower is a long horizon agentic loop. Models favour fresh, updated content, so the loop takes the near term actions out of the strategy and executes them frequently and across channels: Reddit narratives, LinkedIn posts, nudges to affiliated third party sites, product page updates, site changes, blogs.
Execution needs mature workflows, the right skills attached to each step, and token discipline. Value per token is a real constraint once you are running continuously.
From GEO anxiety to action
The answer engine boom has produced a generation of dashboard companies. They track a handful of machine invented prompts, display an AI visibility score nobody can trace, and leave the marketer more anxious than before.
What the category needs is a closed loop: track real human intent, learn where the brand actually stands, build a strategy that fits the business, then execute, experiment and act on it continuously.
That takes a team that understands markets and marketing, paired with enough technical grounding to know how AI engines search the web and interpret what they find. It also takes a forward deployed posture. Plug and play was never going to work on a problem this unstable.
Epitom is built to do exactly this.
Frequently asked questions
What is generative engine optimization?
Generative Engine Optimization is the practice of influencing how AI assistants describe a brand in their generated answers. It covers prompt discovery, measurement of how the brand appears, strategy tied to the business, and continuous execution across owned and third party channels.
How is GEO different from SEO?
SEO optimises for rank against keywords on a results page. GEO optimises for how a model retrieves and phrases an answer. The target list is prompts rather than keywords, the metrics include sentiment and share of voice, and much of the source material sits on sites you do not own.
Which metrics matter in generative engine optimization?
Visibility, share of voice, sentiment, position within the answer, and word count give you the first level. The second level is the retrieval layer: which domains and URLs the model cited, and what kind of content earned those citations.
Where should tracked prompts come from?
From real conversations. Subreddits, forums, X threads, review site complaints and sales call transcripts, filtered for high signal and grouped by persona. Prompts generated by a model from your own landing page mostly tell you how AI answers questions no buyer is asking.
Can a brand actually influence what ChatGPT says about it?
Yes, though rarely by editing its own website alone. Models draw heavily on third party sources, so influence usually comes from a mix of clearer owned content, presence in the communities and comparison sites the models retrieve from, and consistent updates over time.
How often should a GEO program run?
Continuously. Model providers change retrieval behaviour and competitors publish constantly, so a quarterly cadence borrowed from SEO leaves long windows where the answer drifts without anyone noticing.