What GEO is, and how it differs from SEO
Generative Engine Optimization (GEO) is the practice of making your brand appear, accurately and favorably, in answers written by AI assistants. You may also see it called AEO (answer engine optimization) or LLM optimization. The goal is the same: when someone asks an AI “what's the best accounting software for a small agency?”, your brand is part of the answer, described the way you would want it described.
SEO and GEO overlap, but they optimize for different outcomes:
- SEO competes for a position in a list of links. Success is a ranking and a click.
- GEO competes for a mention inside a synthesized answer. There may be no click at all: the recommendation itself is the outcome, and the sources the AI cites are a second, separate opportunity.
- Search results are fairly stable for a given query. AI answers vary from one run to the next, which changes how you measure them (more on that below).
In practice, good GEO builds on good SEO. Engines that search the web before answering tend to pull from pages that are already discoverable and trusted. GEO adds a layer on top: is your brand named, in what position, with what framing, and which websites are speaking for you?
How AI engines decide what to recommend
Each engine works differently, but most answers about products and services come from a mix of two inputs.
1. What the model learned during training
Large language models absorb associations from the text they were trained on: which brands are commonly mentioned together, which are described as leaders, what people complain about. This knowledge has a cutoff date and changes only when the model is updated, so it moves slowly.
2. What it retrieves at answer time
Assistants with web access (ChatGPT search, Perplexity, Gemini, Google AI Overviews and AI Mode, Claude with web search) run searches before answering. A single question is often expanded into several narrower searches, sometimes called query fan-out: “best trail running shoes” might become searches for beginner picks, wide-fit options, reviews and price ranges. The engine reads the top results, synthesizes an answer and usually lists some of those pages as citations.
This has practical consequences:
- The pages AI retrieves for your category (review sites, comparison articles, forums, publisher roundups) often matter as much as your own site.
- Clear, specific, well-structured pages that directly answer a question are easier to retrieve and quote than vague marketing copy.
- If AI crawlers can't access your site, it can't be retrieved or cited, however good it is.
What to measure
Start from the questions your buyers actually ask, phrased the way they would type them, without your brand name. Then track, per engine and over time:
- Visibility (mention rate): the share of answers that mention your brand. This is the headline metric.
- Share of voice: your share of all brand mentions among the competitors you track. It shows whether you're gaining ground relative to the field, not just in absolute terms.
- Citations: how often answers link to your site, and which third-party domains get cited for your prompts. The latter is your off-site target list.
- Position: where you appear when you are mentioned. Being named first in a list of five is not the same as being an afterthought.
- Sentiment and framing: the words AI uses about you. A mention that says “popular, but users report billing issues” is a reputation problem, not a win.
Every metric should trace back to real answers you can read. If a dashboard can't show you the answer behind a number, treat the number with caution.
Why one run isn't enough
Ask an AI assistant the same question twice and you will often get two different answers: different brands, different order, different sources. That isn't a bug. The models generate text probabilistically, and live search results shift too. It means a single answer is an anecdote, and a single weekly run is closer to a poll than a measurement.
The fix is the same one used anywhere you measure something noisy: take several samples and report the rate together with its uncertainty. Suppose your brand appears in 3 of 5 answers. That's 60%, but with so few answers the true rate could plausibly be anywhere from about a quarter to almost nine in ten. As the number of answers grows, the same 60% becomes a much tighter estimate:
| Answers mentioning you | Rate | 95% interval | Uncertainty |
|---|---|---|---|
| 3 of 51 run on each of 5 days | 60% | 23%–88% | |
| 18 of 303 samples × 5 engines × 2 runs | 60% | 42%–75% | |
| 90 of 1503 samples × 5 engines × 10 prompts | 60% | 52%–67% | |
| 360 of 600a month of weekly runs on 10 prompts | 60% | 56%–64% |
The interval in that table is a Wilson score interval. In plain words: given how many answers you looked at, it's the range of true rates that are reasonably consistent with what you saw. It behaves well with small samples and with rates near 0% or 100%, which is exactly where simpler formulas break down.
The practical rule
The playbook
Answer the prompts directly
For each important prompt, make sure a page on your site answers it head-on. Lead with a clear, quotable answer, then support it: who the product is for, how it compares, what it costs, and evidence such as reviews, data or certifications. Engines retrieve and quote specific pages, not general brand impressions.
Build honest comparison pages
“X vs Y” and “alternatives to X” questions are common in AI prompts. Fair, specific comparisons (including where a competitor is the better fit) tend to be more useful, and more citable, than one-sided pages.
Earn mentions on the sources AI already trusts
Look at which domains are cited for your prompts. Those review sites, publisher roundups, directories and community threads are where the answer is being shaped. Getting listed, reviewed or included there is often the fastest way into the answer itself.
Fix the technical basics
- Crawler access: check robots.txt for rules that block AI search and answer bots (for example OAI-SearchBot, ChatGPT-User, PerplexityBot, Claude-SearchBot). Decide deliberately which bots to allow.
- llms.txt: a short file that points AI systems to your most important pages. It's cheap to add and helps make your key content easy to find.
- Structured data: Organization, Product, FAQ and Review markup make facts about you machine-readable and unambiguous.
- Entity consistency: use the same name, description, pricing and key facts across your site, profiles and listings. Contradictions get repeated, and outdated claims linger.
Correct the narrative
When AI repeats something negative or out of date, find where it comes from. Usually it's a specific page or thread among the cited sources. Address it at the source and on your own site, plainly and factually.
Proving impact
GEO work only earns budget if you can show it worked. Treat each change as an experiment: record the date, the prompts it targets, and compare visibility on those prompts before and after, over windows long enough to collect a meaningful number of answers.
Then test whether the difference is larger than normal variation. A two-proportion significance test answers exactly that question: if visibility on the targeted prompts went from 31% to 54% across a few hundred answers on each side, that's a real change; if it went from 31% to 35% across a few dozen, it may well be noise. Reporting the before, the after, the sample sizes and whether the change is significant turns “we think it helped” into evidence.
How Huginnova does it
FAQ
Is GEO replacing SEO?+
No. AI engines that search the web lean heavily on pages that already rank and on sites with authority in your category. Strong SEO is the foundation; GEO adds the questions of whether you are named, how you are described and which sources speak for you.
How long does it take to see results?+
Changes to live-search engines can show up within days of a page being crawled and cited. Changes that depend on third-party coverage or on model training take longer. Measure continuously so you can tell real movement from noise.
Should I track prompts that include my brand name?+
Track them separately. Branded prompts tell you how AI describes you; unbranded prompts ("best X for Y") tell you whether AI recommends you at all. Visibility metrics should be computed on unbranded prompts.
How many prompts do I need?+
Enough to cover your core use cases, price points and comparisons: typically 20 to 100 per brand. Fewer well-chosen prompts sampled repeatedly beat hundreds of prompts sampled once.
Does blocking AI crawlers protect my content?+
It can keep your pages out of training data, but blocking search and answer bots also keeps you out of live AI answers and citations. Decide per bot: many brands allow search/answer bots while restricting training crawlers.