Generative Engine Optimization (GEO) Strategy: Where a B2B Startup Should Start

TL;DR

• The median enterprise B2B brand appears in 3.0% of relevant Google AI Overviews, in Walker Sands' benchmark of more than 45 million keywords and 828 companies, so assume you are missing from most answers until you check. Write down the questions your buyers ask, run them, and keep that log as the baseline every later answer is measured against.
• A generative engine optimization strategy for a startup is an order of work, and the first step depends on what the baseline shows. As our AI visibility hub defines them, GEO (Generative Engine Optimization) targets LLM-powered platforms like ChatGPT and Perplexity, while AEO (Answer Engine Optimization) is structuring a page so an answer engine, from Google's featured snippets and voice assistants to ChatGPT, can lift a direct answer from it.
• Your rankings will not tell you which pages an answer cites: in a measurement study of Google AI Overviews, nearly 30% of cited domains did not appear in the first-page results shown beside them. The citation list from your own baseline is the target list for coverage, and earning a place on it is PR work.
• Skip llms.txt, AI-only schema, chunked pages, a page per prompt variation, and any vendor who guarantees citations. Google says none of the first three is needed for Google Search, and Bing says GEO does not guarantee grounding or citations. The diagnosis stays in-house; outside help earns its fee on sustained earned media and monitoring.

Where does a GEO strategy start for a B2B startup?

It starts with the answers your buyers already see, and with what those answers cite. Your Google rankings cannot tell you whether ChatGPT leaves your company out, describes it wrongly, or names it and then recommends a competitor, and those are different problems with different first steps. A measurement study that issued 55,393 trending queries to Google over 40 days in March and April 2026 found that AI Overviews appeared on 13.7% of queries overall and on 64.7% of question-form queries, and that nearly 30% of the domains those overviews cited did not appear in the first-page results displayed beside them. So the questions buyers ask are the ones most likely to get an AI answer, and your own rankings will not tell you which pages that answer will cite.

That is the PR half of the problem, and it is where we start. When someone asks an AI assistant to recommend tools in your category, the answer draws on your earned media, your community presence and your own pages together. In the baselines we run for clients, the sources that keep appearing beside a category's answers are rarely the biggest outlets in that category; they are the trade publications, forums and documentation pages the engines already trust for that question. Those sources are the target list, and earning a place on them is PR work before it is content work.

The page-level method in sequence is in our guide to getting cited by ChatGPT, and the agency decision in what a GEO agency actually does.

Which questions should you test, and what do you record?

Start with the questions buyers ask before they know your name: the category question ("which tools help fintech teams reconcile payments?"), the comparison that names a competitor, the "alternatives to" question, and the risk question your sales calls always reach. Keep the buyer's wording even when it differs from your website copy. A spreadsheet and 30 prompts are enough to start; our client baselines run to 300 to 500 prompts pulled from real conversations.

Run each prompt three times, in a new session each time, in ChatGPT, Perplexity, Claude, Copilot and Google's AI Mode. What to log for every answer is in the checklist at the end of this post, and nothing in the wording changes between runs: one answer cannot tell you whether an omission persists, and a reworded prompt makes later runs incomparable.

One first-party report helps you find questions you missed. Bing Webmaster Tools' AI Performance report lists the grounding queries behind citations of your pages across Microsoft Copilot, AI-generated summaries in Bing and partner integrations; Bing says they are grouped representations rather than full user prompts, so use them as leads for the prompt list, never as the baseline.

What do you do first when the answers come back?

An answer can leave you out, get you wrong, or describe you well and still send the buyer elsewhere, and each one starts the work in a different place. Fixing the wrong one first is the most common waste we see: coverage earned while the description is wrong repeats it.

If no answer names you

Omission is a sources problem before it is a page problem. Confirm the engines can reach you at all, meaning your key pages are indexed and eligible for a snippet and the search crawlers are allowed in robots.txt (the site section below has the list), then look at which sources the answers cite for your competitors. The domains that recur for your category are where coverage earns a place in the answer, and they are rarely where the startups we meet have been sending their PR effort. Earned coverage on those sources is the first step for an absent startup, and it is PR work. Our guide to PR for SEO covers how the two disciplines meet.

If an answer names you but gets you wrong

A wrong description follows you into every recommendation. If ChatGPT calls your developer tool an analytics platform, more coverage spreads the wrong category. Compare each description in the log with your product pages, your LinkedIn company page, your directory listings and the bio your founder uses when quoted, and look for the sentence the engine is repeating. In our experience it usually exists somewhere you control. Fix it there first: one name, one category sentence, one intended buyer, stated plainly on the homepage and the product pages, then repeated in the places outside your site that describe you. Rerun the baseline prompts and confirm the description moved before you seek new coverage, because a placement earned now carries whichever description the engines currently hold.

If an answer describes you well but recommends someone else

Read the reason the answer gives, because it is usually specific: a feature the competitor documents and you do not, an integration, a customer story in a vertical the buyer named. Then check whether your pages and the cited sources establish a distinction for that use case. The fix is evidence on the page that answers that buyer question: the documented product constraint, the test result you can substantiate, the customer evidence you have permission to publish. Google's guidance asks for exactly this, "a unique point of view" and non-commodity content, with a first-hand review as its example. The same evidence is what a trade outlet can report, which is how a startup in this position earns coverage on the sources from step one.

How do you earn coverage on the sources the answers already cite?

Take the citation list from the baseline, not a media list built from search rankings. In a July 2025 Semrush study of 5,000 keywords, a domain's presence in AI citations rose with its count of top-10 Google rankings, a correlation Semrush called remarkably strong at the domain level, but Google AI Mode's sidebar links shared only 32% of their URLs with those top 10 results. Ranking tells you which domains tend to get cited, not which page an answer will quote, which is why your own citation log is the better target list.

Then match the work to the source. A trade outlet wants original evidence its readers do not have: product data, a documented change in your market, a practitioner's account of a decision that went wrong. A forum wants the specific question answered, with real detail, by someone who says who they are. Google's AI search guidance says its generative features can show what is being said about products and services across blogs, videos and forum discussions, and in the same list warns that seeking inauthentic mentions is not as helpful as it might seem.

A placement cannot guarantee a citation or control the resulting description, which is why the description fix comes before it. What it does is give the answer another trusted source to draw on. Earned media is the Authority component of PR Plus, and in our experience it is the part of the work a one-person team most often has to buy.

What should you check on your own site before seeking more coverage?

Both checks are in-house work: whether the engines can read your pages, and whether the pages say what you want repeated.

Crawler access first. For ChatGPT search, OpenAI's crawler documentation says sites that opt out of OAI-SearchBot will not be shown in ChatGPT search answers, though they can still appear as navigational links; it is a separate setting from GPTBot, which crawls content that may be used for training. Check Anthropic's crawlers in the same pass: it documents three, ClaudeBot for training, Claude-User for fetching a page when someone asks Claude a question, and Claude-SearchBot for search indexing, and says disabling either of the last two may reduce your site's visibility. For Google's AI features, a page must be indexed and eligible to be shown with a snippet, and the property's Search generative AI control must read include, which is the default, so check it has not been switched off. Copilot and Bing's AI summaries rely on the same crawling, indexing and ranking foundation as Bing search. Our post on whether your site blocks AI crawlers goes through the robots.txt rules crawler by crawler, including the training blocks that turn into search blocks by accident.

Then the pages. An owned page is easiest to quote when it answers one buyer question with information a competitor cannot simply repeat, puts the answer near the top, and uses headings a reader skimming for the condition or the exception can find. Google says you "don't need to write in a specific way just for generative AI search" and specifies no ideal page length. The page-by-page detail is in the ChatGPT citation guide.

What do the engines say you can skip?

Skip AI-specific files and formatting until the baseline shows a problem they would solve. Each has an engine's own documentation saying it is not needed.

• llms.txt as a citation tactic. Google says keeping one for other services is fine and that it will neither help nor harm your visibility in Google Search, which ignores the file. OpenAI's crawler documentation routes publishers to the OAI-SearchBot rule in robots.txt and names no AI text file as a requirement for appearing in ChatGPT search.
• Schema added only for AI. Google says structured data is not required for generative AI search and there is no special schema.org markup to add; Bing says structured data may support clearer grounding but does not guarantee visibility or grounding traffic. Keep the markup that serves rich results and matches the visible page.
• Chunking pages into fragments. Google says there is no requirement to break content into tiny pieces for AI to understand it. Organize each page around a buyer question and the evidence that answers it.
• A page for every prompt variation. Google's guide says that creating separate content for every variation of how people might search, fan-out queries included, primarily to manipulate rankings or AI responses, violates its scaled content abuse policy. Publish a separate page only when it carries distinct, useful information.
• Any vendor who guarantees citations. Bing's webmaster guidelines state that GEO does not guarantee grounding or citations in AI experiences, and Google's AI search guidance says no third-party tool has access to its internal ranking or AI systems. A vendor can improve the sources an engine might use. No vendor can promise which source an answer will cite, and that includes us.

Who on a small marketing team does each step, and what needs outside help?

The agency-or-in-house decision is covered in what a GEO agency actually does. The narrower question for a Series A or B startup with one to three people in marketing is who owns which step, and where the work exceeds the time they can protect.

What one marketing lead can run alone

The baseline, the diagnosis and the description fix. Collecting 30 buyer prompts, running them and logging the answers is work one person does with a spreadsheet, and it is the one step not to outsource: whoever ran the baseline is the one who can judge whether a partner's work moved it. The description fix is copy on pages you control plus the profiles outside your site, and the same person owns both. Crawler access needs a developer for an afternoon.

What a second or third person adds

With a content lead, the owned-page work becomes possible at the pace the baseline demands: one page per buyer question, each carrying evidence a competitor cannot repeat. With someone who can pitch, the first earned coverage on the cited sources becomes possible too, though it is the first thing to slip when a launch or a funding round takes over the calendar, every time we have watched it happen.

What usually needs outside help

Earned coverage on the sources the answers cite takes relationships and sustained follow-up. Ongoing monitoring is in our experience the second thing a small team stops doing after the first quarter: the baseline gets built once, and the tracked set that has to be rerun every month is what quietly lapses. Those are the two places outside help earns its fee. Ask any partner which of the sources in your log they can credibly approach, which engines their monitoring samples, what they will produce, and how they will report against your baseline rather than their own.

How do you measure whether any of this worked?

Against the baseline, with the same prompts. Measure the share of baseline prompts on which each engine names you, describes you accurately, and recommends you for a relevant use case, and record which owned or third-party sources each answer cites. Rerun the set every month, three times per prompt in fresh sessions, and compare patterns with the original log rather than one new mention. If running all of it by hand is more than the month allows, freeze a subset and rerun that same subset every month: a comparable record you keep beats a complete one you abandon. A description that has changed in all three runs is a result; one that varies between sessions is noise.

Two first-party reports sit beside the log. Bing's AI Performance report shows which of your pages were cited, and says the data is a sample of overall citation activity and that a citation does not represent traffic, clicks or user engagement. Google's Generative AI performance report in Search Console counts impressions where a link to your site was shown in AI Overviews or AI Mode, by page, country and device, Google engines only, with no clicks and no query view. The ordinary Search Console performance report counts a click on a link in an AI Overview or in AI Mode as a click, so Google's AI referrals sit inside your usual traffic. Log page changes and earned placements on the same timeline, so a change in the answers can be read against what you did. What none of these reports tells you is what the answer actually said: whether it named you, described you correctly, or recommended someone else to the buyer who never clicked. The sampled answers in your log are the only record of that.

Generative engine optimization checklist

• [ ] Write down the buyer questions. Thirty prompts from sales calls, demo requests and support conversations: category questions, comparisons that name a competitor, "alternatives to", and the risk questions. Keep the buyer's wording.
• [ ] Record the answers. Run each prompt three times, in a new session each time, in ChatGPT, Perplexity, Claude, Copilot and Google's AI Mode. For every answer, log the date, the engine, the exact prompt, whether it names you, how it describes you, which competitors it names, and which sources it cites. Save that log as your baseline.
• [ ] Sort the prompts by what they show. No mention, a wrong description, or an accurate description with another recommendation. The column with the most entries sets your first step.
• [ ] Fix the description first if it is wrong. One name, one category sentence, one intended buyer, on the homepage, the product pages, LinkedIn and the directories. Rerun the affected prompts before seeking coverage.
• [ ] Confirm the engines can read you. Check robots.txt for OAI-SearchBot, Claude-SearchBot and Claude-User and the crawler behind Bing and Copilot; confirm the key pages are indexed and snippet-eligible in Search Console, and that the property's Search generative AI control is not set to exclude. Write the result down per crawler.
• [ ] Build the target list from the citations. Count the domains cited across the log. Pitch the outlets, forums and comparison sites that recur with evidence their readers do not have.
• [ ] Make the pages that answer buyer questions worth quoting. One question per page, the answer near the top, a documented constraint or a result you can substantiate on each.
• [ ] Rerun the same prompts every month. Compare named, described accurately and recommended with the baseline, and read Bing's AI Performance report and Google's Generative AI performance report beside the log.
• [ ] Skip the shortcuts. No llms.txt as a tactic, no AI-only schema, no chunking, no page per prompt variation, no vendor who guarantees a citation.

FAQs

Is a GEO strategy different from an AEO strategy? They converge on the same goal without being interchangeable. GEO (generative engine optimization) targets LLM-powered platforms like ChatGPT and Perplexity, while AEO (answer engine optimization) is structuring a page so an answer engine can lift a direct answer from it. In the order above, AEO is the owned-page step and GEO adds the sources you do not control, which is why it pulls PR into the same program. Our comparison of SEO, AEO and GEO lays out the overlap, and Althea Labs covers GEO and AEO under a single visibility program.

Does Google treat GEO as separate from SEO? No. Google says that "optimizing for generative AI search is optimizing for the search experience, and thus still SEO", and that its AI features are rooted in its core Search ranking and quality systems. The same page says indexing and serving are not guaranteed even when a page meets every requirement.

Do we need an llms.txt file? No. Google says Google Search ignores the file, so it neither helps nor harms visibility there, and OpenAI's crawler documentation points publishers to the OAI-SearchBot rule in robots.txt rather than to any AI text file. Keep one if another service reads it.

How long before AI answers change? We have not found a source that gives a reliable timetable for a revised answer. OpenAI says a robots.txt change can take about 24 hours to reach its search systems, which concerns access, not when ChatGPT will cite or describe a page. In our work, crawler access and entity facts move fastest and earned coverage takes longest, so judge by the same prompts rerun monthly, never by one answer.

Can anyone guarantee AI citations? No. Bing says GEO does not guarantee grounding or citations in AI experiences, and Google says no third-party tool has access to its internal ranking or AI systems. Ask anyone offering a guarantee which prompts, engines and period it covers, and how they will tell a repeatable change from one generated answer. We make no such promise; we report against your baseline.

Start with a Brand Intelligence Audit

Althea Labs is a brand presence and AI visibility agency that builds targeted visibility systems for B2B startups through integrated PR, SEO, and content programs. We cover GEO and AEO under a single visibility program, and in every engagement you work directly with Kara Silverman, our founder.

We call the method PR Plus. It works across four components, Authority (traditional PR), Infrastructure (SEO and schema), Human Proof (community) and The Bridge (content), and an engagement runs in five steps, in order: Audit, ICP + Messaging, Visibility Tracking, Strategy, Execution. AI visibility is the core of the work: we start where your baseline points, whether that is the description on the pages you own or the sources off your site that AI tools cite, and we measure both against the same prompts rerun on a schedule. Earned-media pitching, the Authority component, is scoped separately for each engagement.

Every Althea Labs engagement starts with the audit. The Brand Intelligence Audit is a full presence audit across SEO, PR, AI visibility and community, with custom Althea Score benchmarks and tactical recommendations for the next 30 to 45 days, delivered in five days as a deck plus a one-hour session. Bring the prompt log from the baseline above to that session: the answers you recorded are the fastest way for us to tell a description fix from an earned-media or owned-page priority. Start your audit and we will show you where your brand stands in search, in AI, and in the communities where your buyers actually are.