GEO for B2B How is my business recommended by ChatGPT, Claude and Perplexity?

The short answer: Generative Engine Optimization (GEO) ensures that AI search engines — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — name and cite your business. Three levers are decisive: machine-readable facts on your own domain, content with verifiable figures, and mentions on external trustworthy sites. First, secure your own brand, then conquer the category. GEO complements SEO; it does not replace it.

Contents

• What is Generative Engine Optimization?
• Why is AI search changing B2B procurement right now?
• What does the zero-click trend mean for your website?
• How does AI search choose who to recommend?
• What is the difference between brand visibility and category recommendation?
• What six levers can bring a B2B company to AI-driven solutions?
• Which AI crawlers do you need to allow on your website?
• What role do directories and review platforms play?
• How quickly does GEO work and what does it cost?
• What does a 90-day roadmap for GEO look like?
• What are the most common mistakes hindering AI visibility?
• How do you measure if the AI search mentions your company?
• Which GEO myths cost budget without effect?
• Frequently Asked Questions

Generative Engine Optimization (GEO), often also called Answer Engine Optimization (AEO), is the optimisation of content in order to be cited in AI search answers – meaning in ChatGPT, Claude, Perplexity, Gemini or Google AI Overviews. While classic SEO competes for positions in a link list, GEO competes for a place in the generated answer itself, including name attribution and source link.

The difference is more than semantics. AI answer engines work in two steps: during retrieval, they fetch relevant webpages in real-time or access training knowledge. During generation, they typically summarise three to eight sources into one answer and link the citations. Anyone who doesn't feature in either of these steps doesn't exist for that search query. And because the answer only has a handful of citation slots, the competition for these spots is tougher than for page one on Google – but it's decided by different rules.

Because the buyers are already there. The Forrester Buyers' Journey Survey 2026 of around 18,000 B2B buyers shows: 94 percent used AI in their last purchasing process, and AI response engines are now the most important research source, ahead of vendor websites, product experts, and sales. 55 percent compare vendors directly in the AI tool, and 47 percent build their internal business case there before they even contact a vendor (Forrester, January 2026).

The trend is clearly established. 51 percent of B2B software buyers prefer to start their research with an AI chatbot rather than Google, and 71 percent use AI assistants in their software research (G2 via Demand Gen Report). A multi-source analysis of 680 million AI citations confirms 73 percent AI usage in B2B purchasing (Loganix/Averi, March 2026). The volume is growing accordingly: ChatGPT alone processes around 2.5 billion prompts per day (TechCrunch/OpenAI, 2025), in addition to Gemini, Claude, Perplexity and Copilot — and the market continues to fragment: Gemini has increased its share of AI traffic from 5.7 to 22 percent within twelve months (The Digital Bloom, 2026). Anyone optimising for just a single platform is now ignoring more than a third of AI search.

The traditional approach via the link list is drying up. An AI Overview appears for around 21 per cent of all keywords (Ahrefs, 146 million SERPs, desktop, as of September 2025). In the first four months of 2026, 68.01 per cent of US Google searches on desktop and mobile ended without a single click on a website (SparkToro using clickstream data from Similarweb, June 2026). Where an AI Overview is displayed, the click-through rate for the number one position falls by around 58 per cent (Ahrefs, analysis of over 300,000 keywords, as of December 2025).

The search journey shifts from distributor to destination: the answer is directly in the search engine or AI chat, and clicking through to a website is becoming increasingly rare. For B2B providers, this doesn't mean visibility becomes worthless – it merely shifts. Brands that are cited in AI answers receive around 120 per cent more clicks per impression than those not cited (Seer Interactive). Being cited is the new ranking: the question is no longer just „What position am I in?", but „Will I appear in the answer when my target customers ask?"

Language models don't guess, they match. If a brand appears consistently and is substantiated across several independent current sources, the model's confidence in that exact brand increases. You can influence this in three places – the infographic shows them at a glance:

The mechanism has been scientifically investigated. Content with specific statistics, citations, and clean source references achieves up to 40 percent more visibility in generative answers (Aggarwal et al., Princeton/Georgia Tech, SIGKDD 2024). Also striking: brand mentions on the web correlate around three times more strongly with AI citations than classic backlinks, with a correlation of 0.664 compared to 0.218 (Ahrefs analysis of over 75,000 brands). Thus, AI search rewards different signals than classic ranking.

And it rewards other pages: Around 62 percent of URLs cited in AI Overviews do not rank in the organic top 10 (Ahrefs, 863,000 keyword SERPs and 4 million AI Overview URLs, as of March 2026). Comparison articles make up the most frequent citation format at 33 percent (ConvertMate, 2026). This is the real news for smaller B2B providers: In the AI answer, you can beat corporations that dominate you in Google rankings – if your content is more citable than theirs.

They are two different games, and mistaking them will cost months. The table shows the difference:

The order is non-negotiable: first, make your own house machine-readable, then build the third-party signals. Whoever wants to win the category before AI search accurately describes their own company is building on sand — the model cannot recommend a brand it cannot confidently classify.

AI bots divide into three classes: search bots retrieve content for live answers and generate citations, user bots retrieve a page when a user requests it in a chat, and training bots collect data for model training. For maximum visibility, always allow search and user bots. For training bots, decide consciously – those who want to be present in the models themselves should also allow them.

The most common mistake is not a deliberate one: firewall or bot protection rules (for example, with CDN providers) block AI crawlers across the board as „suspicious traffic". A look at the server logs shows whether the search bots are actually arriving – if not, any content optimisation is in vain.

It is larger than most people like. When the AI search answers „best B2B agency in Austria", it predominantly cites sources it trusts for comparisons rather than the agency websites themselves: industry directories, review platforms and editorial lists. In the DACH region, these typically include Sortlist, ProvenExpert, Clutch and OMR Reviews for service providers, as well as topic-specific portals.

Our own prompt monitoring confirms this pattern: in the agency category prompts we track, directory and platform domains are consistently among the most cited sources – even more so than most provider websites (own Peec AI measurement, June to July 2026). The consequence: a well-maintained, consistent profile on the two to three relevant platforms in your category is not a nice-to-have, but the most direct route to the category answer. Consistency counts double here – name, positioning and description should be identical everywhere so that the machine can confidently assign the entity.

Brand visibility through your own facts and schema shows up in weeks, category recommendations through third-party signals take months. Ad budget is not needed for any of the six levers: schema, a clear entity, extractable content, and self-maintained directory profiles are working time, not media budget.

The window of opportunity is real. 92 percent of marketers state they want to optimise for AI search, but only 41 percent are actively adapting their strategy (HubSpot, 2026). Users report 4.4 times more qualified traffic through GEO optimisation. Those who start now will compete with a fraction of future competitors for the three to eight citation spots per answer.

Afterwards, ongoing operation begins: maintaining content, replacing statistics older than 18 months, prioritising new topics based on prompt demand, and remeasuring citation impact monthly. AI systems favour current sources – citations expire without maintenance.

Without measurement, GEO remains a matter of faith. Four levels have proven effective: prompt monitoring like Peec AI, where defined customer prompts are run daily against relevant AI search services, including competitor comparisons. In addition, AI bot traffic in server logs, a dedicated referral segment for AI sources in Analytics, and regular manual testing of the most important customer questions in multiple AI tools – platforms cite very differently, with only 11 percent of domains being cited simultaneously by two major AI search services (Averi, 680 million citations).

We apply this to ourselves: Defined category prompts are run daily against the relevant AI search services; the services pull and cite Lilox pages within these exact prompts, and the citation rate of our domain increases week by week (own Peec measurement, July 2026). Precisely this method, measured monthly, is the practical proof behind our consulting.

Reflects the real prompts from ChatGPT, Perplexity & Google AI around GEO for B2B.