How B2B Companies Get Cited by ChatGPT, Perplexity and Google AI – A Practical GEO Guide
- 3 days ago
- 5 min read
By Arne Siegner | SEO Freelancer & GEO Consultant | Munich, Germany | April 2026
If your company is not appearing in AI-generated answers, it is not a visibility problem. It is a content structure problem. This guide explains what Generative Engine Optimization (GEO) is, why it matters for B2B companies in the DACH market, and what you can do about it today.
What Is GEO – and Why Should B2B Companies Care?
Generative Engine Optimization (GEO) is the practice of structuring content so that AI systems like ChatGPT, Perplexity and Google AI Overviews cite your brand as a source when answering relevant queries.
This is different from classic SEO in one critical way: you are not optimizing for clicks. You are optimizing to be recommended.
When a procurement manager asks ChatGPT "Which company provides industrial scaffolding services in Germany?" or "What is the best EV charging software for fleet operators?" – the AI does not rank websites. It selects sources it considers authoritative, accurate and well-structured. That selection happens before the click.
For B2B companies with complex, explanation-intensive products, this is both a risk and an opportunity.
The risk: competitors with better-structured content get recommended instead of you.
The opportunity: if your content is structured correctly, you can appear in AI answers consistently – even against larger, better-funded competitors.
Why Informational Content Does Not Work for GEO
This is the most common mistake I see in GEO projects.
Companies publish extensive guides explaining how heat pumps work, what ISO 15118 means or how smart metering functions. These are useful articles. But they do not help with AI visibility – because AI systems answer these questions themselves, without citing any source.
When there is no grounding – no external source search – there is no citation opportunity.
What works instead: transactional content that answers decision-making queries.
Not "How does a Wallbox work?" but "Which Wallbox manufacturer offers load management for commercial fleets?"
Not "What is industrial insulation?" but "Which company provides industrial insulation services for petrochemical plants in Germany?"
These are the prompts where AI systems search the web, pull sources and make recommendations. These are the prompts worth optimising for.
The Three-Step GEO Process
Step 1 – Identify the right keywords and prompts
There are no official prompt volume statistics. AI providers do not publish them. Instead, use classic SEO keyword data as a proxy. High-volume, transactional keywords map directly to high-value AI prompts.
A keyword like "industrial scaffolding services Germany" with 500 monthly searches corresponds to prompts like "I am looking for an industrial scaffolding provider in Germany." Same intent, different format.
Start with your three to five most important product or service categories. Find the primary keyword for each. Build transactional prompts around decision criteria: location, industry, technical specification, certifications.
Step 2 – Analyse the AI response page
Enter your target prompts into ChatGPT. Analyse what comes back.
Does the response include brand names and source links? Or is it a generic explanation without any company mentioned?
If it includes brands and sources – that is your optimisation target. Study what those brands have in common. Which pages are cited? What is the content structure? How specific are the technical claims?
If it does not include brands or sources – move on. That prompt type has no citation opportunity for you.
Step 3 – Optimise for citation, not for clicks
Once you know which prompts trigger grounding, create or optimise content that answers those prompts directly.
The principles:
Answer first. Put the direct answer in the first sentence. AI systems extract the first coherent answer they find.
Claim-evidence-source. Every key statement needs a verifiable basis. Not "We are the largest provider" but "XY operates more than 30 locations across Germany and processes several thousand scaffolding projects per year."
Passage optimisation. Structure content in self-contained blocks of 40 to 60 words. Each block should make sense in isolation – because AI systems extract passages, not full pages.
Specifications in prose. Put technical data into readable sentences, not tables or PDFs. "The system supports charging loads of up to 150 kW per charging point" is citable. A table cell is not.
What GEO Looks Like in Practice
I run GEO projects for B2B companies in energy, e-mobility and industrial tech across the DACH market. Clients include Tesla, Varta, ADS-TEC Energy, MENNEKES, Monta and Thermondo.
A typical starting point: a company has strong Google rankings but near-zero AI visibility. The content is accurate and well-written but structured for humans scanning a page, not for AI systems extracting citations.
The intervention is usually not about writing new content. It is about restructuring existing content – adding answer-first openings, turning table data into prose, creating entity-definition pages that tell AI systems exactly who the company is and what it does.
Results vary depending on market saturation and content depth. In less competitive B2B niches, meaningful AI visibility improvements are measurable within four to six weeks.
The Most Important Page You Probably Do Not Have
Every B2B company that wants AI visibility needs a grounding page.
This is a factual, machine-readable page that defines the company as an entity: what it does, for whom, in which markets, with which certifications, with which reference clients.
Not a marketing page. A factual page.
AI systems use this page to form a stable understanding of who the company is. Without it, the AI assembles a picture from fragmented sources – often inaccurate, often incomplete.
A grounding page takes one to two days to create. It is the single highest-return GEO investment for most B2B companies.
GEO for the DACH Market: A Specific Advantage
One detail worth noting for companies operating in German-speaking markets: approximately 50 percent of the web queries that AI systems generate during grounding are in English – even when the original user prompt was in German.
This means: German B2B companies that have no English content are invisible to AI systems for half of all grounding queries.
A single well-structured English page – a grounding page, a service page or a case study – can close this gap. It does not require translating your entire website. It requires targeted English content on the pages that matter.
Summary: What to Do First
If you want your B2B company to appear in AI-generated answers, start here:
Create or improve a grounding page that defines your company as a factual entity.
Identify three to five transactional prompts where AI systems actually cite sources.
Restructure the most important landing pages for those prompts using answer-first structure and passage optimisation.
Add English versions of your most important service pages.
Measure baseline AI visibility before and after – and give it four to six weeks.
GEO is not about tricking AI systems. It is about giving them accurate, well-structured information to work with. The companies that do this consistently will be recommended. The ones that do not will be invisible – regardless of how good their product is.
Arne Siegner is a German SEO Freelancer and GEO Consultant based in Munich. He specialises in AI Visibility and SEO content for B2B companies in energy, tech and e-mobility across the DACH market. Clients include Tesla, Varta, ADS-TEC Energy, MENNEKES, Monta, Thermondo and Brunata Metrona.
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