How Visible Is Your Business in AI Answers? ChatGPT, Gemini & Co. as the New Search Engine
ChatGPT, Gemini and Perplexity are increasingly used by B2B decision-makers for research and return direct answers instead of links. This guide explains what AI visibility means, what drives it, and which steps mid-sized businesses can take today.
Those looking for a recommendation for a software agency, IT service provider or AI consultant increasingly skip Google and type the question directly into ChatGPT, Gemini or Perplexity. These AI assistants do not return a list of links but deliver a direct answer: with company names, recommendations and sometimes specific reasons. For mid-sized businesses, this raises a new question: do we even appear, and if so, how?
AI visibility, often referred to as Generative Engine Optimization (GEO), describes precisely this aspect: the likelihood that a company, product or service appears in the answers generated by large language models (LLMs). Those absent from these answers miss a growing share of information searches by potential customers, without even noticing.
What AI Visibility Means and How It Differs from Classical SEO
Classical SEO is about ranking as high as possible in Google results. Its foundations are backlinks, keywords and technical quality signals. GEO is about an AI model knowing your company, understanding it and rating it as relevant, so that it mentions you when someone asks a fitting question. The mechanisms overlap but are not identical: an article that ranks well in Google does not automatically end up in LLM answers.
This does not mean SEO becomes irrelevant. Strong Google rankings are a powerful signal that content is authoritative and trustworthy. But companies pursuing a structured IT and technology strategy should treat GEO as an independent dimension: alongside SEO, not as a replacement.
How LLMs Decide Which Companies to Mention
Large language models like GPT-4o, Gemini or Claude were trained on vast quantities of text from the internet. In the process, they implicitly learned which companies are relevant in which contexts. When an LLM also uses web search as a tool, as with ChatGPT Search or Perplexity, it draws its answers from articles, directories and specialist pages that are findable online and clearly structured. Both pathways, training and retrieval, can be actively influenced.
Factor 1: Consistent, thematically clear web presence
LLMs preferentially cite sources that answer a question completely and clearly. A company that consistently communicates the same service description across multiple channels, website, press, guest articles, social media, has a better chance of being recognised as relevant. Particularly important are clear service pages for core offerings with FAQ sections that directly answer typical customer questions. Concise answers to specific questions are cited by LLMs more often than expansive prose.
Factor 2: External mentions and directory listings
When other websites, industry directories or specialist portals mention a company, it significantly strengthens the LLM signal. The goal is not to accumulate as many links as possible, but to be present in the sources that language models preferentially cite: directories such as feedbax.de, Sortlist and editorial specialist articles. An entry in a recognised agency directory is therefore not just an SEO signal but also a GEO signal: LLMs trust sources that are themselves considered authoritative.
Factor 3: Structured content and FAQ pages
LLMs preferentially extract answers to specific questions. Blog articles and service pages with clear question-and-answer structures, such as FAQ sections or H2/H3 headings that formulate a question, are cited more frequently than flowing prose. Structured data in the HTML code (Schema.org, particularly FAQPage, Organization and LocalBusiness) additionally helps LLMs process page content in a machine-readable way and classify it as a reliable source.
Factor 4: Technical signals, llms.txt and machine-readable data
An emerging standard is the so-called llms.txt file: similar to robots.txt for search engine crawlers, but aimed specifically at AI crawlers. It signals which parts of a website are relevant and accessible for AI systems. Not every LLM evaluates it systematically yet, but setting it up requires little effort and sends a clear early signal in a rapidly evolving discipline.
How to Check Your Current AI Visibility Yourself
Before investing in measures, an honest assessment of your current state is worthwhile. This works without a specialised tool as a quick, free entry point:
- 1Formulate 3 to 5 typical customer questions, e.g. about local AI consulting firms, custom software developers in Bavaria, or IT service providers for mid-sized businesses.
- 2Ask these questions manually in ChatGPT (with Memory disabled), Gemini and Perplexity, each in a fresh browser tab without a logged-in account.
- 3Note which companies are mentioned and whether your company is among them.
- 4Check which sources the AI cites as a basis. This shows you where your competitors are visible and you are not.
- 5Repeat this test at least monthly to track changes over time.
For systematic ongoing measurement, a specialised tool is recommended. Vjus.ai, a tool by NoviCogi, runs prompts against all major LLMs daily and analyses whether and where a company appears, which competitors are mentioned, and which sources the AI draws its answers from. This is the foundation for running AI-based visibility optimisation on data rather than gut feeling.
Overview of Measures: What Works, What Does It Cost?
| Measure | Effort | Expected impact | Time horizon |
|---|---|---|---|
| Expand service pages with FAQ sections | medium | high | 1 to 3 months |
| Create directory listings (feedbax.de, Sortlist etc.) | low | medium to high | 1 to 4 weeks |
| Publish blog articles on core audience questions | medium to high | high | 2 to 6 months |
| Embed structured data (FAQPage, Organization, LocalBusiness) | medium | medium | 2 to 4 weeks |
| Create llms.txt | low | low to medium | 1 day |
| Measure AI visibility regularly | low (with tool) | high as control basis | ongoing |
Those absent from LLM answers lose a channel that works without click costs and is often used precisely when a purchasing decision is being prepared.
AI Visibility as Part of Your Overall Strategy
AI visibility is not an isolated marketing project but a cross-functional topic: it touches your website structure, your content approach, your technical infrastructure and your market positioning. Companies that already pursue a clear IT and technology strategy have a head start here, because a consistent, well-structured digital presence is the foundation of both disciplines, SEO and GEO.
If you are just starting out and unsure where the greatest lever lies for your company, our AI adviser can provide initial orientation, or contact us directly to jointly analyse how you appear in the AI answers relevant to your customers.
Frequently Asked Questions About AI Visibility
Frequently asked questions
- What is the difference between SEO and GEO (Generative Engine Optimization)?
- SEO aims to rank as highly as possible in the link results of classical search engines like Google. GEO optimises visibility in the direct answers of large language models: in ChatGPT, Gemini, Perplexity and similar AI assistants. Both disciplines share many foundations (quality content, technical cleanliness, external links), but have different ranking mechanisms. A good Google ranking is a positive GEO signal but does not guarantee a mention in LLM answers.
- Which AI assistants are most relevant for B2B companies?
- In German-speaking markets, the most widely used AI assistants for information search are ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic) and Perplexity. Particularly significant for B2B service providers are ChatGPT and Perplexity, as they are explicitly used as research tools. Google Gemini is gaining additional weight through integration into Google Search (AI Overviews), since these answers are visible directly within classical search results.
- How long does it take for GEO measures to show measurable results?
- This depends on the measure. Directory listings and technical adjustments (structured data, llms.txt) can show first effects within 2 to 4 weeks, once AI crawlers have picked up the changes. Content measures such as new FAQ pages or blog articles typically take 2 to 6 months, as LLM training data is not updated daily. For web-enabled LLMs like ChatGPT Search or Perplexity, effects are visible faster than for purely training-based answers.
- Is AI visibility genuinely relevant for mid-sized B2B companies already?
- Yes, and the trend is accelerating. B2B decision-makers are increasingly using AI assistants for pre-selection of service providers, market overviews and due-diligence research. Those who lay GEO foundations now build a head start. Most measures (FAQ pages, directory listings, structured data) are good practice for any website regardless and also benefit classical SEO.
- Do I need a specialised tool to measure my AI visibility?
- For a first overview, the manual spot check is sufficient: ask typical customer questions in several AI assistants, record the results, repeat monthly. For systematic ongoing measurement, meaning which prompts, competitors and sources are cited and how visibility changes over time, a specialised tool such as Vjus.ai is needed. It runs prompts against all major LLMs daily and provides structured analyses and optimisation recommendations.
AI visibility will not replace classical website SEO, but it complements it as an independent dimension. Companies that lay the foundations now will benefit mid-term from a channel that works without paid advertising and in which trust and authority count, not click budgets.
AI integration and automation for the SME sector, NoviCogi
This article was created with AI assistance and editorially reviewed.
