How to Get Into ChatGPT Answers: GEO for B2B Websites

October 12, 2026 · Bartłomiej Spasowski, Founder, modulla · EN

What is GEO and why does ChatGPT ignore your website?

Generative Engine Optimization (GEO) is a set of optimization practices that make a website cited and recommended by language models such as ChatGPT, Perplexity, or Google Gemini. Unlike classic SEO, which competes for position on a list of links, GEO competes to get a brand included in the synthesized response generated by AI [1][4].

Let's be direct: ChatGPT doesn't browse your website the way Google does. It retrieves text fragments it previously indexed, evaluates their information density, and builds an answer from that. If your content isn't structured for this mechanism, you can have an excellent Google ranking and zero presence in AI answers.

The business problem: traffic you don't see in analytics

In Poland, over 13 million users interact with language models on a regular monthly basis [9]. Customers increasingly skip traditional search engines and ask AI assistants directly for service recommendations, product comparisons, and purchasing advice [9][10].

The mechanism is simple and painful for unaware companies: ChatGPT lists 2–3 brands in response to a buying question. If your name isn't there, you lose the customer before they ever reach your website [9]. On top of that, when a customer does land on your site via an AI recommendation, GA4 analytics records that traffic as direct (Direct) or branded, hiding the true acquisition source in what's known as the Dark Funnel [9][10].

Add to that Gartner's forecast of a 25% decline in traditional search engine query volume in favor of conversational AI assistants [8]. Classic SEO isn't going away, but it's no longer the only game worth playing.


Approach	Classic SEO	GEO (AI Search)
Goal	Position on the link list (SERP)	Citation in AI-synthesized response
Currency of success	Clicks (CTR)	Citations, brand mentions
Primary signal	Keywords, backlinks	Information density, entity authority
Content format	Long page with keywords	Inverted pyramid, Q&A, attributed data
Measurement	Keyword position, organic CTR	Answer Presence (AP), AI Citation Frequency (AICF)
Traffic conversion	Average 1.76% for Google organic traffic [8]	14.2–15.9% for ChatGPT traffic [8]

How ChatGPT Search works: RAG architecture in brief

ChatGPT Search is built on a multi-stage process called RAG (Retrieval-Augmented Generation) [1][4]. The process works as follows: the user's query is broken into smaller semantic sub-queries, the engine searches external indexes for matching page fragments, and a reranking algorithm selects the most valuable paragraphs, which are passed to the model as context for building the answer [1][4].

Here is where the difference begins: the model doesn't evaluate an entire page, but text fragments. A fragment that doesn't contain precise, verifiable information within its first few dozen words is eliminated at the reranking stage. Research shows that 44.2% of all citations generated by LLMs come from the first 30% of a page's content [8][10]. The key answer must appear directly under the heading, not at the end of a section.

An important technical dependency: 87% of pages cited in ChatGPT Search overlap with the top results in Bing's organic index [10]. Companies focused exclusively on Google that neglect Bing Webmaster Tools never even enter the candidate pool at the retrieval stage [10].

The four pillars of GEO: what the market does

1. Technical access: bots and indexing

The first step is opening access in robots.txt for OpenAI crawlers. It's worth distinguishing two separate bots: GPTBot handles training of base models, while OAI-SearchBot and ChatGPT-User power live search [5][14]. Blocking the latter removes your site from ChatGPT Search results entirely [5][14].

In parallel, it's worth submitting your sitemap in Bing Webmaster Tools. Due to ChatGPT Search's dependence on the Bing index, failing to submit means failing to enter the candidate pool in the RAG system [10].

2. Content engineering: data, citations, inverted pyramid

Researchers from Princeton University, Georgia Tech, and IIT Delhi (Aggarwal et al., KDD 2024) created the GEO-bench benchmark covering 10,000 queries and proved that structured content modification increases visibility in AI answers by 30–40% [3][4][10]. Three tactics produce the greatest results:

Adding hard numerical data instead of qualitative declarations: visibility in AI answers increases by +37–40% [3][4][10].
Citing authoritative sources with inline references: visibility increases by +30–40% [3][4][10].
Verbatim expert quotes with attributed authorship: visibility increases by +30–32.8% [3][4][10].

The flip side: excessive keyword repetition (keyword stuffing) reduces semantic coherence and causes a 12.4% drop in visibility [10]. Subjective marketing claims without data backing (best in class) result in an 18.2% drop [10].

3. Structured data: JSON-LD and semantic graphs

Language models operate on entities, not keywords [1][6]. An entity is a defined subject: a brand, person, product, or article. Linked JSON-LD structured data with @id nodes unambiguously define relationships between author, organization, and content, eliminating semantic ambiguity in the vector index [12][21][22].

A practical set of Schema.org types useful for B2B sites: Organization, Person, Article or TechArticle, FAQPage, Service. Connecting these nodes with sameAs links to Wikidata, LinkedIn, or industry profiles strengthens entity authority in RAG systems [12][21][25].

4. The llms.txt file: a shortcut for AI agents

The llms.txt standard is a Markdown-format text file placed in the root directory of a site (/llms.txt) [13][27][28]. Its purpose is straightforward: to give AI agents condensed knowledge about the site in a computationally low-cost format, without needing to parse heavy HTML [13][26][28].

The file typically contains: a description of the brand and its offering, links to key subpages with a one-sentence description of each, and basic facts about the organization and its reach. This is not a technical requirement for ChatGPT, but a convenience that lowers the token cost of parsing and increases the likelihood of correct model interpretation [26][27][30].

Practical applications: what the B2B market does

Implementation results documented by GEO practitioners show three patterns that work regardless of industry:

Category-defining pages

When AI explains a category ("what is X?"), it cites the most complete, most authoritative definition in the index [7][8]. B2B companies that build dedicated subpages defining their product category (rather than just describing their own product) win citations even with a lower Google ranking.

Evidence from the market: in one analyzed implementation, a page ranked 5th in organic SERP, after adding solid citations and hard data, recorded a 115.1% increase in visibility in AI answers [4][10].

Comparison pages and feature matrices

Users ask comparative questions ("X or Y for a manufacturing company?"). Structured comparison tables with precisely described feature differences are highly favored by RAG systems at the reranking stage [7][8]. This is particularly effective in the B2B segment, where purchasing decisions involve comparing multiple solutions.

Grounding Pages: factual reference pages

Grounding Pages are dedicated subpages containing concise, verifiable facts about an organization: scope of services, team data, achievements, definitions of key concepts [11]. Their role is precise: they prevent AI hallucinations and anchor models to accurate brand data [11]. One documented implementation allowed a new domain with no link profile to achieve active citations in ChatGPT, Perplexity, and Google Gemini within three weeks [11].

How to measure AI visibility: metrics that will replace keyword rankings

Classic keyword position tracking has no place in a generative environment, because the model's answer is probabilistic and changes between queries [8]. The market has developed its own metrics:

Answer Presence (AP): whether the brand appeared in the response to a given query (yes/no).
AI Citation Frequency (AICF): how often a specific URL is cited in a controlled sample of queries [8].
Share of Model (SoM): the percentage share of the brand's citations versus competitors in the same sample [8].

Measurement requires regularly querying a set of 25–100 queries with purchase intent, weekly or monthly, with results recorded. A single check is only a snapshot and provides no basis for drawing conclusions about trends [8].

Traffic attribution is a separate challenge. It's worth adding custom channel groups to GA4 covering sessions from chatgpt.com, perplexity.ai, gemini.google.com, and claude.ai, to extract AI traffic from the Direct bucket [8].

First-mover advantage and what it means

The core point is this: GEO is not another channel to optimize — it's a contest over whose brand AI recognizes as the authoritative source in a given category. Over 47% of brands have no GEO strategy at all [10]. Early implementation allows a brand to become embedded in RAG indexes and model knowledge bases, building an advantage that grows with every new article and every new Grounding Page [10].

The correlation between Google ranking and AI citation is weakening. In mid-2025, around 75% of pages cited by AI came from the top ten organic Google results; by early 2026, that figure had dropped to 17–38% [8]. A high Google ranking no longer automatically means presence in AI [8].

FAQ: Getting your website into ChatGPT

Does GEO replace classic SEO?

It doesn't replace it — it complements it. ChatGPT Search indexes sites primarily through Bing, so a good organic ranking still increases the chance of entering the RAG candidate pool [10]. GEO adds a layer of content engineering and structured data that determines whether a page is cited, not just indexed.

How quickly do GEO efforts show results?

Documented case studies show results in the range of 3–12 weeks. A new domain with correctly implemented Grounding Pages and JSON-LD achieved active citations across three AI engines within three weeks [11]. For an existing domain with established authority, results appear faster, though measuring them requires regular AP and AICF monitoring [8].

What is the llms.txt file and does every site need one?

The llms.txt file is a condensed summary of a site's content in Markdown format, placed in the root directory [13][28]. It is not a technical requirement, but a convenience for AI agents that lowers the token cost of parsing and increases the precision of content interpretation by the model [26][27][30]. For B2B sites with a complex offering, it is one of the fastest and cheapest GEO implementations available.

Can you get into ChatGPT answers without a Google presence?

Yes, though it's a harder path. New domains with zero link profiles but with correct technical structure, Grounding Pages, and JSON-LD achieved citations within a few weeks [11]. For most companies, the most practical approach is a combination: a solid Bing ranking (via Bing Webmaster Tools), open access for OpenAI bots, and well-structured content built for the RAG mechanism [10][14].

If you want to find out how your brand appears in AI answers and what to change first, get in touch.