GEO Glossary: AI Search Terms B2B Marketers Should Know

The language AI tools use to describe your company is becoming the language your buyers hear first. B2B software buyers now routinely ask ChatGPT, Perplexity, and Gemini to compare vendors, explain categories, and shortlist solutions before they ever visit a website or speak to a sales rep. If you don’t understand how these tools decide what to surface, you’re flying blind. This glossary of AI search terms gives B2B marketers a practical reference point: clear definitions, real context, and honest guidance on what actually matters for visibility in generative search results. Whether you’re a Head of Marketing at a SaaS company or a founder trying to figure out where GEO fits alongside your existing SEO programme, these are the terms you need to have down cold. We’ve kept the jargon to a minimum and the practical detail high, because a glossary is only useful if it helps you do something differently tomorrow.

Core GEO terms

These four terms form the foundation of everything else in this glossary. If you’re going to invest time understanding any AI search concepts, start here. They describe the technology, the practice, and the outputs that determine whether your brand shows up when a buyer asks an AI tool a question about your category.

Generative Engine Optimisation (GEO)

GEO is the practice of making your brand, content, and positioning visible inside AI-generated answers. Where SEO focuses on ranking in traditional search engine results pages, GEO focuses on being cited, referenced, or recommended by AI tools like ChatGPT, Perplexity, Gemini, and Claude.

GEO isn’t a replacement for SEO. Strong SEO foundations are actually an input to GEO, because AI models often pull from the same sources that rank well in Google. But GEO adds a layer of work around how AI models learn about your company: the sources they pull from, how consistently your positioning is described across the web, and whether you’re cited in the right contexts. For B2B SaaS companies in the £2M to £20M ARR range, this matters because buyers increasingly use AI tools to research software categories and compare vendors before they ever engage with your sales team.

At Gripped, a GEO audit is the starting point: checking how ChatGPT, Perplexity, Gemini, and Claude currently describe your company, what sources they pull from, and where competitors are cited instead.

Large language model (LLM)

An LLM is the AI system that powers tools like ChatGPT, Claude, and Gemini. It’s a machine learning model trained on vast amounts of text data, which it uses to generate human-sounding responses to prompts.

For marketers, the important thing to understand is that LLMs don’t search the internet in real time the way Google does (though some now have retrieval capabilities bolted on). They synthesise answers based on patterns in their training data, supplemented by whatever retrieval tools they have access to. This means your content needs to be present in the right places, described consistently, and structured clearly enough for these models to pick it up. A training data cutoff is the date after which the model has no knowledge unless it retrieves it live, so freshness and distribution both matter.

AI Overviews

AI Overviews are Google’s AI-generated summaries that appear at the top of search results for certain queries. They pull information from multiple web sources and present a synthesised answer directly in the search results page, often before any traditional organic listings.

For B2B marketers, AI Overviews represent a significant shift. A buyer searching for “best project management software for remote teams” might get a summary answer without clicking through to any website. Your content either contributes to that summary or it doesn’t. The sites that AI Overviews cite tend to have strong topical authority, clear structure, and consistent entity information. If you’re already doing solid content marketing and SEO, you’re better positioned than most, but you’ll need to pay attention to how your content is structured for extraction.

Retrieval-augmented generation (RAG)

RAG is a technique where an AI model retrieves relevant documents or data from external sources before generating its response. Instead of relying solely on what it learned during training, a RAG-enabled system searches a knowledge base or the web, then uses that retrieved information to produce a more accurate, up-to-date answer.

This is why GEO matters practically. When Perplexity answers a question about your software category, it’s often using RAG to pull from live web sources, reviews, documentation, and articles. The way LLMs now handle relevance through semantic and vector search means your content needs to match the intent behind a query, not just contain the right keywords. If your content is well-structured, authoritative, and present on sources these tools index, you’re more likely to be retrieved and cited.

Visibility and citation terms

Once you understand the core technology, the next question is: how do you measure whether it’s working? These three terms describe the outcomes and metrics that matter for GEO.

Citation

A citation in the GEO context is when an AI tool references your brand, content, or website as a source in its generated answer. In Perplexity, citations appear as numbered footnotes linking back to source URLs. In ChatGPT with browsing enabled, they appear as inline references. In Google’s AI Overviews, cited sources appear beneath the summary.

Citations are the closest thing GEO has to a “ranking.” If your company is cited when a buyer asks “what are the best B2B payment platforms,” you’re on the shortlist. If you’re not, you don’t exist in that conversation. Tracking citations requires manual prompt testing right now: running category queries, comparison queries, and problem queries across multiple AI tools and recording which brands appear. There’s no equivalent of Google Search Console for AI citations yet, so teams that monitor their AI search visibility consistently have a real advantage over those that check once and forget.

Entity

An entity is a distinct, identifiable thing that AI models can recognise and associate with attributes. Your company is an entity. So is your product, your CEO, your category, and your competitors. AI models build an understanding of entities from multiple sources: your website, LinkedIn, G2, Capterra, Crunchbase, Wikipedia, press coverage, and more.

The practical issue for B2B SaaS companies is consistency. If your LinkedIn says you’re a “revenue intelligence platform,” your G2 profile says “sales analytics tool,” and your website says “AI-powered CRM,” the AI model has three conflicting signals. It will either pick one, hedge, or leave you out entirely. Auditing and aligning your company descriptions, categories, and core value propositions across all public profiles is one of the highest-impact GEO activities you can do. It costs nothing but time, and it directly affects how AI tools describe you.

Share of voice in AI

Share of voice in AI measures how often your brand appears in AI-generated answers relative to your competitors for a given set of queries. It’s the GEO equivalent of share of voice in traditional media or search.

Measuring this is still manual. You pick a set of queries that represent your buyer’s research process: category queries (“best endpoint security tools for SMBs”), comparison queries (“Vendor A vs Vendor B”), and problem queries (“how to reduce customer churn in SaaS”). You run them across ChatGPT, Perplexity, Gemini, and Claude, then record which brands are mentioned, cited, or recommended. Do this monthly, and you’ll start to see patterns. Some companies dominate certain AI tools but are absent from others. Some appear for category queries but vanish for comparison queries. This data tells you where to focus your GEO effort. AI search is already changing how customers find businesses, and tracking your share of voice is the only way to know whether you’re gaining or losing ground.

Technical terms

These terms describe the specific mechanisms that help AI tools understand and extract information from your content. They’re more hands-on than the concepts above, and they’re where your development and content teams will spend time.

Structured data

Structured data is a standardised format for providing information about a page and classifying its content. It helps search engines and AI tools understand not just what’s on a page, but what it means. For example, structured data can tell an AI tool that a specific page contains a product with a price, a set of features, and customer reviews, rather than just a block of text.

Teams that fix their structured data tend to get cited more often because AI tools can extract clean, reliable information from their pages. For SaaS companies, the most relevant structured data types include Organisation, Product, FAQ, Article, and Review. If your website doesn’t have structured data implemented, you’re making AI tools work harder to understand your content, and they’ll often choose an easier source instead.

Schema markup

Schema markup is the specific vocabulary (from schema.org) used to implement structured data on your website. It’s the code that tells search engines and AI tools what type of content is on a page and how different pieces of information relate to each other.

For B2B SaaS websites, priority schema types include Organisation schema (your company name, logo, description, founders, social profiles), Product schema (features, pricing tiers, integrations), and FAQ schema (common buyer questions with direct answers). Implementing schema markup correctly is a technical foundation that supports both SEO and GEO visibility. Your development team can add it using JSON-LD format, which sits in the page’s HTML head and doesn’t affect the visible content. Test it with Google’s Rich Results Test tool to confirm it’s valid.

llms.txt

llms.txt is a proposed standard file (similar to robots.txt) that websites can place at their root domain to provide AI models with guidance about the site’s content. It typically includes a brief description of the organisation, links to key pages, and context about what the site covers.

This is still an emerging standard, and its adoption varies across AI tools. The honest position is that llms.txt is a low-cost signal rather than a guaranteed fix. It takes minutes to create and deploy, so there’s no reason not to have one, but don’t expect it to single-handedly transform your AI visibility. Think of it as one small piece of a larger GEO programme. The file should include your company name, a one-sentence description of what you do, links to your most important pages (product pages, pricing, key content pieces), and any category or positioning context that helps an AI model understand where you fit.

How these terms fit together

None of these terms exist in isolation. They form a connected system that determines whether your brand appears when a B2B buyer asks an AI tool about your category.

Your LLM visibility depends on your entity consistency. Your entity consistency depends on having aligned descriptions across your website, review sites, and social profiles. Your content gets retrieved through RAG when it’s well-structured, topically authoritative, and present on sources that AI tools index. Your structured data and schema markup help AI tools extract clean information from your pages. And your llms.txt file provides additional context about your organisation.

GEO ties all of this together as a practice. It’s the discipline of auditing where you stand, identifying gaps, and systematically closing them. The traditional keyword research approach misses many of the conversational queries that drive B2B procurement decisions in AI tools, which is why GEO requires its own research methodology alongside your existing SEO work.

A practical monthly cadence looks like this: run manual prompt tests across ChatGPT, Perplexity, Gemini, and Claude using your core category, comparison, and problem queries. Track which brands are cited. Compare month over month. Identify where your competitors are showing up and you’re not, then work backwards to understand why. Is it a content gap? An entity inconsistency? A missing third-party citation?

For SaaS and tech companies, Gripped builds GEO into its broader marketing programmes alongside SEO, content, and paid media. The work includes GEO audits, content architecture built around the questions buyers ask AI tools, and entity and authority building through consistent brand descriptions and third-party citations. The parts work together because they sit under one roof, which means your SEO content strategy directly feeds your GEO visibility.

The terminology in this glossary will keep evolving as AI search matures. New terms will appear, and some of these definitions will expand. What won’t change is the underlying principle: AI tools recommend brands they can verify from multiple consistent, authoritative sources. If you want to be on the shortlist, you need to be findable, describable, and credible across every source these models consult.

If you’re a marketing leader at a B2B SaaS or tech company and you want to understand where you currently stand in AI search, Gripped can run a GEO audit alongside a full growth assessment of your digital marketing. Get your free growth audit and find out what buyers see when they ask AI about your category.