--- title: "Underneath | GEO Agency: Get Cited and Recommended by AI" description: "Underneath is a generative engine optimization (GEO) agency. We get brands mentioned, cited and recommended by ChatGPT, AI Overviews, Gemini and Perplexity." canonical: "https://underneath.agency" published: 2026-09-26 updated: 2026-10-19 publisher: "Underneath (https://underneath.agency/agent)" entity: "https://underneath.agency/.well-known/entity.json" --- Generative engine optimization (GEO) agency # The answer is only the surface. *We work underneath it.* Underneath is a generative engine optimization agency. We help businesses become easier for AI-powered search and answer systems to understand, retrieve, describe and reference, including ChatGPT, Google AI Overviews and AI Mode, Gemini, Claude, Perplexity and Microsoft Copilot. When someone asks an AI assistant which company to choose, the answer can look simple. But an answer is built from information: claims, sources, entities, relationships, evidence, pages, reviews, the questions people ask, the information competitors have, and the things that are current, missing or conflicting. **Underneath investigates that information system, changes what matters, and measures what happens next.** [Talk to us about your business](https://underneath.agency/contact)[See the Underneath Method](https://underneath.agency/approach) Illustrative example · AI answer · 3 sources What’s the best accounting software for a growing business? For growing businesses, YourBrand is frequently recommended, citing its transparent pricing1 and wide range of integrations2. Owners on forums also highlight its onboarding support3 - 1yourbrand.com/pricing - 2yourbrand.com/integrations - 3reddit.com/r/smallbusiness - ChatGPT - Gemini - Perplexity - AI Overviews The AI assistants and AI search we get you mentioned, cited and recommended in - ChatGPT - Google AI Overviews - Google AI Mode - Gemini - Claude - Perplexity - Microsoft Copilot - Bing Underneath an AI answer ## What happens underneath an AI *answer*? A buyer asks: > “What’s the best accounting software for a growing business?” An AI system may return three recommendations. The customer sees the answer. We look underneath it. - ### What is the system trying to understand? Who are the companies? What category are they in? Who are they for? - ### What is being said? What claims are being made about each company? - ### What supports those claims? Which pages, reviews, publications, communities or other sources provide the evidence? - ### What is missing or conflicting? Where is information incomplete, outdated or inconsistent? - ### Why does one company appear while another does not? The answer tells us where to look. The information underneath tells us what to change. *Illustrative example. Not a representation of any one platform’s internal process.* Our method ## Understand first. Then change what *matters*. We don’t start with a list of pages to publish. We start with the questions your customers ask and the answers they receive. Then we work backwards. - ### Diagnose **Find out what the market, and the machines, actually understand about you**. We establish a baseline across AI visibility, search, customer questions, competitors, entities, sources, customer language and technical foundations. We look beyond mentions and rankings to understand the claims, evidence and relationships underneath the answer. - ### Architect **Decide what needs to become true**. The diagnosis becomes a prioritized plan across content, technical foundations, entities, sources, evidence and measurement. Every recommendation has a reason. Nothing is added simply to create activity. - ### Deploy **Change the information system around the business**. Depending on what the diagnosis finds, that can mean changing important pages, improving information architecture, strengthening technical foundations, clarifying entities, improving external sources or making important claims easier to support and reference. The work follows the problem, not the other way around. - ### Compound **Measure. Learn. Change. Test**. AI answers shift. Search results move. Competitors change. Businesses change. So the program does too. Every cycle starts with better evidence than the last. [Explore the Underneath Method](https://underneath.agency/approach) What makes us different ## We don’t optimize one page in *isolation*. Your website is one source. Your reviews are another. Your profiles are another. Your customers are another. Publishers, communities, directories and other sources all contribute to the information environment around your business. We look at how those pieces fit together. Then we identify where the system is helping you, where it is letting you down, and what deserves attention first. ### We ask seven questions. - ### Understanding Why do your best customers choose you? - ### Recognition Do search engines, AI systems and the sources they read recognize who you are? - ### Retrieval Can the right information be found when it matters? - ### Evidence Are the important claims supported? - ### Competition Where do competitors have a stronger information footprint? - ### Measurement What actually changed? - ### Learning What do we know now that we didn’t know before? **The questions are simple. Answering them well is the work.** [See how we work](https://underneath.agency/approach) Research ## We don’t just follow the industry. We *study* it. Underneath publishes original research into how AI search and answer systems actually behave. Not opinion pieces dressed up as research. Our studies publish the question, sample, method, limitations and underlying data so the numbers can be checked and reused. ### 23 studies. One principle. **If we make a claim about how AI systems behave, we want evidence behind it.** Our research has examined: AI crawlers : Which crawlers websites block, how sites serve information to AI agents, and how common formats such as `llms.txt` and Markdown actually are. AI search : How often AI Overviews appear, how their citations relate to traditional rankings, how AI Mode differs from AI Overviews, and which sources appear in answers. AI assistants : How ChatGPT, Gemini, Perplexity and Claude agree, or disagree, on recommendations. Recommendation stability : How answers change when the same question is repeated, reworded or asked in different countries. Sources and citations : Which pages assistants cite, how fresh those sources are, and where those citations come from. Local AI search : How AI recommendations compare with Google Maps and how accurately assistants represent local businesses. The findings are not a collection of assumptions about what AI “likes.” They are observations from defined datasets. What we’ve learned ## A few things we’ve *learned*. - ### AI recommendations are not fixed rankings. Across 80 buyer questions, all four assistants we studied recommended the same first pick for only 10.0% of questions. - ### Rewording the question can change the recommendation. Adding “on a tight budget” kept the same first brand only 15.3% of the time across three engines in our study, against 68.0% when the same question was simply asked again. - ### AI citations don’t simply mirror Google rankings. In our study of 4,051 AI Overview citations, only 28.7% of cited URLs ranked in the top 10 for the same search. - ### AI answers can use sources outside the traditional top 10. 17.9% of the AI Overviews we studied cited Reddit, and most of the cited threads were outside Google’s top 10 for the same search. - ### AI answers can contradict a business’s Google profile. Across 159 local businesses and four AI engines, 18.9% of answers contained a fact that differed from the business’s Google profile. These are not universal laws. They are measurements from defined studies, with published methods and limitations. **That distinction matters.** How we measure ## We measure the work in four *layers*. **Visibility → Evidence → Behavior → Business.** An AI mention is not automatically a business result, so we don’t collapse everything into one “AI visibility score.” - ### Visibility **What is being said?** Mentions, recommendations, descriptions, citations, competitive presence and persistence across important questions. - ### Evidence **What supports it?** Claims, sources, entities, corroboration, conflicts, freshness and information coverage. - ### Behavior **What did people do?** AI referrals, engagement, inquiries, applications, bookings and conversions. - ### Business **Did it matter?** Pipeline, customers, revenue, acquisition cost or the business metric that actually matters to you. A visibility increase is not automatically a revenue increase. A citation is not automatically a lead. A lead is not automatically a customer. **We measure each step on its own terms.** [See how we measure](https://underneath.agency/results) Results ## When the work produces results, we show the *evidence*. **No invented scores. No convenient stories.** When client results are available and approved, we show the baseline, intervention, measurement and outcome, not just a screenshot of an AI answer. Until a result is substantiated and approved, we label it accordingly. Because a good case study should answer four questions: - What was the problem? - What changed? - What happened afterward? - What can we actually attribute to the work? Our standard is simple: **a result is only proof if you can check it.** [How we report results](https://underneath.agency/results) What we do ## Generative engine *optimization*. GEO is the work of making a business easier for AI-powered search and answer systems to understand, retrieve, describe and reference. It builds on SEO, but asks a broader question: > Given everything published about this business, on its own site and elsewhere, what will an answer system understand, retrieve and say? Depending on the diagnosis, our work can include - AI visibility and answer analysis - Buyer-question and query research - Claim and evidence analysis - Content and information architecture - Technical GEO - Entity and information architecture - Source and evidence strategy - Monitoring and experimentation - Team enablement We don’t prescribe the work before we understand the problem. [See what’s included](https://underneath.agency/services/generative-engine-optimization#deliverables) Who we help ## Built for businesses where being chosen *matters*. - ### Home and local services Businesses where the recommendation can become a call, booking or visit. - ### Franchises and multi-location brands Businesses that need consistent information across markets and locations. - ### B2B software and technology Businesses competing for consideration, comparison and shortlist inclusion. - ### Financial services and insurance Businesses where accuracy, trust and compliance matter. - ### Healthcare and dental Businesses where reputation and local recommendation matter. - ### Legal and professional services Businesses where expertise and trust shape the decision. - ### Retail and ecommerce Businesses where product information, comparison and recommendation matter. - ### Hospitality and travel Businesses where local discovery and recommendation can directly influence demand. Engagement model ## One team. *One* accountable lead. Every engagement has one senior lead responsible for the strategy, priorities, decisions and overall program. Specialists join where the work requires them. You know who is responsible. You know what changed. You know what we learned. And you know what happens next. Every program runs on the weekly 30, a 30-minute review in the same slot every week, with a decision-maker from your side in the room. If a decision-maker cannot join the weekly review, we may not be the right fit. [How we work](https://underneath.agency/approach) Why Underneath ## Because the visible answer is not the whole *problem*. A customer sees a recommendation. We want to understand what produced it. A system cites a source. We want to understand why that source was there. A competitor appears in the shortlist. We want to understand what information supports their position. And when your business is missing, we want to know whether the problem is recognition, retrieval, evidence, representation, or something else entirely. **The answer is only the surface.** That’s why we’re called Underneath. [About Underneath](https://underneath.agency/about) Free strategy call ## The market is already asking. The question is whether your business is part of the answer. Tell us what you’re trying to change. We’ll take a first look at how your business appears across search and AI answers, what information sits underneath that visibility, and where we would start. [Get a first look at my business](https://underneath.agency/contact) --- This is the Markdown twin of https://underneath.agency. The HTML page is canonical. Publisher: Underneath, https://underneath.agency/agent. Site index: https://underneath.agency/llms.txt.