# How to Monitor What AI Assistants Say About Your Brand By Jeremy Prasetyo, Co-CEO, Lanesra AI · 2026-10-15 · AI Visibility > AI assistants can shape how buyers perceive your brand before they visit your website. Learn how to track mentions, position, sentiment, citations and inaccuracies across AI search, then improve the sources shaping those answers. Source: https://lanesra.ai/blog/how-to-monitor-what-ai-assistants-say-about-your-brand # How to Monitor and Improve What ChatGPT, Gemini and Other AI Assistants Say About Your Brand > Learn how to check, track and fix how ChatGPT, Gemini, Perplexity and Google AI Overviews describe your brand, and how to choose a monitoring tool. ## What AI search visibility is and why agencies and B2B software teams need to track it AI search visibility is how a brand shows up when people ask ChatGPT, Gemini, Perplexity, Google AI Overviews and other answer engines a question. It is not a single score. It is five things measured together: - **Mentions:** whether the brand is named at all in answers to the questions your market asks. - **Citations:** whether the answer links to or draws on your pages, or on someone else's pages about you. - **Position:** where the brand lands inside the answer. Being listed first, or being one of several alternatives near the end, shapes how prominent it is. - **Sentiment:** how the answer frames the brand. It may recommend it, qualify it with caveats, or dismiss it. - **Accuracy:** whether the facts are right, such as what the product does, who it is for, what it is called and what it offers. The first four tell you how visible you are. The fifth tells you whether that visibility helps or harms you. A brand can be mentioned often and still be described with outdated or wrong details. ### Why it matters now More buyers use an AI assistant as an early step in research, before they visit any website. The answer they receive often forms the first impression of your brand, and you did not write it. Traditional search rankings do not show this. A page can rank well on Google and still be absent from, or misdescribed in, an AI-generated answer. For more on this shift, see [AI Is Becoming the First Step in the Buying Journey](https://lanesra.ai/blog/ai-is-becoming-the-first-step-in-the-buying-journey) and [Why Being Number One on Google Is No Longer Enough](https://lanesra.ai/blog/why-being-number-one-on-google-is-no-longer-enough). ### Why agencies need to track it Clients now ask agencies: "What does ChatGPT say about us?" Answering requires evidence, not impressions. A repeatable record of mentions, position, sentiment and competitor presence gives an agency something concrete to report, a baseline to measure against, and a way to show whether its work changed what the models say. Because clients often sit in different markets, the record should be split by engine and, where relevant, by language. ### Why small B2B software teams need to track it A small software company rarely has a large footprint of third-party coverage, so an AI model may fill gaps with guesses. It may describe the wrong category, list features you do not have, or name competitors in your place. Each of these can reach a prospect before your sales team does. Tracking lets a small team find these errors early and fix them at the source, rather than learning about them from a lost deal. ### The four questions this guide answers 1. How to monitor how ChatGPT and other AI assistants describe your brand. 2. Which tools track brand mentions and sentiment, and how to choose one. 3. How to find and fix inaccuracies in what models say about you. 4. Which answer engine optimization strategies improve your visibility in AI-generated answers. If you want the underlying measurement concepts first, [The Seven Layers of AI Visibility Most Brands Are Not Measuring](https://lanesra.ai/blog/the-seven-layers-of-ai-visibility-most-brands-are-not-measuring) covers them in more depth. ## How to check what ChatGPT, Gemini, Perplexity and Google AI Overviews say about your brand You can monitor how ChatGPT and other AI assistants describe your brand by asking them the questions your buyers ask, recording the answers on a schedule, and comparing them with the facts you know to be true. No special software is needed to start. The method below gives you a baseline you can report on. ### Step 1: Build a prompt list Write a list of prompts that reflect how real buyers phrase questions. Group them so the results are easy to read: - **Brand prompts:** "What is \[Brand\]?", "Who is \[Brand\] for?", "What does \[Brand\] cost?", "Is \[Brand\] any good?" - **Category prompts:** "What are the best \[category\] tools for a small B2B company?" - **Comparison prompts:** "\[Brand\] vs \[Competitor\]", "Alternatives to \[Competitor\]" - **Problem prompts:** "How do I \[problem your product solves\]?" Keep the wording fixed once you begin. Changing a prompt changes the answer, and you will not be able to compare runs. For more on choosing prompts, see [What Is Prompt Coverage?](https://lanesra.ai/blog/aeo-basics-what-is-prompt-coverage). ### Step 2: Run the list on every engine Run each prompt in ChatGPT, Gemini, Perplexity and Google AI Overviews. Use a fresh session or a logged-out window where you can, so earlier conversations do not shape the reply. Note the date, the engine and, where shown, the model version. Models differ in what they recommend, which is why one engine is never enough; [Why Do Different AI Models Recommend Different Brands?](https://lanesra.ai/blog/aeo-basics-why-do-different-ai-models-recommend-different-brands) explains why. Answers vary between runs. Run each prompt two or three times and record what appears consistently. ### Step 3: Log the answers Copy this template into a spreadsheet, one row per prompt per engine per run: Date Engine Prompt Brand mentioned? (Y/N) Position (1st, 2nd, listed, absent) Sentiment (recommends / qualifies / dismisses) Sources cited Competitors named Inaccuracy (describe) \[date of run\] ChatGPT Best \[category\] tools for small B2B teams Y 3rd of 5 Qualifies review site, a blog post Competitor A, B Says we lack \[feature\] \[date of run\] Perplexity What is \[Brand\]? Y Direct answer Recommends our About page None None Also save the full text of each answer, or a screenshot, as evidence and to see wording changes later. ### Step 4: Flag inaccuracies Keep a short "source of truth" document with your correct facts: product name, category, features, audience, pricing model and founding details. Compare each answer with it and classify every mismatch: - **Wrong:** a false statement, such as a feature you do not have. - **Outdated:** something once true, such as an old product name. - **Missing:** a fact that matters to buyers but is never mentioned. - **Misplaced:** the wrong category or audience. An inaccuracy that appears on several engines usually has a shared source, which you will trace below. ### Step 5: Repeat on a schedule Re-run the list every two to four weeks, and after any significant site change or product launch. Compare mentions, position and sentiment with the previous run. For agencies, a one-page summary per client, showing change since the baseline and open inaccuracies, is a good start. ### Where manual checking runs out The manual method is reliable but slow. The number of answers to review multiplies with every prompt, engine, repeat run, client and language. That is when many teams look at a dedicated tool. ## What to look for in a tool that tracks brand mentions and sentiment in AI search A good monitoring tool automates the manual method and adds what a spreadsheet cannot: scale, history and reporting. Ranked-list pages on this topic tend to agree on similar criteria. Use the checklist below to compare any platform, including ours, and ask each vendor to show the feature working on your brand. ### A seven-point checklist **1\. Engines covered.** Check which engines the tool queries: at minimum ChatGPT, Gemini, Perplexity and Google AI Overviews, and ideally others your market uses. Ask whether it reports by engine or blends them into one number. A blended score hides the differences between models. Ask how it handles model versions, which change over time. **2\. Prompt tracking.** The tool should let you set your own prompt list, keep the wording fixed, and re-run it on a schedule, with repeat runs of the same prompt. Ask whether you can group prompts by type and by market or language. **3\. Sentiment.** Look for a clear definition of how sentiment is judged. Useful output shows whether the answer recommends, qualifies or dismisses the brand, and lets you read the underlying text. **4\. Competitor comparison.** Visibility only means something relative to alternatives. The tool should track named competitors on the same prompts and show who appears, who is placed first, and how each is framed. **5\. Citation sources.** The tool should show which pages and domains answers draw on, so you can trace a wrong statement to its origin. **6\. Accuracy flagging.** Ask whether you can enter your correct facts and have mismatches surfaced, and whether flagged items can be tracked as open or resolved. **7\. Client-ready reporting.** For agencies this is often decisive. Look for dashboards or exports a client can read without explanation, separate client workspaces, and white-label output. A small B2B team may need only a simple recurring summary. ### Questions that separate tools - Can you see the full answer text, not only a score? - Is the method documented well enough to explain to a client? - Does the data tell you what to change, or only what happened? [Tracking AI Visibility Is Not Enough: Here Is What to Do With the Data](https://lanesra.ai/blog/tracking-ai-visibility-is-not-enough-here-is-what-to-do-with-the-data) covers that gap. - Can you test it on your own brand before committing? ### How Lanesra maps to the checklist Lanesra AI is an industry-enhanced AEO/GEO platform for brands that want to be found, trusted and recommended inside AI-generated answers. Where its features match the criteria above: - **Visibility** measures how often a brand appears when its market asks relevant questions, split by factors such as model and language. This corresponds to criteria 1 and 2. - **Position** shows where a brand lands inside the generated answer, and so how prominent it is relative to alternatives. - **Sentiment** shows how an AI system frames the brand, including whether it recommends, qualifies or dismisses it. This matches criterion 3. - **Perception** describes how AI systems frame the brand, for example as innovative, established, specialist, premium, affordable, trusted or controversial. It helps with the framing questions behind criterion 6. - **Competition analysis** supports multi-brand tracking, so you can monitor how competitors are positioned across AI engines, including their Visibility, Share of Voice, Sentiment, Position in answers and Retrieval Rates. This matches criterion 4. - **Agency reporting:** agency workflows are designed to support client-facing and white-label reporting, which relates to criterion 7. No single tool fits every team. A small B2B software company may value simplicity and accuracy checks most, while an agency will weigh multi-client reporting heavily. Score each candidate against the seven points, test it on your own prompts, and choose the one whose answers you can explain to a client. ## How to fix inaccurate AI descriptions of your brand You fix an inaccurate AI description by finding the pages the answer draws on, correcting the facts on your own site so they are clear and consistent, and re-testing with the same prompts. You cannot edit a model's answer directly. What you control is the information your own site provides, and every action below stays on your own site. ### Step 1: Find the sources the answers draw on Start with the inaccuracies in your log. For each one, open every page in the "Sources cited" column and ask: - **Is the wrong statement on one of your own pages?** An old pricing line, a retired product name or a vague feature description is the most common cause. - **Is your site silent on the point?** If no page states the fact plainly, the model may infer it or guess. - **Is the statement on a page you do not own?** Note it, but treat it as a signal about what your own pages fail to say clearly. Where an engine shows no citations, copy the wrong phrase and search for it on your site. You can also ask the engine "What is your source for that?", though the reply is not always reliable, so confirm against the pages. When the same error appears on several engines, a shared source is the likely cause. See [Which Websites Influence What ChatGPT Says About Your Brand](https://lanesra.ai/blog/which-websites-influence-what-chatgpt-says-about-your-brand) and [What Is Source Authority in AI Search?](https://lanesra.ai/blog/aeo-basics-what-is-source-authority-in-ai-search). ### Step 2: Correct your own site Error type Typical cause on your site Fix Wrong A feature page that implies a capability you lack Rewrite it to state exactly what the product does and does not do Outdated Old names, prices or screenshots on live pages Update or remove them, including blog posts and docs Missing The fact appears nowhere in plain text Add it to the About, product or FAQ page Misplaced No clear statement of category or audience Open the home and About pages with one sentence naming both Then apply these practices: - **Write a clear About page.** Lead with a plain definition. Example: "\[Brand\] is a \[category\] for \[audience\] that \[main function\]." - **Write a clear page for each product.** State what it does, who uses it and what it integrates with, in text rather than only in images or scripts. - **Keep facts consistent.** Use your source-of-truth document so name, category, features and pricing model read the same on the home page, product pages, footer and documentation. - **Retire conflicting pages.** Remove or update old announcements and duplicates that contradict current facts. Date any page that must stay for history. - **Confirm engines can read the site.** A correct page does nothing if a crawler is blocked. Check robots rules and whether key content depends on scripts crawlers may not run. ### Step 3: Re-test and track the fix Models do not update the moment you publish, so allow time. Re-run the same prompts, with the same wording, on the same engines, and compare against your baseline. 1. Mark each inaccuracy as open or resolved, with the date of the site change. 2. Re-run the affected prompts two or three times. 3. If an error persists, check whether the cited source changed and whether another page of yours still says the old thing. 4. Record the result. For agencies, a before-and-after view per client shows what the work changed. Expect gradual improvement rather than an overnight switch, and keep re-checking on your regular schedule. ## Answer engine optimization strategies that work, with examples The AEO strategies that work best make your pages easy for a model to read, quote and trust: direct answers near the top, structured headings, schema markup, comparison and FAQ content, and consistent brand facts. None guarantees a mention. Each removes a reason for a model to skip, misread or misquote your site. For the wider framework, see [AEO vs GEO vs LLMO](https://lanesra.ai/blog/aeo-vs-geo-vs-llmo) and [SEO vs GEO vs AEO](https://lanesra.ai/blog/seo-vs-geo-vs-aeo). ### 1\. Put a direct answer near the top of the page Answer engines tend to lift a clear statement that resolves the question. Open each page with a one- or two-sentence answer, then add detail. **Example:** A page titled "What is \[Brand\]?" begins: "\[Brand\] is a \[category\] for \[audience\] that \[main function\]." The pricing page begins with how pricing works, not a slogan. ### 2\. Use structured headings that mirror real questions Write headings as the questions buyers actually use, keep one idea per section, and follow a logical H2 and H3 order. **Example:** Instead of "Why us", use "Who is \[Brand\] for?" followed by a short paragraph naming the audience. Instead of "Features", use "What can \[Brand\] integrate with?" followed by a plain list. ### 3\. Add schema markup Schema markup is structured data that labels the facts on a page for machines. It confirms clear text rather than replacing it. Useful types for most B2B software sites include Organization, Product, FAQPage and Article. The markup must match the visible text exactly. **Example:** Add Organization markup to the home page with the official name, URL and a one-line description. Add FAQPage markup to a page whose questions and answers appear in the visible copy, and update it in the same edit when a price or feature changes. ### 4\. Publish comparison and alternatives content Buyers ask models to compare options. If you do not publish a comparison, it is written from other sources. Be accurate about competitors and clear about where you fit and where you do not. **Example:** Create "\[Brand\] vs \[Competitor\]: which is better for a small B2B team?" with a table covering audience, key capabilities, setup and limits, and state who each option suits, including cases where the other is better. See [Why Does ChatGPT Recommend My Competitors Instead of Me?](https://lanesra.ai/blog/why-does-chatgpt-recommend-my-competitors-instead-of-me) ### 5\. Build FAQ content around real prompts Your prompt log is the best source of FAQ topics. Every prompt where you were absent or misdescribed is a candidate for a short, direct answer. **Example:** If models get your pricing model wrong, add "How does \[Brand\] pricing work?" with a two- or three-sentence answer that makes sense on its own if quoted. ### 6\. Keep brand facts consistent everywhere When your name, category, features or audience differ between pages, the answer may blend or pick the wrong version. **Example:** If the home page says "analytics platform", the About page says "reporting tool" and the docs say "dashboard software", choose one category phrase and use it across all three. Do the same for the product name and audience description. ### How to prioritise Start with consistent facts and direct answers, then FAQ and comparison pages for prompts where you are missing, then schema to reinforce them. After each change, re-run the same prompts and record the result. For more, see [How LLMs Work and How to Make Them Recommend Your Product](https://lanesra.ai/blog/how-llms-work-and-how-to-make-them-recommend-your-product). ## Frequently asked questions ### What are some tools to track brand mentions and sentiment in AI search results? We're a marketing agency and need to report to clients. Start with a spreadsheet log to set a baseline, then move to a dedicated platform once prompts, engines and clients outgrow it. Compare any tool against the seven-point checklist above, paying close attention to sentiment, competitor comparison and client-ready reporting. Lanesra AI offers Visibility, Position, Sentiment and Competition analysis, and its agency workflows are designed to support client-facing and white-label reporting. ### Can you recommend platforms that analyze how AI models like GPT-4 and Gemini talk about our brand? We need to identify and fix inaccuracies. Look for a platform that reports by engine, shows the full answer text and sources cited, and lets you flag mismatches against your correct facts. Several vendors offer this, so test each on your own prompts. Lanesra's Perception and Sentiment show how AI systems frame a brand, and the fixing steps above explain how to correct the cause on your own site. ### How can I monitor how ChatGPT and other AI assistants describe my brand? Build a fixed list of buyer prompts, run it on ChatGPT, Gemini, Perplexity and Google AI Overviews, and log each answer in the template above. Compare every answer with a source-of-truth document, mark inaccuracies, and repeat every two to four weeks. This gives a small B2B software team a reliable baseline without special software. ### What strategies work best for answer engine optimization? The clearest tactics are direct answers near the top of pages, question-style headings, matching schema markup, fair comparison pages, FAQ content built from real prompts, and consistent brand facts. Start with those that fix the errors in your log, then re-run the same prompts after each change to show what moved. ### How long do fixes take to show up in AI answers? Expect gradual change rather than an overnight switch, because models and their sources do not update the moment you publish. Re-test with unchanged prompts on your regular schedule and record each inaccuracy as open or resolved. ## See how Lanesra tracks this across models If you would rather not run this by hand, see how Lanesra AI tracks visibility, position, sentiment and perception across AI models, and ask about a trial or demo at [lanesra.ai](https://lanesra.ai). For more guides, browse the [Lanesra resources](https://lanesra.ai/blog).