How to Know Which LLMs Mention Your Company

Why this is harder than a simple search

If you want to know which LLMs mention your company, you cannot check one prompt in one tool and call it done. AI answer engines do not work like a search results page with a stable ranking. ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews can all answer the same question differently because they use different models, retrieval systems, indexes, freshness rules, and citation logic.

A brand mention can come from two places. The first is model knowledge, meaning information learned during training or from internal knowledge updates. The second is retrieval, meaning the engine searches the web or a proprietary index during the answer and uses those sources to build a response. In practice, most commercial queries use some mix of both.

That means your company may appear in Perplexity because it cites a recent list article, but not in ChatGPT because the same source is not retrieved. You may appear in Gemini for a local or category query, but not in Claude for a comparison prompt. The only reliable approach is to test the actual answer engines with the actual questions your buyers ask.

Build a prompt set before you test

Start with the questions where a mention would matter commercially. Do not only test your company name. If a user asks, “What is Acme?” the engine will likely mention Acme. That does not prove category visibility.

Build prompts across these groups:

1. Category discovery, such as “What are the best payroll tools for startups?”
2. Use case queries, such as “What software helps agencies manage client reporting?”
3. Comparison queries, such as “HubSpot alternatives for B2B SaaS teams.”
4. Recommendation queries, such as “Recommend a vendor for SOC 2 automation.”
5. Problem queries, such as “How can I reduce cloud waste across AWS accounts?”
6. Local or market specific queries, if relevant.

For each prompt, define what counts as a mention. Track direct recommendations, brief mentions, comparison table entries, citations, and cases where the engine describes your category but names only competitors.

Test across the engines your buyers use

At minimum, check ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. If your audience uses Microsoft Copilot, add it too. For each engine, test both branded and non branded prompts.

Use a neutral setup when possible:

1. Start a new chat or clean session.
2. Avoid leading language like “include our company.”
3. Set location if the query has local intent.
4. Record the date, model, mode, and whether web browsing or search was active.
5. Save the full answer, not just the final list of brands.
6. Capture cited URLs where the engine provides them.

Run important prompts more than once. LLM answers are probabilistic, and retrieval can change by time, location, or query wording. A single miss does not always mean invisibility. A repeated miss across several related prompts usually does.

This is where a platform like Seeno helps. Instead of manually checking dozens of prompts across engines, Seeno tracks whether your brand is recommended, where it is missing, and which sources or competitors appear instead.

What to record in your LLM visibility audit

A useful audit needs more than a yes or no column. Create a sheet with these fields:

1. Prompt
2. Engine
3. Model or answer mode
4. Date tested
5. Was your company mentioned
6. Position in the answer
7. Sentiment or framing
8. Was it recommended or only named
9. Competitors mentioned
10. URLs cited
11. Missing source types, such as reviews, listicles, docs, or analyst pages

Position matters because many users read only the first few recommendations. Framing matters because an LLM can mention your company as “better for enterprises” or “less suitable for small teams,” which may or may not match your target market.

Citations are especially important. If Perplexity recommends three competitors and cites the same buying guide twice, that guide is probably influencing the answer. If Google AI Overviews cites a review site that does not list you, you have found a distribution gap, not just an AI problem.

Why your company may not be mentioned

When an LLM leaves out your company, the cause is usually one of these mechanics:

1. The engine does not clearly understand your entity. Your site may not explain what you are, who you serve, and which category you belong to.
2. Your pages lack evidence. AI systems favor specific proof, such as customer names, integrations, pricing facts, certifications, benchmarks, reviews, and documented use cases.
3. Competitors appear in stronger retrieved sources. Answer engines often pull from list posts, review sites, comparison pages, documentation, news, Reddit, and high authority niche publications.
4. Your content is too branded. If every page says you are “the leading platform” but never answers buyer questions directly, it gives the model little useful material.
5. Your site is hard to crawl. Important content hidden behind scripts, PDFs, gated pages, or vague navigation may not be easy for retrieval systems to use.
6. Your category language is inconsistent. If your site says “revenue intelligence,” but buyers ask for “sales forecasting software,” the match can be weak.

How to improve your odds of being mentioned

You cannot force an LLM to recommend your company, but you can improve the inputs it relies on.

Create clear category and use case pages that answer buyer prompts directly. Include who the product is for, when it is not a fit, core features, integrations, proof points, and comparison context. Publish pages that explain alternatives and tradeoffs honestly. Get included in the third party sources that engines already cite for your category. Keep review profiles, partner directories, docs, and company descriptions consistent.

Also make your facts easy to extract. Use plain HTML, descriptive headings, schema where appropriate, concise product descriptions, and specific claims backed by evidence. If you want to be recommended for “best compliance software for fintech,” your site and external mentions should clearly connect your brand to compliance, fintech, and the relevant proof.

The practical answer

To know which LLMs mention your company, test a structured set of buyer prompts across the major answer engines, repeat the tests, record mentions and citations, and compare your visibility against competitors. Then trace missing mentions back to the sources and facts the engines use.

The goal is not to trick the model. The goal is to make your company easy to understand, easy to verify, and easy to recommend when the user’s question genuinely fits what you sell.