How to Check What ChatGPT, Gemini and Perplexity Say About Your Company

To check what ChatGPT, Gemini or Perplexity says about your company, ask each assistant the questions a sceptical buyer would ask, not just your name; run them in every mode the assistant offers; record the full answer and every citation; and repeat the run on a schedule. One question in one assistant on one day proves almost nothing, because answers vary between runs, between browsing and memory modes, and between surfaces. The procedure below takes about an hour the first time and is the same one we run at the start of an AI reputation audit; the audit adds coverage, repetition over days and the source tracing that turns a bad answer into a repair plan.

Why asking once is not a check

An AI answer about your company is assembled on the spot from two layers: a frozen snapshot of the public web learned during training, and whatever the assistant retrieves live when it searches. Switch browsing on and the answer can change completely. Ask the same question an hour later and the wording shifts again. Ask "is it legit" instead of "reviews" and a different set of pages gets pulled in. A single screenshot, good or bad, is a coin flip. What you want is the pattern across prompts, modes and days, because the pattern is what your buyers, investors and recruiters statistically meet.

How to check what AI says about your company, step by step

Step 1: Build the prompt set

Write the questions people actually ask about you, in the words they use. The same set we use in ChatGPT reputation management work:

your company name alone, and your name with your city or industry;
"is it legit", "is it safe", "is it a scam";
"reviews", "complaints", "lawsuit", "controversy";
comparisons with your two or three closest competitors;
the awkward questions your sales and hiring teams already hear.

Keep the set to ten or fifteen prompts and use exactly the same wording every time you run it, so later checks compare like with like.

Step 2: Run the set in ChatGPT, with and without browsing

Run every prompt twice: once in the plain chat mode, which draws on model memory, and once with search or browsing switched on, which pulls in live pages. Record both answers in full. In browsing mode, open and note every citation ChatGPT shows, because those links are the raw material for any fix. Where the two modes disagree, the memory answer tells you what the model believes and the browsing answer tells you what the web is currently saying.

Step 3: Run it in Google AI Overviews and AI Mode

Type each prompt into Google search in a private window with your market's location set. For many brand queries an AI Overview appears above the results; the AI Mode tab gives a longer answer to the same question. Record the summary text and every linked source. AI Overviews are built from what already ranks for the query, so they double as a map of your page-one results; our post on why AI Overviews show negative reviews explains the mechanics.

Step 4: Run it in Gemini

Ask each prompt in Gemini and then ask it which sources it used, or use its response-check feature where available, so you capture both the answer and what grounded it. Gemini leans on Google Search for current facts and on model memory for the rest, which is why its answer can differ from the AI Overview for the same query. Our Gemini reputation management page covers what drives its answers.

Step 5: Run it in Perplexity

Perplexity footnotes every page it reads, which makes it the fastest way to see which URLs are feeding the negative sentences across all the assistants. Run the set, record the answer, and copy out every numbered citation with the sentence it supports. This citation trail is how we build the source map in an audit; our Perplexity reputation management page explains how it is used.

Step 6: Run it in Microsoft Copilot

Copilot retrieves from Bing's index, and in our experience ChatGPT's browsing leans on Bing too, so a page that ranks on Bing but not on Google can still drive two assistants. Run the set, record the answer and the links it shows.

Step 7: Record everything in one sheet

Keep one row per prompt per assistant per mode: the date, the verdict (clean, drifting or damaging), the damaging sentence copied verbatim, and the cited URLs. Take a dated screenshot of each answer. This sheet is your baseline. Without it you cannot tell next month whether anything changed.

Step 8: Repeat monthly, with the same prompts and modes

Re-run the whole set once a month, or sooner after any press, dispute or review spike, and compare each cell against the baseline. A new sentence or a new citation is your early warning that a fresh source has appeared, and it is far cheaper to deal with a source before the assistants have settled on it. This is what AI brand monitoring does as a standing service, on a schedule and across many more prompt variants than most teams run by hand.

Which surface updates how fast?

Once a source page changes, is removed or gets outranked, the assistants do not all catch up at the same speed. Knowing which layer the damage lives in tells you how long to wait before re-checking.

Surface	What it reads	Updates after a source changes
ChatGPT with browsing	Live search results, leaning on Bing's index	Days to weeks, once the changed page is re-crawled
ChatGPT without browsing	Model memory, a frozen training snapshot	Next model update, on OpenAI's schedule
Google AI Overviews and AI Mode	What already ranks in Google for the query	Days to weeks, as Google re-crawls and re-ranks
Gemini	Google Search grounding plus model memory	Days to weeks for grounded claims; next model update for memory
Perplexity	Live web retrieval with footnoted sources	Days to weeks, once the source page is re-indexed
Microsoft Copilot	Bing's index plus model memory	Days to weeks for retrieved claims; next model update for memory

Live layers follow the web within days to weeks of a source changing. Memory layers wait for the vendor's next model release, which is why a fix that only touches one layer can look like it relapsed.

What to do with what you find

If every cell comes back clean, you have a baseline and nothing to fix. Keep the sheet, re-run it monthly, and move on. If an answer is drifting or damaging, the next job is not to argue with the assistant but to find the page behind the sentence. With browsing on, ChatGPT and Perplexity usually cite it; without browsing, search the exact claim on Google and Bing and you will normally find the source. Our post on what sources ChatGPT trusts explains which page types tend to sit behind a bad answer, and our guide to changing what ChatGPT says about your brand takes it from source map to removal, outranking and machine-readable facts.

Should you run the check yourself or have it done?

Run it yourself first. The self-check above costs an hour and tells you whether there is anything worth paying anyone for, which is exactly the question a vendor has an incentive to answer for you. Have it done when the name is contested, when there are several markets or languages, when a raise, exit or hiring push is coming, or when the first run shows damage and you need every sentence traced to its source with an honest read on what can be removed. Roughly one audit in five ends with "there is nothing here worth fixing", and we say so.

If you would rather we ran the full matrix and traced the sources for you, call +91 8700 450 757info@onlinereputationmanage.comemail info@onlinereputationmanage.com or contact the team. The report is private, and you keep it either way.

Frequently asked questions about checking what AI says about you

How do I check what ChatGPT says about my company?

Ask it the questions a buyer would ask, not just your name: your name with your city or industry, "is it legit", "is it a scam", "reviews", "complaints", "lawsuit", and comparisons with close competitors. Run each prompt once in plain chat mode and once with browsing switched on, record the full answer and every citation, and repeat the set a few times over several days. The pattern across those runs is what ChatGPT actually says about you; any single answer is a sample.

Why do I get a different answer each time I ask?

Because the answer is generated fresh every time from two layers, model memory and live retrieval, and the retrieval step pulls a slightly different set of pages on each run. Wording changes between runs are normal. What you are looking for is the claims and sources that repeat, because those are the ones most people asking about you will meet.

How often should I check what AI says about my company?

Monthly is the practical minimum for most companies, using the same prompts and modes each time so you can compare against your baseline. Check sooner after any press coverage, dispute, review spike or product launch, since a new source can change an answer within days once it is crawled. Companies in contested markets or preparing for a raise or exit usually move to continuous monitoring.

What if an assistant gets a fact wrong but it is not negative?

Treat it as a finding. A wrong founding year, a merged identity with a similarly named company or an outdated address is how a model drifts toward someone else's problems. Fix it at the source: make your website, LinkedIn, registry entries, directories and any knowledge panel state the same facts in plain sentences, and add structured data so the facts are unambiguous.

Can I just ask the assistant to correct itself?

No. Correcting an assistant inside a chat changes that conversation only; the next person who asks gets the same answer. The only durable fix is to change the evidence the assistant reads: remove the page that seeded the claim where it breaks a platform's rules or the law, outrank it where it cannot be removed, and publish clear, machine-readable facts the assistant can quote instead.