How to Check Whether ChatGPT Recommends Your SaaS or Business

Why is a quick check in your own ChatGPT account misleading?

The fastest check, typing "what are the best [category] tools?" into your usual account, is also the least reliable:

• Memory and history. If you have discussed your own company with ChatGPT, it may bring that context into the answer. You can end up testing what it remembers about you, not what it tells strangers.
• One answer. Recommendations vary between runs. A single answer that includes you, or leaves you out, can easily be chance.
• Wrong question. "Best [category]" is broader than what buyers ask. Real questions carry conditions, and the shortlist changes with each one.
• Search or no search. ChatGPT sometimes searches the web and sometimes answers from what it learned in training, and the two can give different recommendations.

Use an unpersonalized Temporary Chat, which does not use or save memories, or a logged-out session, for every test run. Since August 2026 a Temporary Chat can also be set to personalized, which uses your memories, so check the setting.

How do you set up a clean ChatGPT recommendation check?

1. Write 15–30 buyer questions in the words buyers use. Get them from sales calls, demo requests, support tickets and reviews. See how to find the questions people ask ChatGPT about your business.
2. Leave your name out of most questions. Keep a few branded questions ("is [you] good for [need]?", "[you] vs [rival]") in a separate group.
3. Add conditions to most questions: "for a 10-person agency", "under $50 per user", "that integrates with HubSpot", "in Manchester".
4. Run each question five times in fresh temporary or logged-out chats.
5. Record for each answer: whether you are named, your position, the words used to describe you, the other businesses named, and any cited sources.
6. Summarise per question: "recommended in 3 of 5, first in 1".

Repeat monthly with the same questions to see the trend. For logging across several engines at once, see how to manually track AI visibility.

What should you record for each answer?

"Mentioned" is not the same as "recommended". Record the difference:

Caveats and positioning deserve attention. A caveat that is wrong, such as a missing feature you actually have, is a fixable accuracy problem and often costs more than not being mentioned.

How do you check SaaS recommendations specifically?

Software buyers ask in predictable patterns. Build the question set around them:

• Use case: "best tool for [job] for [team type]".
• Company size: "for startups", "for a 200-person company", "enterprise".
• Integration: "that works with Salesforce", "with a Slack integration".
• Price: "free", "under $20 per user", "without per-seat pricing".
• Alternatives: "alternatives to [market leader]", "cheaper than [rival]".
• Comparisons: "[rival A] vs [rival B]" questions where you should be in the conversation.

The alternatives questions matter most for smaller SaaS products. Buyers who already know the market leader often ask what else exists, and that is where challengers are most often recommended. If you never appear there, check whether third-party comparison and alternatives pages include you. More in how to get a B2B software product recommended by AI assistants.

What is a credible way to measure AI visibility for product management software?

Take a product management tool as a worked example. A credible measurement has four properties:

1. Segmented questions. Split the set by the buyer segments that matter, for example startups, mid-market product teams, and teams working in a particular method or with a particular issue tracker. Report each segment separately, because a tool can dominate one and be missing from another.
2. Named rivals tracked alongside. Record how often each main competitor is recommended on the same questions, so your figure has context.
3. Enough runs. Five runs per question over 20–30 questions gives 100–150 answers per month, enough to see large differences. Small month-to-month changes are usually noise.
4. Visible evidence. Every number in the report can be traced back to the actual answers.

A report built this way might say: "Recommended in 38 of 125 answers overall; 22 of 50 for startup questions, 4 of 40 for enterprise questions; the market leader was recommended in 97 of 125." That tells the team where to focus: in this example, the enterprise segment.

How do you check recommendations for service businesses?

The method is the same; the conditions differ. Two examples:

How can a business broker check whether ChatGPT recommends them for restaurant sales?

Ask the questions a restaurant owner would ask: "How do I sell my restaurant in [city]?", "business brokers who specialise in restaurants in [region]", "what does a business broker charge to sell a restaurant?". Note whether ChatGPT recommends using a broker at all, which brokers it names, and what sources it cites. Brokerage recommendations often lean on industry directories, listing marketplaces and professional association member lists, so check your presence and specialisation there. If ChatGPT names only national marketplaces, a clear page about restaurant sales in your area, with the process, fees and past sale types, gives it something specific to cite.

How can a media buyer tell whether ChatGPT recommends their agency for paid search?

Ask with the conditions a client brings: "PPC agency for ecommerce brands spending $20k a month", "Google Ads agency for B2B SaaS in London", "paid search agency that works with small budgets". Record which agencies are named, whether yours appears, and whether the description matches your actual specialisms and minimum budget. Agency recommendations often draw on agency directories, partner listings and "top agencies" articles; if competitors appear and you do not, those pages are the first place to look.

What should you do with the results?

• Recommended often: protect it. Keep facts, prices and pages current, and check that descriptions stay accurate.
• Mentioned with wrong caveats: fix the source of the wrong fact; see why ChatGPT recommends the wrong website for your business for related errors.
• Missing on certain segments: write pages for those use cases and get onto the sources cited for them.
• Missing everywhere: check that engines can read your site and that independent sites mention you at all.

Whether these recommendations turn into customers is a separate question, covered in whether businesses get leads from ChatGPT recommendations.

What do people ask about ChatGPT recommendation checks?

Can I pay ChatGPT to recommend my product?

No. ChatGPT's ads are labelled and kept apart from the answer, and OpenAI says they do not influence it. Recommendations come from the model and the pages it retrieves; see whether a vendor can pay or train ChatGPT to recommend a product.

Should I test with search turned on or off?

Both, if you can. Answers with search show which pages shape the recommendation; answers without it show what the model learned in training.

How often should I run the check?

Monthly for most businesses, and after launches, pricing changes or a new competitor's arrival.

Does ChatGPT give the same recommendations in different countries?

Not always. Location affects search results and local recommendations, so test from each market you sell in.