The Actual Problem: You’re Being Asked to Fix Something You Can’t See Yet
If you’re a marketer at a B2B SaaS company right now, there’s a good chance someone above you has started asking a version of the same question: “Are we showing up when people ask ChatGPT for tools like ours?” It’s a fair question. It’s also one that most marketing teams have no real way to answer yet, and that gap between the question and the tooling is exactly where the stress comes from.
The instinct is to open whatever SEO tool you already pay for — Semrush, most likely — and look for an answer there. It usually isn’t one. AI-answer tracking exists on some of these platforms, but often only as a separate add-on to the plan most teams already have, which is easy to miss if nobody told you to look for it. Rank trackers were built to tell you where a page sits in a list of search results. An AI answer isn’t a list. It’s a generated sentence, assembled fresh each time from whatever the model retrieved and however it phrased things on that particular run. Different mechanisms. A tool built for the first one won’t show you the second, no matter how good it is at what it does.
That’s not a reason to panic. It’s a reason to stop looking for a dashboard and start with something much simpler: a baseline you build yourself.
Why Your SEO Tools Don’t Show You This
Worth being precise about the gap, because it explains everything that follows. A search engine ranks existing pages against a query and shows you a list. An AI answer engine does something else entirely: it takes a question, decides what the question actually means, retrieves some mix of sources, and generates a new sentence that may or may not name any brand at all. There’s no fixed “position” to track. Ask the same question twice and you can get two different results.
A handful of SEO platforms have started bolting on AI-answer tracking as an add-on, and dedicated AI-visibility tools exist too. Some are genuinely useful once you know what you’re looking for. None of them replace the first step, which costs nothing and takes an afternoon.
Start With a Baseline, Not a Fix
Here’s the version of this that shows up, independently, from a striking number of people who actually do this work for a living: before you change a single word on your website, find out what the AI engines are already saying about your category.
The method is almost boringly simple:
Pick 15 to 30 questions a real buyer would type before they know your company exists. Not your brand name — the plain category question. Think “best [category] for [use case],” “[category] alternatives,” “what should I use for [problem].” Run each one through ChatGPT, Perplexity, Gemini, and Claude. Run each question more than once, because answers move between runs. The same prompt can name a competitor on one pass and not the next. A single check tells you almost nothing. Then log the results in a plain spreadsheet.
The part people skip, and the part that actually matters: don’t collapse the result into one yes-or-no column. Track at least two things separately.
Mentioned, cited, and recommended are three different outcomes.
A model can pull a page from your site as a source and still recommend a competitor by name in the actual sentence a buyer reads. Your analytics will only ever show you the click. It can’t tell you whether your brand’s name appeared in the answer at all. Conflating these three outcomes is probably the single most common mistake in how teams try to track this — and an easy one to fix once you know to keep them apart.
Testing your own brand name tells you almost nothing.
Of course a model can describe your company when you ask it to directly — that’s the easy case. The real test is the plain category question with no brand name in it at all. A company can look strong on the first test and be nearly invisible on the second, on the same day.
What the Baseline Actually Tells You
Once you have even a rough version of that spreadsheet, something useful happens. The vague anxiety turns into a specific list. “We’re named in 2 of 20 buyer questions, and here’s exactly who gets named instead” is a completely different conversation with your leadership than “I don’t know if we’re visible in AI.”
Look specifically at what gets cited when a competitor appears and you don’t. In practice, that tends to split into a short list of categories: third-party comparison and review content, community discussions, and your own site’s content (usually the smallest of the three, which is itself informative). That list becomes your actual to-do list — not a generic checklist copied from a blog post, but the specific gaps your own questions surfaced.
Only Then: What to Actually Change
With a real baseline in hand, the standard technical and content advice finally makes sense. Not as a shot in the dark, but as a next step aimed at a known gap:
Structured data (FAQ schema, clear entity information) and a well-formed llms.txt file don’t manufacture citations on their own. What they do is make you easier to lift once something else — a comparison page, a review, a community thread — has already made you worth mentioning. Skipping them costs you nothing dramatic; having them in place removes a small, avoidable barrier.
Clear, direct on-page answers to the real questions your buyers ask matter more than keyword density ever did here — a model is more likely to lift a well-written paragraph that actually answers the question than a page written primarily to rank.
Off-site presence tends to move the needle more than on-site tweaks alone — genuine mentions on comparison sites, review platforms, and industry discussions. These models draw heavily on what other sources say about you, not only on what you say about yourself. Reddit specifically is a big enough channel on its own for some categories that we’ve covered it in a separate guide, linked below, rather than squeezing it in here.
A Few Traps Worth Knowing Before You Start
Two things are easy to get wrong even with a solid baseline process:
A single check proves very little, in either direction. If you check once and see no mention, that’s one data point, not a verdict — run it again before you write anything down as a finding.
Context can change the answer. Session details, like stated location, are a known variable in how these models personalize responses. If your buyers are concentrated in one region, testing from a different context than they’d actually use can quietly mislead you about what they’re seeing.
Neither of these means the exercise is pointless. It means treat any single run as a data point, not a conclusion, and build your baseline from a handful of runs rather than one lucky (or unlucky) answer.
How Ridure Approaches This for SaaS Clients
We run a version of this same measurement-first process before we touch a client’s content or technical setup, for the reason described above: change things without a baseline and you can’t tell later whether anything actually worked. Across our own client work, we’ve now tracked more than 200 real data points across ChatGPT, Perplexity, and Gemini specifically. That’s part of why measurement comes first in every engagement, not a generic checklist. A typical engagement runs about six months and starts at ₹1,00,000. We also tell clients upfront when a category is too new or too small to be worth optimizing yet, rather than selling a service into a market that isn’t there.
Frequently Asked Questions
Is this different from regular SEO?
Related but distinct. Traditional SEO ranks existing pages in a list; this is about whether a generated answer names you at all, which depends more on being mentioned favorably elsewhere than on any single page you control.
How many prompts should I actually test?
Somewhere in the 15-to-30 range is the most commonly recommended starting point among people who do this work regularly — enough to see a real pattern, not so many that the exercise becomes a project of its own before you’ve learned anything.
Do I need a paid tool to do this?
No, not to start. The baseline described here costs nothing but time, and it’s worth doing by hand at least once so you understand what a tool would actually be automating for you. A dedicated tracking tool starts to earn its cost once you’re running this weekly across multiple engines and it’s eating a meaningful chunk of your week.
What’s the fastest way to see if we have a real problem?
Run five category questions, no brand name, across two engines, three times each, today. If you’re named in most of them, the pressure you’re feeling may be ahead of the actual problem. If you’re named in none, you now have a concrete starting point instead of a vague mandate.
Does schema markup actually get us cited?
Not by itself. It’s a real, low-cost technical fix that removes a barrier to being lifted once you’re otherwise worth citing — treat it as supporting infrastructure, not the strategy.
Where to Go From Here
If your business is smaller or more consumer-facing than the SaaS context here, our guide on getting ChatGPT to recommend a small business covers that version of this problem directly. If off-site channels like Reddit are a fit for your category, our Reddit SEO strategy guide goes deep on that specific channel. Want a second set of eyes on your own baseline once you’ve run it? Talk to our GEO/LLM SEO team. We’ll tell you plainly what we see — including if the honest answer is “you’re in better shape than you think.”