How to fix the way AI describes your brand

A founder emailed me last month with a screenshot. He had asked ChatGPT about his own company and the answer described it as "a smaller alternative to [competitor], popular with early stage teams on a budget".

Every word was technically defensible. Every word was also killing his enterprise deals.

That is the problem with how AI describes brands. It is rarely a clean lie you can point at and refute. It is a framing. A set of adjectives. A comparison you never agreed to. And it repeats, in slightly different words, across millions of conversations you will never see.

I build LLM Pulse, an AI visibility platform, so I spend most of my week looking at exactly this: the sentences models produce about companies. I want to be straight with you about what you can change, what you cannot, and how to tell the difference.

The words matter more than the ranking

Most people who worry about AI visibility ask one question: am I mentioned or not?

That is the easy question. The harder one, and the one that actually moves revenue, is: when I am mentioned, what exactly does the model say?

A brand can be mentioned constantly and described in a way that quietly disqualifies it. "Reliable but dated." "Cheap option." "Good for beginners." "Had some outages a while back." None of those are visibility problems. They are reputation problems, and they live inside the wording.

The uncomfortable part is that a single bad adjective, repeated by a model that hundreds of thousands of people trust, does more damage than a page ranking tenth on Google ever did. Nobody reads the tenth result. Everybody reads the one paragraph ChatGPT hands them.

So the job is not just to appear. It is to appear described correctly.

Why AI gets your brand wrong

Before you can fix the wording, you need to understand where it comes from. In my experience it is almost always one of four sources, and each has a different fix.

Stale training data. The model learned about you from a snapshot of the web that might be a year or two old. If you repositioned, raised a round, dropped a product or changed your pricing, the model may still be describing the old you. This is the most common cause of "that was true in 2024" answers.

Third-party sources you do not control. Review sites, Reddit threads, listicles, comparison pages, an old TechCrunch piece, a competitor's "alternatives to" article. Models lean heavily on these because they read as independent. If a widely cited roundup calls you "the budget pick", the model will too, no matter what your homepage says.

Your own outdated or vague content. Sometimes the model is quoting you accurately and the problem is you. If your site still leads with messaging from two pivots ago, or buries what you actually do under vague slogans, the model has nothing better to work with.

Genuine hallucination. Less common than people think, but real. The model invents a feature you do not have, attaches a controversy that belongs to another company, or merges you with a similarly named brand. This one is maddening because there is no source to correct.

The reason detection matters is that the fix for stale data (get fresh, crawlable content indexed) is completely different from the fix for a bad third-party source (go influence that source) which is different again from a hallucination (build enough clear signal that the model stops guessing). Treat them the same and you waste months.

Step one: find out what is actually being said

You cannot fix wording you have not read. And you cannot read it by asking ChatGPT once, because the answer changes by model, by phrasing, by country, and by day.

Ask "is X a good tool" versus "what are the downsides of X" versus "X vs Y" and you get three different portraits. Ask Gemini instead of ChatGPT and it is different again. Ask from Spain instead of the US and the sources shift.

So the manual version of this audit looks like: pick 20 to 30 prompts a real buyer would type, run each across the models that matter to you, in the markets that matter, and copy the exact sentences the models use about you. Do it again next week to see if anything moved. It is tedious and it does not scale, but it is real, and doing it by hand once will teach you more than any dashboard.

The reason tools exist is that nobody keeps doing that by hand. This is the part LLM Pulse was built for, and I will be blunt that I am biased. What our Reputation and sentiment features do is pull the exact words the models use to describe you, not a happy-or-sad score, and flag the ones that are negative, wrong or off-brand. You see the adjective, the sentence it lives in, and which model and prompt produced it. Then you watch that wording over time, so when an answer shifts from "reliable" to "reportedly had issues" you find out in days, not when a deal dies.

We track five core models on every plan (ChatGPT, Perplexity, Gemini, Google AI Mode and Google AI Overviews) and you can add Claude, Copilot, Grok, DeepSeek and Meta AI. That range matters here, because a brand that reads fine in ChatGPT can be described badly in Gemini, and you would never know if you only checked one.

I will come back to alternatives, because depending on your size a different tool may fit you better.

Step two: fix the source, not the symptom

Here is the mistake I see constantly. A brand finds a bad description in ChatGPT and tries to fix ChatGPT. You cannot. There is no dashboard where you edit what the model says.

What you can do is change what the model reads. The description is downstream of sources. Go upstream.

If the problem is stale data, your job is to publish clear, current, crawlable content that states plainly what you are now. Not clever copy. Plain sentences a model can lift: what you do, who you serve, what changed, what you are not. Make sure your own pages are accessible to the AI crawlers (GPTBot, ClaudeBot, PerplexityBot and the rest), because if you are blocking them you are handing the model nothing but the old third-party version of you.

If the problem is a third-party source, identify the specific pages the models cite and go work on those. A factual correction request to a review site. A better, more current comparison page of your own that ranks. Outreach to the author of the listicle that froze you as "the cheap one". This is slow relationship work, but it is the highest-leverage thing you can do, because models trust these sources more than they trust you.

If the problem is a hallucination, the counterintuitive fix is volume and clarity of correct signal. Models invent when there is a vacuum. Publish enough unambiguous, consistent information about the invented topic that the correct version outweighs the guess. Structured data, an FAQ that answers the exact wrong claim, consistent phrasing across your properties.

None of this is instant. Models re-crawl and re-train on their own schedule. But I have watched descriptions shift over weeks once the underlying sources changed, and that is the honest timeline: weeks, sometimes a couple of months, not days.

Step three: monitor the wording, not just the mention

Fixing it once is not the win. The win is knowing when it drifts back.

Model updates reshuffle everything. A new training run, a new default model, a change in which sources get cited, and your carefully corrected description can regress overnight. A competitor publishes an aggressive comparison page and suddenly their framing of you leaks into the answers.

So the wording needs a baseline and an alarm. Capture how you are described today, in specific quotable sentences, and get told when those sentences change. That is a monitoring job, not a one-off audit, and it is the single most underrated part of GEO.

Connecting your GA4 or Plausible closes the loop, because then you are not just watching words, you are watching whether ChatGPT, Perplexity and Gemini actually send visits and conversions while the wording improves. That link between "the description got better" and "revenue moved" is the part most people never measure.

Which tool, honestly

I build one of these, so treat what follows as informed but interested. Here is where I would genuinely send you elsewhere.

If you are a large brand or an enterprise that needs the deepest data set, SOC 2, SSO, on-prem and prompt volume analysis, look hard at Profound. It is the heavyweight and it earns the label. It is demo-led with no public pricing, and for a Fortune 500 that is fine.

If you are an agency or a team that wants strong regional and multi-language tracking with per-country competitor comparison, Peec AI is genuinely good, from around $89/month, with unlimited seats. Note that it includes three models and extra ones are paid add-ons per model.

If you want the cheapest honest entry point and screenshots of real AI outputs, Otterly.ai starts at $29/month and ships an MCP server and a public API.

And if you already pay for Ahrefs or Semrush, their Brand Radar and AI Toolkit features are shallower than the specialists but they are already in your subscription. For a first look, that is a reasonable place to start.

Where I would put LLM Pulse in your shortlist: when the specific thing you care about is the wording and its reputation over time, at fair public pricing, without an enterprise call. Plans start at 49 €/month and every plan has unlimited seats, no per-seat fee, plus a 14-day trial. The Reputation and sentiment work that this whole article is about sits on the paid tiers, alongside AI traffic analytics and a hosted MCP server that pulls your own data straight into Claude or ChatGPT.

Here is the current pricing, so you can compare like for like.

Prices change, so check the current numbers before you buy. And if the honest answer for your situation is Profound or Peec or Otterly, take it. A tool you will actually run beats the one with the best feature list.

The part nobody wants to hear

You do not own how AI describes you. You influence it. The models sit between you and everyone Googling your name, and they will paraphrase you using whatever sources they trust, updated on a schedule you do not set.

That is not a reason to give up. It is a reason to treat your AI description as a living thing: read it, trace the words to their source, fix the source, and watch the wording so you catch the day it slips.

Because right now, somewhere, a model is telling a buyer what your company is in one confident paragraph. The only question is whether you have read it.