Does AI misrepresent your brand?

The way buyers find and judge brands has changed more in the last few years than in the previous twenty.

Researching a business meant a search engine and a page of blue links.

A buyer typed a query, weighed up the results, clicked through to a few sites, and made up their own mind.

The name of the game was to rank, and to make sure that when someone landed on your pages, that your brand and story won out.

Today, a growing share of B2B buyers skip that process entirely.

They open ChatGPT, Claude or Google's AI Overviews and simply ask.

And the AI doesn't return ten links to compare. It returns one answer: a single, confident summary of who you are, what you sell, what you charge, and whether you deserve a place on the shortlist.

That answer has become one of the most important touch-points in the customer journey. It’s also the one piece brands have no direct control over, and no clear way of consistently being able to measure accuracy and performance.

Until now.

The old model: update your website, job done

For twenty years, keeping your brand accurate online was a manageable task. When something changed, you updated your website. Search engines crawled the new version, the old cache faded, and within a few weeks the record was corrected. Your site was the single source of truth, and everyone, including Google, deferred to it.

That model worked because search returned links. A buyer clicked through to your page and read the current version in your words. You controlled the destination.

The new model: AI reads the whole internet, then answers for you

Large language models don't work that way. They don't send a buyer to your homepage to read the latest. They absorb an enormous slice of the internet, form a view of your brand from everything they have seen, and then hand the buyer a single, confident answer.

That answer is stitched together from many sources. Your website is one of them. So is a review site's summary from 2023, a directory listing nobody has touched in years, an old press release, a competitor's comparison page, a forum thread, a partner's outdated description of what you do. The model weighs all of it and speaks on your behalf.

Which means your brand is no longer described only by the pages you control. It is described by the whole internet's memory of you, much of which is out of date the moment you change anything.

Why the wrong facts usually aren't on your site

When a client comes to Tilio convinced that AI is misrepresenting them, the instinct is always the same: something must be wrong on the website. Occasionally it is. Far more often, it isn't.

The inaccurate claim is coming from somewhere else. An old pricing figure lives on a third-party review platform. A retired product is still listed in an industry directory. A positioning line you dropped two rebrands ago survives in a guest article, a podcast show-notes page, or a roundup post someone published and forgot about. The model has read those pages, treated them as fact, and repeated them.

This is the trap. You can rewrite your homepage a hundred times and the AI answer won't move, because the source of the error was never your homepage. You updated your website. You did not update the internet's copy of you.

The internet is a big place

That is the real shift, and it is a significant one. Keeping your brand accurate used to mean maintaining one website. Keeping your brand accurate in AI search means maintaining your presence across every source an AI engine might learn from. The internet is a big place, and no in-house team has the time or the reach to manage all of it by hand.

So the job splits into two parts that most brands are not yet set up to handle.

The first is knowing what AI actually says about you, and why. Not a vague sense that "the AI gets us wrong," but a precise picture: which specific claims are inaccurate, and which specific sources are feeding those claims into the models. Without that, you are guessing.

The second is doing something about the sources themselves. If the problem is a third-party page, the answer does not sit on your side of the fence at all. It sits with whoever published that page. Correcting it means reaching the right people, showing them what has changed, and updating the record at source, so the next time a model learns from that page, it learns the truth.

Correcting the record, at source

This is the work Tilio does for clients, and it runs in a deliberate order.

Imagine you changed your pricing six months ago. You repositioned last year. You retired a product line and launched two new ones, and your website reflects all of it.

Ask an AI engine about your brand, though, and you may hear a very different story: the old pricing, the old positioning, a feature you no longer offer, or a claim you never made.

To the buyer reading it, that answer is your brand. And it’s wrong.

Most teams assume that if the facts are correct on their own site, they are covered. In AI search, that assumption no longer holds.

Tilio begins by comparing what the AI engines say about your brand against your own source of truth: your current pricing, positioning, products, claims and brand guidelines.

That surfaces the gap between how you describe yourself and how the models describe you, claim by claim.

From there, Tilio traces each inaccuracy back to its origin. Every wrong answer has a source, and usually a small number of pages are doing most of the damage. Identifying them turns a vague, sprawling problem into a defined, addressable list.

Then the work moves off your website and out onto the wider web. Tilio identifies the publishers and platforms behind the outdated mentions, prepares corrected copy that matches your current brand guidelines, and works with those sources to bring the record up to date.

The aim is straightforward: wherever an AI engine goes looking for information about you, it finds the current version, not a fossil.

Because the internet keeps moving and the models keep re-reading it, this is not a one-off exercise. New mentions appear, old ones resurface, and your own messaging keeps evolving.

Accuracy in AI search is something you maintain, the same way you maintain the rest of your marketing, and Tilio runs it as an ongoing programme rather than a single project.

Why this matters now

Business buyers increasingly start their research inside an AI tool rather than a search bar. At Tilio, 30% of our inbound inquiries are via AI Search and that number will increase dramatically over the coming months and years. By the time they reach your sales team, they have already formed an impression, and a large part of that impression came from an answer you did not write and may never have seen.

If that answer is out of date, you are losing deals to a version of your brand that no longer exists. Showing up accurately is no longer a nice-to-have. It is table stakes for earning a place on the shortlist at the exact moment a buyer is deciding whether you belong there.

The good news is that this is entirely resolvable. It takes the right diagnosis and disciplined work across the web, but the outcome is a brand that AI describes the way you actually describe yourself.

Where to start

If you are not yet sure how ready your brand is to show up in AI search, Tilio's free AI checker is a fast first step. It reviews your site's technical readiness and runs a preliminary analysis of your content, then gives you an initial AI visibility score to work from.

That score tells you where you stand today. Understanding exactly what AI is saying about your brand, which sources are shaping those answers, and what it would take to bring the record back in line with your source of truth is the next conversation. Book a call and Tilio will walk you through it.

Frequently asked questions

Why does AI get facts about my brand wrong?

AI engines get brand facts wrong because they answer from the whole internet, not just your website. Large language models learn from third-party sources such as review sites, directories, articles and old press releases, many of which still hold outdated pricing, retired products or positioning you have since changed. The model repeats that information as fact, even when your own site is completely up to date.

Can I correct AI's answers about my brand by updating my website?

Usually not. When an AI engine repeats an outdated claim, the source is often a third-party page rather than your own site, so updating your website leaves the original error untouched. Correcting the answer means identifying which external sources are feeding the inaccuracy and updating the record there, so the models learn the current version the next time they read it.

How do you correct inaccurate information about a brand in AI search?

Tilio corrects inaccurate AI answers in three stages. First, we compare what the AI engines say about your brand against your own source of truth to pinpoint every inaccurate claim. Next, we trace each claim back to the specific sources feeding it. Then we work with those publishers to update the record at source, so AI engines describe your brand accurately going forward.

Is keeping a brand accurate in AI search the same as SEO?

No. Traditional SEO aims to rank your own pages in a list of results, where the reader clicks through and reads your words. Accuracy in AI search is about the single answer an engine assembles from many sources across the web, most of which you do not own. It focuses on what those sources say about you, not simply where your site ranks.

How long does it take to correct my brand's information across AI engines?

It varies. Correcting the record involves updating third-party sources and waiting for AI engines to re-read those pages, so changes tend to appear over weeks rather than instantly. Because the web keeps changing and the models keep re-learning, accuracy in AI search is best maintained as an ongoing programme rather than a one-off task.