You can't edit what AI says about your brand. Here's what you can do instead.

AI forms brand perceptions by pattern-matching signals absorbed during training or fetched at answer time from whatever's currently live on the web. The quality of those signals determines the accuracy of every answer an LLM gives about your company.

If it's off, the fix is a methodical audit of every source that feeds the machine - and a deliberate effort to make those sources say the right thing.

1. Understand what AI is doing with your brand

Before you try to fix anything, get the frame right. AI models form brand perceptions from two places: training data absorbed historically, and live retrieval at answer time. You cannot edit training data directly. What you can do is make sure every source that feeds both layers reflects accurate, consistent, current positioning.

Brands that get described accurately by AI have consistent, specific, up-to-date information published across enough credible sources that the model has no reason to guess. Your job is to remove the guesswork.

2. Nail your entity definition

When AI encounters incomplete information about a brand, it infers from whatever signals are available - and inference built from scattered fragments produces vague, inconsistent, sometimes wrong answers. Wikidata is the most direct fix here. A well-structured Wikidata entry gives AI models a canonical fact anchor: your category, your founding date, your website, your founders, your relationships to other entities. Wikipedia matters too, though not every brand qualifies for a full article. Google's Knowledge Panel is the visible output of this work - a robust panel signals that your entity definition is landing.

This is unsexy infrastructure work. A solid entity layer is what makes every other effort to shape AI outputs land with more precision.

3. Get your structured data right

Organisation schema on your own site is the minimum. It tells AI retrieval layers your official name, your category, your location, your URL, and how you relate to other entities via sameAs markup. Consistent NAP (name, address, phone) across every directory and listing site matters here too - consistency across sources gives AI a single coherent signal to work with.

SameAs markup is particularly underused. It connects your website to your Wikidata entry, your LinkedIn page, your Crunchbase profile, and your other authoritative presences - so every source points to the same entity rather than looking like separate, loosely connected fragments.

4. Build a consistent brand narrative across the web

This is where most brands leak. A Capterra profile from a few years back says something slightly different from your homepage. A G2 listing has a category tag you no longer fit. A listicle from 18 months ago describes a feature you've since replaced. An industry directory has your old tagline. AI pulls from all of it.

The audit here is manual and genuinely tedious - but it produces a cleaner, more consistent signal that compounds over time. Search your brand name in combination with every type of third-party source: review platforms (G2, Capterra, Trustpilot, Trustradius), directory listings (Crunchbase, AngelList, LinkedIn company page), analyst pages, comparison sites (Versus, AlternativeTo), industry listicles, partnership pages, guest posts, podcast show notes, and anywhere else your brand has ever been described in writing. Every instance of outdated or inaccurate positioning is an AI input you haven't corrected yet.

Update what you can control directly. For third-party listings, claim your profile, request an edit, or contact the site owner. For old listicles and blog posts, reach out and offer updated copy. Some will update it. Many won't. But the ones that do reduce the contradictory signal volume, and that compounds over time. Content audits identify which pieces are training AI with incorrect information.

5. Seed the content AI is most likely to cite

Retrieval-augmented AI surfaces specific content types more reliably than others: clear definitions of what your product is and does, comparison content that positions you accurately against alternatives, FAQ content that mirrors how buyers actually ask questions, and structured explainers that are easy to parse without reading the whole page.

Publishing this content on your own site is table stakes. Getting it published on the third-party sources AI already trusts is the bigger win. If a particular comparison page or industry resource keeps appearing when you query your category in ChatGPT or Perplexity, that page is feeding the model's answer. Getting accurate information about your brand onto that page matters more than publishing another blog post on your own domain. For a practical framework on getting mentioned by LLMs, the approach maps directly to this kind of source-level thinking.

6. Earn press that reinforces your positioning

Press coverage is one of the most credible inputs AI models use to form brand perceptions. Earned media is a direct signal to AI models - coverage from known publications carries authority weight that shapes how those models describe your brand.

That means earned coverage needs to be deliberate about positioning. A profile in a relevant trade publication that accurately describes your category, your audience, your differentiation, and your core use cases shapes AI outputs in a way that a passing mention simply cannot. Brief journalists and editors the same way you'd brief your own content - specific, accurate, consistent with how you want to be positioned everywhere else. Agencies navigating AI chaos are already shifting focus from backlinks to earned media for exactly this reason.

7. Add an llms.txt file

An llms.txt file sits at your root domain and gives AI agents a structured, plain-language summary of who you are, what you do, and how your site is organised. It's a direct communication channel to the models crawling your site - less interpreted, more explicit than standard web pages. It won't override training data, but for retrieval-based AI answers it's a clear signal. It takes an hour to set up and delivers a meaningful signal improvement for the effort. Building an agent-friendly website covers this and the broader infrastructure work in detail.

8. Audit what AI is currently saying - and keep checking

Run your brand through ChatGPT, Perplexity, Gemini, and Claude with the same queries your buyers would use. Ask which tools they'd recommend for your category, then ask each one to describe what you do and write down exactly what comes back.

When the answer is wrong, trace it. If a specific source is feeding the wrong information, that source is your next fix. If the answer is vague or incomplete, the entity definition work hasn't landed yet. This is not a one-time exercise - AI model outputs shift as training data updates and retrieval sources change. Building a monthly habit of checking what the models say about you is how you catch drift before it costs you a recommendation. Building your own LLM tracking tool is more accessible than most people assume, and it's the most direct way to systematise this habit.

Frequently asked questions

What is an AI audit and why does your brand need one?

An AI audit is the process of checking how large language models currently describe your brand - what they say about your category, your product, your positioning, and your differentiation. You need one because AI is increasingly the first place buyers ask for category recommendations, and a wrong or outdated description can cost you opportunities before you're ever in the conversation.

Why is it important to audit the information AI has about your brand?

AI doesn't distinguish between current and outdated information - it works from whatever signals it has absorbed or can retrieve. If the most prominent sources describing your brand are old, inaccurate, or inconsistent, that's what the model will repeat. Auditing lets you identify which sources are feeding wrong information and prioritise fixing them.

How does controlling AI outputs differ from traditional SEO?

Controlling AI outputs means managing a distributed set of signals that collectively determine how your brand gets described in AI-generated answers - structured data, entity definitions, third-party source accuracy, and content consistency are the levers.

How often should I audit what AI says about my brand?

Check monthly. AI model outputs shift as training data refreshes and retrieval sources change, so a quarterly check is too infrequent to catch drift early. Run a consistent set of test queries each time - the same category recommendation prompts, the same competitor comparison questions - so you can track changes over time rather than getting a snapshot with no context.

What information does an AI audit analyse?

A thorough audit covers what AI says about your product category, your use cases, your audience fit, how you compare to specific competitors, and whether your core positioning is reflected accurately. It also involves tracing which sources are feeding those outputs - review platforms, listing sites, editorial coverage, your own structured data - so you know where to focus the fix rather than just knowing something is wrong.

What are the most important sources to update to control AI outputs?

Review and comparison platforms (G2, Capterra, Trustpilot), directory listings (Crunchbase, LinkedIn), industry listicles, analyst pages, and your own structured data and entity definitions are the highest-priority sources. These are the inputs AI retrieval layers pull most reliably, and outdated information on any of them feeds directly into inaccurate AI answers.