Can You Influence What an AI Model Knows About Your Company?

Yes, but not directly. You cannot edit, delete or overwrite what a model learned during training, and no brand can submit a change that instantly rewrites an AI model's built-in knowledge. What you can do is influence the two layers around that frozen knowledge: the sources an AI assistant retrieves when it answers today, and the public record that future models will learn from.

In practice, that means your influence works on three timelines. Retrieval can change within days or weeks once better pages are published and crawlable. Training influence shows up only when a provider releases a new model, often months later. The model's existing memory stays as it is until then, so the job is to make sure fresher, clearer evidence outweighs it whenever the assistant looks something up. This guide breaks down which levers exist, which ones people overestimate, and how to use them in a sensible order.

What does "what an AI model already knows" actually mean?

An AI model's existing knowledge is the information encoded in its parameters during training, before the model was released. It is not a database of facts about your company that someone can look up and correct. It is a statistical pattern learned from a very large body of text, which is why a model can describe your company roughly right in one conversation and slightly wrong in the next.

It helps to separate three things people often call "what the AI knows":

Layer	What it is	Can a brand change it?	How fast
Model knowledge (parametric)	Patterns learned from training data before release	Not directly. Only future models change	Months, at the next training run
Retrieved content	Pages the assistant fetches or searches at answer time	Yes, by improving and exposing the sources it retrieves	Days to weeks, depending on crawling
Personalization	Per-user memory and chat history	No. It belongs to each user	Not a brand lever

The distinction matters because most complaints like "ChatGPT gets our pricing wrong" blend all three. If you want a deeper treatment of the first two layers, our guide to how retrieval-augmented generation affects brand visibility explains the retrieval side in detail. This article focuses on the practical question of control.

Can you directly edit or correct a trained model?

No. Once a model is trained, its knowledge about your company is fixed for that version. Providers retrain and release new models on their own schedules, and no public process lets a company file an edit to a released model's understanding of its brand.

Two mechanisms often get mistaken for direct correction:

User feedback. Thumbs-down ratings and written feedback go to the provider as signals. They are useful for flagging a clearly wrong answer, but nothing in the providers' public documentation presents them as a way for a brand to correct a model. Treat them as a report, not a fix.

Memory features. ChatGPT's memory lets the assistant remember preferences and details from a user's own chats. It is tied to that user's account. Telling ChatGPT "our company now offers a free tier" in your own session may change what it tells you later, but it does not change what it tells your prospects.

So the direct route is closed. The indirect routes are where the real leverage sits.

Lever 1: Influence what AI assistants retrieve right now

Retrieval is the fastest and most controllable lever. When an assistant searches the web before answering, the retrieved pages can override or refine what the model would have said from memory alone. Google, for example, says AI Overviews and AI Mode use a "query fan-out" technique, issuing multiple related searches across subtopics to build an answer.

To influence retrieval, focus on four things.

Make sure AI search crawlers can reach you

Search crawlers and training crawlers are separate, and blocking the wrong one can remove you from AI answers. OpenAI states that "sites that are opted out of OAI-SearchBot will not be shown in ChatGPT search answers," and notes that search changes can take about 24 hours to take effect after a robots.txt update. Anthropic runs Claude-SearchBot for search indexing, separate from its ClaudeBot training crawler. Perplexity uses PerplexityBot to surface and link sites. Our guide to AI crawler log analysis covers how to verify which bots actually visit.

Publish the facts in a form that can be quoted

If your current pricing, product scope, customer segment or headquarters location only exists inside a PDF, an image or a sales deck, a retrieval system has nothing clean to pull. Put the facts that AI tends to get wrong on indexable pages in plain sentences. For a hypothetical company, "Acme offers three plans starting at $49 per month, and a free tier for teams under five users" is easier to retrieve and cite than a pricing grid rendered by JavaScript.

Fix the third-party pages that get retrieved

A large share of what assistants retrieve about you comes from sites you do not own. Profound's analysis of 680 million citations found Wikipedia was ChatGPT's most cited source, while Reddit led for Perplexity and Google AI Overviews. If a review profile, directory listing or old comparison article still describes a product you retired, that page may be what the assistant quotes. Updating those profiles is often faster than any on-site change.

Keep key pages fresh

Freshness appears to matter more for AI citations than for classic rankings on some platforms. An Ahrefs study of about 17 million citations found AI-cited content was 25.7% fresher than organic search results on average, with ChatGPT showing the strongest preference. The study is correlational, but it supports a simple practice: when facts change, update the page and its visible date rather than publishing a new page next to the old one.

Lever 2: Shape what future models learn

Future models learn from the public web as it exists when their training data is collected. You cannot choose what goes into a training set, but you can shape the public record it samples.

Decide your training crawler policy on purpose

Training access is a business decision, not a default. OpenAI describes GPTBot as crawling "content that may be used in training our generative AI foundation models," and disallowing it signals that your content should not be used for training. Google offers Google-Extended to control whether content is used for Gemini training, separate from Googlebot. Anthropic's ClaudeBot handles training crawls for Claude.

Blocking these crawlers protects content you do not want used for training, but it also removes your own authoritative description of your company from that input. Many brands choose to allow training crawlers on public marketing and documentation pages while restricting proprietary content. That is a recommendation to weigh, not a rule, and legal or licensing considerations may point the other way for your business.

Remember that open datasets reach many models

Your influence is not limited to the crawlers you know about. A Mozilla Foundation study found that at least 64% of 47 text generation models published between 2019 and October 2023 used filtered versions of Common Crawl for pre-training, and that Common Crawl made up more than 80% of the tokens in GPT-3. The same report stresses that Common Crawl covers only a small fraction of the web. Common Crawl's crawler, CCBot, respects robots.txt, so blocking it has downstream effects on many models, not just one.

Build a consistent, widely repeated public record

Models learn patterns that appear often and consistently. If your website, review profiles, founder bios, press coverage and partner pages all describe your company the same way, that description is more likely to be learned. If they disagree, the model may learn a blend. Our guide to fixing inconsistent brand information walks through the cleanup process.

Earned mentions matter here too. Ahrefs' study of 75,000 brands found branded web mentions had a 0.664 correlation with visibility in Google AI Overviews, compared with 0.218 for backlinks. That is correlation, not proof of cause, but it points in the same direction as the training logic: the more the web talks about you accurately, the more material models have to work with.

Lever 3: Give the assistant better evidence than its memory

When a model's memory and retrieved content conflict, the answer depends on what the assistant finds and how confident the evidence looks. You improve your odds by making the correct version unambiguous.

The GEO research paper by Aggarwal et al., published at KDD 2024, found that adding citations, quotations and statistics produced the largest gains in source visibility within generated answers, up to 40% on one metric, while keyword stuffing was ineffective. The study used a controlled benchmark, so treat the numbers as directional. The practical lesson is that specific, sourced, dated statements give a system something concrete to prefer over a vague memory.

A useful pattern for pages that correct a common misconception, shown here with hypothetical examples:

State the current fact in the first sentence under a clear heading.
Add a date, for example "As of September 2026, Acme's Starter plan costs $49 per month."
Name the change if it is a frequent source of error, for example "Acme retired its on-premise edition in 2025."
Link to the primary source, such as your pricing page, changelog or press release.

One caution: accuracy is not guaranteed even with strong evidence. Ahrefs' 2026 benchmark reported that most AI models repeated fabricated claims even when official sources contradicted them. Publishing the correction is necessary, but you still need to check whether it lands.

What does not work

Some tactics promise to change what AI knows but have little supporting evidence.

Special AI files as a fix. Google says there are no special optimizations or AI text files required to appear in AI Overviews or AI Mode, and Google's John Mueller called llms.txt "purely speculative for now" in June 2026. An llms.txt file will not rewrite a model's memory.

Prompting the assistant yourself. Repeatedly telling an assistant the correct facts in your own account affects your own sessions at most. It does not reach other users.

Planting content. Fake reviews, sock-puppet forum posts and paid mentions dressed up as independent opinions carry real risk. The FTC's final rule on fake reviews and testimonials bans fake and AI-generated reviews and allows civil penalties. Wikipedia's conflict of interest guideline also discourages people from editing articles about their own companies directly and requires paid editors to disclose. This is general information, not legal advice.

Expecting paid ads to shape answers. OpenAI states that ads in ChatGPT "do not influence the answers ChatGPT gives you".

A practical sequence for influencing AI knowledge

The following framework orders the work from fastest impact to slowest. It is a recommendation, not a guaranteed process.

Diagnose what each assistant says. Run a fixed set of prompts about your company across ChatGPT, Gemini, Claude, Perplexity, Copilot and Google AI Mode. Record factual errors, outdated claims and which sources get cited. Because answers vary, run each prompt several times. SparkToro and Gumshoe research found less than a 1 in 100 chance that two responses list the same brands.
Separate memory errors from retrieval errors. If the wrong answer cites a source, the problem is retrieval and you can fix that source. If it cites nothing and the assistant answers from memory, the problem is training data and you need both fresh retrievable pages and a longer-term public record.
Fix access. Confirm AI search crawlers are allowed and make a deliberate choice about training crawlers.
Publish one canonical version of your facts. Create or update the pages that state what you do, who you serve, pricing approach, locations and key changes.
Update third-party sources. Prioritize the review sites, directories and articles that assistants actually cite for your brand.
Earn accurate coverage. Pursue genuine mentions in industry publications, podcasts, comparisons and community discussions.
Re-measure on a schedule. Retrieval fixes should show up within weeks. Training changes show up only after new model releases, so compare answers before and after major model updates.

A hypothetical example

Consider a hypothetical HR software company that pivoted from payroll to performance management two years ago. When buyers ask an assistant about it, the answer still calls it a payroll tool.

A diagnosis shows two patterns. In assistants that search the web, the wrong description comes from an old directory listing and a 2023 comparison article, both still ranking. In answers given without search, the model simply remembers the old positioning. The company updates the directory listing, asks the comparison author for a correction, publishes a clear "What we do" page with a dated note about the pivot, and aligns founder bios and review profiles. Retrieval-based answers improve first. Memory-based answers change only after later model releases, and only because the public record now tells one consistent story.

How Bob Builds AI helps

Bob Builds AI is an AEO and GEO platform and agency built for exactly this kind of problem, where the fix spans your site, third-party sources and ongoing measurement. Visibility Monitoring tracks how your brand appears across ChatGPT, Gemini, Claude, Perplexity, Copilot and Google AI Overviews and AI Mode, including citation sources, sentiment and recommendation changes over time, and it measures the real chat and search interfaces instead of raw model APIs. Brand Memory keeps one source of truth for products, differentiators, messaging and proof points so your published facts stay consistent. For the page-level work, see our guide on how to make AI engines understand your company.

FAQ

Can I ask ChatGPT or Claude to update what it knows about my company?

No. You cannot submit an edit to a released model. Telling an assistant the correct facts in your own chats can affect your own sessions, for example through ChatGPT's per-account memory, but it does not change what the model tells other users. To change answers for everyone, improve the pages the assistant retrieves and the public record future models learn from.

How long does it take for AI answers about my company to change?

It depends on the layer. Answers built from web retrieval can change within days or weeks once corrected pages are crawled; OpenAI notes robots.txt changes for search take about 24 hours to apply. Answers from a model's built-in knowledge change only when the provider releases a new model trained on newer data, which can take months.

Should I block GPTBot, ClaudeBot and Google-Extended?

That is a business decision. Blocking training crawlers keeps your content out of future training for those providers, but it also removes your own authoritative description of your company from that input. Blocking search crawlers like OAI-SearchBot has a different effect: OpenAI says opted-out sites will not appear in ChatGPT search answers. Many brands allow both on public marketing pages.

Why does an AI assistant still describe our old product after we updated the website?

Usually because the assistant is answering from training data collected before your update, or because it retrieved an outdated third-party page. Check whether the answer cites a source. If it does, fix or update that source. If it does not, the answer likely comes from model memory, and you need fresh, crawlable pages plus consistent third-party coverage.

Does thumbs-down feedback correct wrong answers about my brand?

Feedback sends a signal to the provider about a specific response, and it is worth using for clearly wrong answers. However, providers do not present it as a way for companies to correct a model's knowledge, and there is no public evidence that a single report changes answers for other users. Treat feedback as a flag, not a fix.

Can editing Wikipedia fix what AI says about my company?

Wikipedia is a heavily cited source for ChatGPT, according to Profound's citation analysis, so it matters. However, Wikipedia's conflict of interest guideline discourages editing articles about your own company directly and requires paid editors to disclose. The safer route is to propose changes on the article's talk page with reliable independent sources.

Does blocking Common Crawl affect AI models?

It can. A Mozilla Foundation study found at least 64% of 47 text generation models released between 2019 and October 2023 used filtered Common Crawl data for pre-training. Common Crawl's crawler, CCBot, respects robots.txt, so blocking it may reduce how often your content appears in many future training sets, not only one provider's.

How do I know whether my changes are working?

Measure repeatedly rather than once. Run the same prompts about your company across several assistants on a regular schedule and track accuracy, cited sources and sentiment. AI answers vary a lot between runs, so compare percentages across many runs, and compare answers before and after major model releases to see whether training-level changes have landed.

Conclusion

You cannot reach into a trained AI model and rewrite what it knows about your company. You can, however, control the evidence around that model: the pages AI assistants retrieve today and the public record that future models learn from. Retrieval is the fast lever, and training influence is the slow one.

The practical implication is to treat AI knowledge as a reputation you maintain rather than a setting you change. Diagnose where wrong answers come from, fix crawler access, publish one consistent version of your facts, update the third-party sources assistants cite and keep measuring as new models ship. If you want to track those answers across assistants and see which sources drive them, Bob Builds AI can help you set up that monitoring and prioritize the fixes.