How to Fix Wrong Information AI Gives About Your Brand

An AI assistant produces an answer about your brand in one of two ways. Either it recalls what it absorbed during training, or it runs a live web search and reads whatever pages come back. Training data has a cutoff, so a memory-based answer describes your company as it looked months or years ago. If you raised prices in January or retired a product in March, the model's memory still holds the old version and will state it with complete confidence.

The gap is measurable. In July 2026 we tested 30 brands across both modes. Answering from memory alone, assistants misdescribed 27 of the 30 (90 per cent). With live search switched on, errors fell to 13 of 30 (43 per cent). The full method and results are in our study of how often AI gets brands wrong. Two things follow from those numbers. Search roughly halves the error rate, so anything you do to improve what retrieval finds pays off first. And search does not eliminate errors, because the assistant can only be as accurate as the pages it happens to read.

Before fixing anything, work out which errors are costing you money. Wrong pricing quoted to a buyer who is mid-comparison costs revenue directly, and a discontinued product described as current generates support tickets and refund disputes. By contrast, a wrong founding year in a company summary is irritating but rarely commercial. Rank each wrong claim by two questions: does it appear in queries real buyers ask, and would it change a purchase decision?

To see what assistants currently say, ask them the questions your buyers would ask, with browsing enabled and, where the interface allows it, disabled. Note the exact wording of each wrong claim, because you will reuse that phrasing when you correct sources later. For a structured baseline, the free AI visibility check runs live queries about your brand through Claude with web search and needs no login.

The highest-leverage correction is a page on your own domain that states the fact in plain, crawlable text. If your pricing is wrong in AI answers, your pricing page should carry the current numbers as ordinary HTML text, not an image or a figure rendered only by JavaScript. If a dead product keeps surfacing, publish a short product status page that says so directly: 'Acme Legacy was discontinued in March 2025. Its replacement is Acme Cloud.' Assistants doing retrieval lift plain declarative sentences, so give them one to lift.

Date the fact ('As of July 2026, plans start at £49 per month') and keep one canonical page per fact rather than scattering versions across the site. Phrase it the way a buyer would search for it. Then confirm the crawlers that feed AI answers can actually reach the page: the free AI crawler check reads your robots.txt against 12 AI crawlers and checks for an llms.txt file. Many sites block these fetchers without realising it, which forces assistants back onto stale third-party pages.

Retrieval-backed answers cite their sources, which makes the wrong ones findable. Ask ChatGPT, Claude and Perplexity your buyer questions with browsing on, and note every page each answer cites. Then search Google for your brand name next to the wrong fact itself, for example 'YourBrand pricing £29', to find any page still repeating it.

The usual culprits are software review directories with old pricing tiers, comparison posts written in a previous year, aggregator listings and out-of-date press coverage. Claim and update every listing you control directly. For editorial pages, email the author with the correction and a link to your canonical page, and ask for an update rather than a removal; a corrected page that ranks well becomes a source working in your favour. Be realistic about response rates, because some site owners never reply. The aim is to shift the majority of what retrieval reads, not to scrub the web clean.

Contradictions in your own scattered footprint cause errors you cannot pin on anyone else. An old LinkedIn tagline or a Google Business Profile showing a previous price range reads, to a retrieval system, as evidence. When sources conflict, the model may pick the stale claim, or blend the versions into something that was never true at all.

Sweep everything you control: social bios, app store and marketplace listings, job adverts and any old landing pages still indexed. Update or redirect stale pages rather than leaving them live. Keeping every statement about your company consistent and machine-readable is the core of brand grounding, and it is the one part of this process entirely within your control.

Corrections do not land instantly, and it helps to know which timescale applies to which fix. Changes on your own site and on third-party pages typically show up in retrieval-backed answers within a few weeks, once the pages are recrawled. Errors baked into a model's training memory are different: they persist until the vendor ships a refreshed model, which can take months and is outside anyone's influence. No vendor can get a memory-based error removed on demand, so treat any guarantee of that with suspicion.

That lag is why a one-off fix is not enough. Third-party pages drift, and each new model version arrives with a different memory. Re-run your buyer queries on a schedule, monthly by hand at minimum, and keep a record of what changed. If you would rather automate it, Discoverable's paid plans (Growth at £99 per month, Scale at £299 per month) re-check your brand automatically every week and chart the score trend over time, with per-product-line tracking on Scale. However you run the checks, keep them on a schedule, because an answer that is correct today can change after the next crawl or the next model release.