How to Fix What AI Assistants Say About Your Brand

An AI assistant misstating your product is a measurable defect with a repair procedure, not a PR emergency. The workflow below is the one we keep ready for our own 3 builds: document the error, trace its source, fix the inputs, and log propagation engine by engine. The mechanism guarantees an uneven timeline — engines that fetch live pages update first, training-frozen answers lag — and that unevenness is itself the most useful thing to understand before you start.

Why do AI assistants get brand facts wrong?

Three distinct failure modes produce wrong brand answers, and the fix depends on which one you have. First, stale training data: Kevin Indig's 2026 analysis found 24% of ChatGPT answers are generated without fetching any live page (Growth Memo, 2026) — those answers describe the world as of the model's training, and your last two years of changes may simply not exist in it. Second, bad retrieval: the engine fetches sources at answer time and lands on an outdated directory listing, an old review, or an article about your 2023 pricing. Third, entity confusion: the assistant blends you with a similarly-named company, inheriting their facts.

The diagnosis is cheap: ask the assistant which sources it used. Cited, checkable URLs mean a retrieval problem — fixable by fixing pages. No sources and a vintage-sounding answer means training data — fixable mostly by patience plus a fresh, fetchable record. Wrong-company details mean entity confusion — fixable by sharpening the identity signals on every surface you control.

What is the correction workflow?

Six steps, in order. Skipping to step 4 — firing off correction requests before tracing sources — is the most common way to waste a month.

Step 1 deserves the repetition it asks for: answer engines are non-deterministic, so a misstatement that appears once in five runs is noise, while one that repeats across sessions is a finding worth the workflow. Our free share-of-voice method exists partly for this — its "wrong" column is where these incidents surface on a schedule instead of by accident.

What should the brand-facts page contain?

Plain, dated, declarative facts in raw HTML: what the company is named, what it sells, what it costs in general terms, who it serves, and — when an assistant is repeating a retired fact — what is no longer true, stated explicitly. Write it the way an engine quotes: short sentences, one fact each, no marketing gloss. Google's people-first content guidance points the same direction — content that demonstrates first-hand knowledge of the subject, published for readers rather than rankings (Google) — and a facts page about your own company is the easiest such page you will ever write.

Freshness is the multiplier. In Indig's 2026 data, content under 3 months old was roughly 3× more likely to be cited — so a facts page with a visible, honest "last updated" date, actually maintained, outperforms a static about page as a retrieval target. This is the same extraction logic that governs all citable content, covered in depth in the operator's guide to generative engine optimization.

Why does crawlability decide whether corrections land?

Because the correction only exists for an engine that can fetch it. The platforms document user-fetch agents — ChatGPT-User (OpenAI), Claude-User (Anthropic), Perplexity-User (Perplexity) — that retrieve pages live during conversations, plus search-index crawlers that keep their answer indexes current. A site that blocked these agents in robots.txt has, by construction, told assistants to answer brand questions from whatever stale third-party sources remain reachable.

Check your robots.txt against the documented agent names before anything else — the full inventory is in our AI crawlers list. If accuracy is the priority, the user-fetch class stays open; that trade-off is one of the main reasons we allow it across our fleet [our data].

How long does a correction take to propagate?

Nobody can promise a number, including us, and the mechanism explains why. Engines that retrieve live pages can reflect a fixed source within their crawl and index cycles — expect those surfaces to correct first, in weeks rather than days. Answers generated from training data alone have no retrieval step to intercept: the 24% of ChatGPT answers involving no live fetch (Indig, 2026) change when the model or its refresh pipeline does, on the platform's schedule, not yours.

So the honest posture is a propagation log, not a countdown: retest the documented prompts monthly per engine, record which have corrected, and keep the sources clean in the meantime. Anyone selling a fixed timeline to a fixed AI answer is selling control they do not have — the same red flag as any vendor promising specific answer-engine outcomes.

When is this not worth doing?

Against our own interest as a shop that runs these audits: if the wrong answer is rare, minor, and appears only under prompt phrasings no customer would type, skip the workflow. A misstatement surfacing in one run out of five, on a question nobody asks, costs you almost nothing — and chasing it consumes real hours. Run the monthly battery, act when an error is repeatable and material to buyers, and spend the remaining effort making the accurate answer easier to find than the stale one — the offense side of the same game, covered in how to get cited by ChatGPT.