Answers / Getting cited

How to fix wrong information AI gives about your business

By Hoss · Published July 13, 2026 · Sources linked at the end

Why is AI wrong about your business in the first place?

Because somewhere, a source the engine reads is wrong — or several sources disagree. When an assistant answers a question about your business with live search grounding, it assembles the answer from what it can retrieve: your site, directories, review platforms, third-party pages. Inconsistent name, address, and phone data across those sources reads as uncertainty about your business, and uncertain entities get vague or wrong answers [1]. One diagnostic industry sources recommend: ask the engine "What can you tell me about [your business]?" — a vague or incorrect response signals weak entity recognition, usually from fragmented or conflicting information [2].

The other possibility is that the error lives in the model's training data rather than in a live source. That case is real, slower to fix, and covered honestly at the end of this page.

Step 1: Capture the error verbatim

Before fixing anything, document exactly what was said. Open a fresh chat — no history, no memory carrying over from earlier conversations — ask the question the way a customer would, and record the full answer word for word, with the date and the engine [3]. If the engine cites sources, save those links; they're about to matter more than anything else on this page. Repeat on each engine your customers use, because the engines don't all make the same mistake.

Step 2: Trace where the error comes from

Start with the cited links — when a grounded answer shows its sources, the wrong fact is usually sitting in one of them. When there are no citations, work through the usual suspects in order:

• Your own site. A stale services page, an old address on a contact page, a pricing page from two redesigns ago. Engines treat your site as a primary source; if it disagrees with itself, you wrote the error.
• Directory listings. Inconsistent name/address/phone data across directories is one of the most-cited causes of AI getting businesses wrong [1] [4].
• Your Google Business Profile. Old hours, a former location, a category you no longer serve — GBP data flows into many of the sources engines read [4].
• Third-party profiles and articles. Old team pages, chamber-of-commerce listings, press mentions with outdated facts. This pool matters more than most owners expect: one industry analysis estimates your own site is only about 15% of your AI presence, with roughly 85% coming from third-party sources [5].

Step 3: Fix the error at the source

Fix in the same order you traced.

• On your own site, publish one canonical, current statement of the fact — one page that owns the answer, not three pages with three versions. If you use structured data, Google's documentation is explicit that it must match the visible text on the page [6]; schema that contradicts your copy adds to the confusion you're trying to remove.
• In directories and profiles, update the listings you control and make name, address, and phone identical everywhere — consistency is the signal [1] [4].
• On pages you don't control, request corrections: an email to the publisher with the correct fact and a link to your canonical page works more often than owners expect. You can't force it, but every corrected third-party page shrinks the pool feeding the error.

Step 4: Make the correct fact machine-readable

Once the fact is right, make it easy to lift. State it in plain, direct sentences near the top of the relevant page rather than burying it in marketing copy. Add structured data that carries the same fact — matching the visible text, per Google's guidance [6]. If you maintain an llms.txt file, keep the fact current there too. The goal is that a retrieval system reading your site encounters exactly one version of the truth, stated plainly, in both human-readable and machine-readable form.

Step 5: Re-check on a schedule

Grounded answers update when their sources and indexes do — so re-run the exact question from step 1, on the same engines, after a decent interval. Some plumbing moves fast: OpenAI documents roughly 24 hours for robots.txt changes to take effect on its crawlers [7]. Content corrections need re-crawling and re-indexing, so give them longer. One check proves nothing either way — answers vary run to run, which is why re-checking is a schedule, not an event.

How long does this actually take?

When this won't work (and what to do then)

Honesty section. If the wrong fact comes from a model's training data and the engine answers without live grounding, there is no source to fix — the error is baked into the model's parametric memory, and it fades only as newer models train on corrected sources. That can take a long time, and nobody outside the model provider controls the schedule. What you can do: make sure every live source is correct so grounded answers get it right today and future training runs inherit the truth, and steer customers toward the grounded answer by being well-cited. What you can't do is edit the model — and any vendor who claims otherwise is selling something that doesn't exist.

Not sure yet whether AI is getting you wrong at all? Start with our free self-check — the same capture process as step 1, spelled out end to end.