Accuracy monitoring is the check on what engines claim about you: every claim tested against your approved facts, traced to its source page, correction drafted — so being described wrongly gets caught early.
Accuracy and corrections is the part of MentionBeat that deals with being described wrongly rather than not being described at all. It checks every claim an engine makes about you against the facts you approved, traces a wrong one back to the page it most likely came from, and drafts a correction request for that page's owner — then re-reads the page later to confirm the change landed. Being recommended with the wrong price is its own kind of invisible. They look identical in an answer and need completely different responses, which is why the tool separates them.
A price, a tier name, a limit or an integration that changed and a retrievable page still states the old version. The most common case, and the most fixable.
You are being merged with a similarly named company, or your product is credited to a parent or a competitor. A grounding problem, not a content one.
A capability, limitation or customer that does not exist anywhere, produced from parametric memory rather than a source. Rarer, and only addressable by making the true version highly retrievable.
Every accuracy item follows the same path, and it ends the same way the fix queue does — on evidence, not on assertion.
The last step is the one that matters. A publisher updating a page is a good day; the engines repeating the corrected version is the actual outcome, and only a re-measurement shows it.
Beyond individual pages, MentionBeat tracks the entity records assistants lean on to work out who you are — directory listings, knowledge-graph entries, professional profiles, aggregator pages.
These are the highest-leverage corrections available, because a single wrong record can contaminate every grounded answer at once. They are also the ones nobody owns internally, which is why they rot.
An LLM reads every answer and decides whether you were named, where, and in what tone. That judge has its own error rate — and an uncalibrated judge can move a headline number by more than the change you are trying to detect. So we treat it as an instrument and measure it.
Where a human label exists, it overrides the judge for your brand on the next recompute. Competitor mentions stay judge-scored, and the report says so rather than implying the whole row was human-verified.
Every other number on this page rests on the judge being right. A tool that scores answers with an LLM and never checks that LLM is reporting its own error as if it were your visibility.
It is also the question to ask any vendor, including us: how do you know your scorer is accurate, and what is the number?
Accuracy is the area where AI-visibility tools over-promise most, so it is worth being explicit about the boundary.
| Sometimes promised | What is actually true |
|---|---|
| "Remove hallucinations about your brand" | Nobody can edit a model's memory. You can change what it retrieves, and wait for the next training cycle |
| "Guaranteed correction within 30 days" | Third-party publishers decide their own timelines. We draft, track and verify — we do not control the other end |
| "Direct line to the AI providers" | There is no submission portal for brand facts. The lever is the open web, and it is the same lever for everyone |
| "We monitor everything said about you" | We monitor the answers your prompt suite produces, on the engines you enable. That scope is stated on every number |
Two clocks apply here: retrieval-layer fixes can change grounded answers within days, while parametric memory only shifts across training cycles. Every correction item says which clock it is on.
A first run tells you whether your problem is absence, or being present and described incorrectly.