TL;DR — AI does not fact-check, so false claims about your brand will persist until you change the sources AI draws from. Go to Visibility > Responses to document the exact false claims and how frequently they appear, then use Visibility > Citation Sources to trace which URLs are feeding the misinformation. Plan corrective content in Strategy > Content Studio targeting the same queries, and use Strategy > Backlink Opportunities to place corrections on high-authority domains AI trusts. Pro tip: a correction on a third-party authoritative site (analyst report, major publication) carries far more weight with AI models than the same correction published on your own domain.
The Question
“How do I correct misinformation that AI is spreading about my brand?”AI models do not fact-check. They reproduce claims that appear frequently in their training data or in the sources they retrieve. When false information — an outdated price, a resolved legal issue, an inaccurate product description, a competitor’s marketing claim — appears in authoritative sources that AI models index, it gets reproduced in AI-generated answers. Users receive that false information as if it were established fact. Correcting AI misinformation requires a different approach from correcting a Wikipedia article or responding to a bad review. You cannot edit the model directly. Instead, you must change the information ecosystem the model draws from: create authoritative content that states the correct information, get it indexed and cited, and ensure it outweighs the sources driving the false claim. You might also be wondering:
- “How do I know which source is causing AI to repeat a false claim?”
- “How long does it take for a correction to show up in AI responses?”
- “Can I ask AI providers directly to correct false information about my brand?”
Where to Go in Qwairy
Start here: Visibility > Responses
Navigate to Visibility > Responses — your primary tool for identifying the specific false claims being made.
Read responses across providers and flag the ones containing inaccurate or outdated information. Note the exact language used, the provider, and the date. Collect at least 5–10 examples before moving to source analysis — you want to understand the scope of the problem before tracing the source.
Trace the source: Visibility > Citation Sources
Cross-reference with Visibility > Citation Sources to identify which URLs are being cited in responses that contain the false claim.
Filter responses by the prompt or topic associated with the misinformation, then examine the citation panel. The source articles AI is using to construct the false claim will appear here. These are the files you need to counteract.
Plan corrective content: Strategy > Content Studio
Navigate to Strategy > Content Studio to plan the content assets that will establish the correct narrative.
Use the keyword and topic intelligence in Content Studio to create content that will rank for the same queries triggering the misinformed responses. Authoritative content that directly addresses the false claim is your primary remediation lever.
Strengthen citations: Strategy > Backlink Opportunities
Open Strategy > Backlink Opportunities to identify high-authority third-party sources where you can place or request correct information.
AI models weight authoritative, well-cited sources heavily. A correction published on your own domain is less powerful than the same correction appearing in an industry analyst report, a major tech publication, or a Wikipedia article. Use Backlinks to find the right placement targets.
Verify correction over time: Insights > Query Fan-Out
Use Insights > Query Fan-Out to monitor whether the corrective content is gaining traction.
Track whether the queries triggering the false claim are now surfacing your corrective content. This closes the feedback loop — you can confirm whether the remediation is working before the next AI model update cycle.
What to Look For
Responses — Identifying False Claims
Before you can correct misinformation, you need to document it precisely. The Responses page lets you read every AI-generated answer and flag the ones containing false or outdated information.| Element | What it tells you |
|---|---|
| Specific false claims | The exact sentences AI is repeating incorrectly |
| Frequency | How often the false claim appears across monitored responses |
| Provider distribution | Whether the false claim appears on all providers (widely sourced) or one (source-specific) |
| Response date | Whether the false claim is recent or historical — recent claims mean current sources are still feeding it |
Citations — Tracing the Source
The Citations view shows which URLs AI models are referencing when they generate responses about your brand. For misinformation cases, the citation panel is your forensic tool: the source of the false claim is almost always visible here.| Element | What it tells you |
|---|---|
| Cited URLs | The specific articles, pages, or documents AI is drawing from |
| Source authority | High-authority sources (major publications, Wikipedia) are harder to counteract than low-authority ones |
| Source date | Old articles about resolved issues are a common misinformation driver — outdatedness is fixable |
| Citation frequency | A URL cited in 80% of false-claim responses is the primary target for your remediation |
Pro Tip: When you identify a citation source for a false claim, check whether you can directly address it: (a) if it is a review platform, respond with updated information or request a correction; (b) if it is an article, contact the author for an update; (c) if it is a Wikipedia article, submit a correction with verifiable references; (d) if it is a defunct page, create better content targeting the same query.
Filters That Help
| Filter | How to use it for this question |
|---|---|
| Provider | Isolate the provider where the false claim appears most — this tells you which source ecosystem is amplifying the misinformation |
| Topic / Tag | Narrow to the topic area where the false claim lives to avoid mixing it with unrelated signals |
| Period | Check whether the false claim is recent or long-standing — a claim that appeared 6 months ago and is still present requires a different strategy than one that just appeared |
How to Interpret the Results
Good result
The false claim appears in fewer than 20% of monitored responses. The citation source is identifiable, low-authority, and likely to be outweighed by new authoritative content. The claim is factually resolvable (an outdated price, a resolved issue, an old product name) rather than a matter of interpretation. The false claim is not being amplified by a provider with live web retrieval (like Perplexity) pulling from a currently indexed page.Needs attention
The false claim appears in 50%+ of responses across multiple providers. The primary citation source is a high-authority publication, an analyst report, or a widely referenced article that will be difficult to outweigh. The false claim involves a legal, financial, or reputational issue (e.g., “was sued for X”, “went bankrupt in Y”) that has long-term indexing shelf life. A retrieval-based provider is consistently serving the false claim from a currently live article that is still indexed and ranking.Example
Scenario: A fast-casual restaurant chain with 45 locations discovers that AI responses consistently state they “do not offer online ordering” and describe them as “dine-in only”. The chain launched a full online ordering and delivery platform 10 months ago. The false claim is steering potential customers to competitors when they ask AI for restaurants with delivery options.
- Open Visibility > Responses and filter by Topic = “Ordering & Delivery”. Read 14 responses. 10 of them describe the chain as “primarily a dine-in experience” or explicitly state “does not currently offer online ordering or delivery”. Only 2 responses mention the delivery platform, and one of those hedges with “reportedly launched delivery in select markets”.
- Open Visibility > Citation Sources filtered to responses containing “dine-in only”. The citation panel shows three recurring sources: (a) a food blog review from 20 months ago that praised the “dine-in-only concept”, (b) a Yelp profile that has not been updated with delivery information, and (c) a local news article about the brand’s original no-delivery philosophy from before the pivot.
- Prioritize: the food blog article is cited in 8 of 10 false-claim responses — it is the primary source. Contact the food blogger with documentation of the delivery launch (press release, app screenshots, delivery partner integrations) and request an updated review. If no response within 2 weeks, create a comprehensive “How to Order” guide targeting the same keywords.
- Open Strategy > Backlink Opportunities. Identify 3 high-authority food and restaurant industry publications that cover delivery platform launches. Pitch the story: “[Chain] expands to full online ordering and delivery across all 45 locations” — a concrete expansion story that food media outlets will cover.
- Open Strategy > Content Studio. Create a dedicated “Order Online” landing page with structured data markup (menu, delivery radius, partner apps), and a blog post titled “Now Delivering: How [Chain] Brings [Cuisine] to Your Door” targeting delivery-related queries. Ensure both pages include the launch date prominently.
- Set a monitoring alert in Qwairy for the topic “Ordering & Delivery” with a threshold flag if “dine-in only” appears in new responses. Recheck in 30 days. After the food blog is updated and the new content is indexed, expect the false claim to fade from retrieval-based providers (Perplexity) within 2–4 weeks and from training-based providers (ChatGPT, Claude) at their next model update.
Go Further
Find the exact misinformation
Read the Analyzing Answers documentation to locate the specific AI responses containing misinformation about your brand
Export the correction evidence package
Export the inaccurate AI responses and supporting data as evidence for your content correction plan
How to correct what AI says about you
Read the GEO Guide for a strategic framework on influencing what AI engines say about your brand
Related Questions
What does AI actually say about my brand?
Start with the full narrative inventory before identifying specific false claims.
Which sources is AI citing about my brand?
Deep-dive into citation analysis to identify every source feeding the false narrative.
What content should I create to improve AI visibility?
Build the corrective content strategy that will replace the misinformation sources.
Is the AI perception of my brand aligned with our actual positioning?
After correcting misinformation, check whether overall positioning alignment has improved.