TL;DR — Yes, AI models frequently cite stale or wrong information because they rely on cached snapshots of the web. Go to Visibility > Responses to read the exact wording AI uses about your brand, then trace the offending source URL in Visibility > Citation Sources. Check Strategy > Brand Perception to see if misalignment is systematic or isolated. Pro tip: if you updated a page but AI still cites the old version, signal freshness through structured data updates, new inbound links, and resubmission via Google Search Console.
The Question
“Are AI models citing outdated or inaccurate information about my brand?”AI models are trained on snapshots of the web, and their retrieval layers pull from cached or indexed versions of pages. A pricing page from 18 months ago, a press release about a product that has since been discontinued, or an early review written before a major rebrand can persist in AI responses long after you have updated the underlying content. The problem is compounding: if that stale content is also being cited by multiple AI models across many prompts, it becomes the dominant narrative — and users making purchasing decisions or forming opinions about your brand are acting on information you no longer stand behind. You might also be wondering:
- “What is the AI saying about my brand that is wrong?”
- “How do I fix AI responses that contain inaccurate brand information?”
- “Which sources are responsible for spreading outdated information about my brand?”
Where to Go in Qwairy
Start here: Visibility > Responses
Navigate to Visibility > Responses and read actual AI-generated answers verbatim — this is the only way to confirm what is being said, not just inferred.
Focus on answers that score low on Accuracy alignment or that contain factual claims about pricing, features, team, or company history.
Go deeper: Visibility > Citation Sources
Cross-reference with Visibility > Citation Sources to identify which URLs the inaccurate responses are drawing from.
Use the URL filter to find specific pages that appear repeatedly alongside low-accuracy responses.
Complete the picture: Strategy > Brand Perception + Sentiment Analysis
Open Strategy > Brand Perception to detect systematic misalignment between how AI describes your brand and how you define it.
Pair this with Insights > Sentiment Analysis to see whether the inaccuracies are neutral (factual errors) or directionally negative (misleading framings).
What to Look For
Visibility > Responses — Verbatim AI Answers
Reading raw responses is irreplaceable. Metrics tell you something is wrong; responses tell you exactly what is wrong and how it is phrased.| Element | What it tells you |
|---|---|
| Response text | The exact wording AI used — check for outdated product names, wrong prices, discontinued features, or superseded company information |
| Accuracy score | Qwairy’s automated alignment score comparing the AI answer to your brand profile; low scores flag candidates for manual review |
| Cited URLs inline | Which specific pages the model cited when producing this answer — your first clue about the upstream source of the error |
| Model label | Which AI provider generated this response; inaccuracies often cluster on specific models with older training data |
| Prompt context | The question that triggered this response — helps you understand whether the error appears for high-intent prompts or only edge cases |
Visibility > Citation Sources — URL-Level Source Tracing
Once you have identified an inaccurate response, the Citations view lets you trace exactly which URL is responsible.| Element | What it tells you |
|---|---|
| Cited URL | The full page path being referenced — you can open it directly to see what the page currently says |
| Last indexed date | When Qwairy last confirmed this URL was active and indexable; a gap between last-indexed and today hints at stale content |
| Citation frequency for URL | How many responses link to this URL; a frequently cited stale page is a high-priority fix |
Pro Tip: Combine Responses (to confirm an inaccuracy exists) with Citations (to find the source URL) and then check whether that URL has been updated on your own site. If you updated the page but AI is still citing the old version, the issue is cache lag — and you need to signal freshness through structured data updates, fresh inbound links, or resubmission via Google Search Console.
Brand Perception — Misalignment Detection
Brand Perception compares AI-generated descriptions of your brand across multiple prompts against the attributes and positioning you have defined. It surfaces systematic misalignment rather than one-off errors.| Element | What it tells you |
|---|---|
| Attribute alignment score | How consistently AI describes your brand using your intended attributes (e.g., “enterprise-grade”, “easy to use”, “GDPR-compliant”) |
| Off-brand claims | Specific phrases AI uses that contradict your positioning — useful for identifying which outdated sources are pulling the narrative |
| Comparison drift | How your perception alignment score has changed over time; a declining score after a rebrand suggests the old narrative is still dominant |
Filters That Help
| Filter | How to use it for this question |
|---|---|
| Provider | Check each model independently — GPT-4o might cite a stale source that Perplexity no longer indexes |
| Period | Run comparisons across periods to detect whether inaccuracies are new (something changed) or persistent (a long-standing source problem) |
| Topic / Tag | Narrow to the specific topic where you suspect the inaccuracy — pricing, features, team, history — to reduce noise |
How to Interpret the Results
Good result
Responses are consistent with your current brand profile. Cited URLs point to recently updated pages on your own site or credible third-party sources that reflect your current positioning. Brand Perception alignment scores are above 75 and stable or improving. Any isolated inaccuracies appear only in one or two low-traffic prompts and do not involve pricing, compliance, or core feature claims.Needs attention
Multiple responses across several prompts contain the same incorrect claim — for example, a discontinued pricing tier or a deprecated integration listed as current. The cited source is a page you updated months ago but that AI models are still referencing in its original form. Brand Perception alignment on a key attribute has dropped by more than 15 points over 60 days. Inaccuracies appear on high-intent prompts that users are likely to act on.Example
Scenario: A regional airline launched a new premium economy cabin class six months ago, replacing its old “Economy Plus” product. However, when Perplexity and ChatGPT answer questions about the airline’s seating options, they still describe “Economy Plus” with the old seat pitch and baggage allowance — information pulled from an aviation review site that has not updated its cabin comparison page.
- Open Visibility > Responses and search for responses mentioning “Economy Plus.” Filter by Provider: ChatGPT and Provider: Perplexity. Confirm both models describe the discontinued cabin class across multiple high-visibility prompts about the airline’s in-flight experience.
- Switch to Visibility > Citation Sources and filter for responses referencing the old cabin name. The top cited source is an aviation comparison site whose cabin review page was last updated 14 months ago — before the rebrand.
- Open Strategy > Brand Perception and check the attribute for “premium cabin offering” — it shows 41% misalignment, confirming that AI models systematically describe the airline using the old product structure rather than the current one.
- Reach out to the aviation review site with updated cabin specs and press materials. Simultaneously, publish a comprehensive cabin guide on the airline’s own site with structured data markup, clear FAQ schema covering seat pitch, baggage, and upgrade options, and earn inbound links from travel media to accelerate AI models’ shift to the new content.
Go Further
Export outdated citation inventory
Export the list of outdated or inaccurate AI citations about your brand for your content correction workflow
Read responses to find inaccuracies
Read the Analyzing Answers documentation to inspect the exact AI responses containing outdated information
Share the correction plan with your content team
Create a shared view highlighting outdated citations for your content team to prioritize corrections