AI Cited Us 6,027 Times for Something We Don't Sell
5 minutes
TL;DR
🔐 AI cited Averi 6,027 times for security/data-security queries last quarter — 10.5% of all our citations — across 43 distinct queries
📭 We've published zero content built around security as a theme; AI is citing us for it anyway
⚠️ A citation gap is a risk (cited for claims your content doesn't substantiate) and an opportunity (measured demand AI is already routing to you)
🧮 The fix is a three-bucket audit: Backed (cited + substantiated), Exposed (cited + thin), Untapped (demand + no content)
📊 This came out of the same dataset where AI cited us 95,431 times in 89 days while only 2.2% of citation queries matched our keyword rankings
🛠️ You can run the same audit in Bing Webmaster Tools for free in under an hour
Zach Chmael
CMO, Averi
"We built Averi around the exact workflow we've used to scale our web traffic over 6000% in the last 6 months."
Your content should be working harder.
Averi's content engine builds Google entity authority, drives AI citations, and scales your visibility so you can get more customers.
AI Cited Us 6,027 Times for Security. We Don't Talk About Security.
Last quarter, AI engines cited Averi 6,027 times for security and data-security queries.
"AI marketing platforms strong security features."
"AI marketing platforms with strong data security."
"AI marketing software data privacy compliance regulations."
Forty-three distinct queries, more than 10% of every AI citation we earned.
Here's the problem… we have never built a single piece of content around security. It's not a positioning pillar. It's not a blog category. We have a data-handling posture like any SaaS, but as a content theme, security is a blank page. And AI is citing us as if it isn't.
That's an AI citation gap, and finding ours changed how I think about answer engine optimization. It's not only about earning more citations. It's about knowing what you're being cited for, and whether your content can actually back it up. This piece is the concept, the risk, the opportunity, and the audit you can run on your own data this week.
What is an AI citation gap?
An AI citation gap is the distance between what answer engines cite your brand for and what your content actually substantiates. When AI cites you for a topic, attribute, or claim that your published content doesn't meaningfully cover, you have a gap.
It runs in two directions: AI citing you for something you can't back up (a credibility risk), and high-citation demand for a topic you've published nothing on (a missed opportunity).
Most teams measuring answer engine performance count total citations and call it a day. That misses the more useful question. A citation for "best AI marketing platform features" when you have deep feature content is earned. A citation for "strong data security" when you've published nothing on security is exposure.
They look identical in a citation count. They are not the same thing.
We found ours by accident, auditing the grounding queries behind our first-party citation data. The security cluster jumped out because it was large, consistent, and pointed at a topic I knew we'd never written about.
The data: 6,027 citations for a topic we never covered
Here are the top security grounding queries that pulled Averi into AI answers last quarter:
Security grounding query | AI citations |
|---|---|
AI marketing platforms strong security features | 1,752 |
AI marketing platforms strong data security | 1,606 |
AI marketing platforms with strong data security | 588 |
AI marketing platforms best data security | 465 |
AI marketing platforms with strong data security features | 241 |
AI marketing platforms data security features | 217 |
AI marketing platform strong security features | 184 |
AI marketing platforms data security compliance | 71 |
AI marketing software data privacy compliance regulations | 54 |
Every one of these is an evaluation-stage query. A buyer is asking AI to recommend marketing platforms with strong security, and Averi is being handed back as a grounding source.
That's high-intent: it's the exact moment a buyer is building a shortlist. We're in the consideration set for a buying criterion we've never addressed in our content.
For scale, this sat inside a quarter where AI cited us 95,431 times total across Bing and Copilot, with citations growing 14x from February to May.
Security alone was 10.5% of that.
Only 2.2% of the queries AI cited us for matched what we rank for in traditional search, so this isn't visible in a keyword report at all. The only way we saw it was by reading the grounding queries directly.
Why this happens: AI infers attributes you never claimed
The mechanism is worth understanding, because it's why citation gaps are common rather than rare.
Answer engines don't only retrieve on what you explicitly say. They infer.
When a buyer asks for "AI marketing platforms with strong data security," the model assembles an answer from sources that are strongly associated with the category "AI marketing platform," then attaches the requested attribute. If your brand is a well-established entity in that category, you can get pulled into the attribute-specific answer even if you've never made the attribute claim yourself. The model is reasoning by association, not by reading a security page you wrote, because you didn't write one.
This is the flip side of query fan-out. The engine decomposes a buyer's question into attribute-specific retrievals, and category entities get matched to attributes they're adjacent to. The stronger your category presence, the more attributes you'll get inferred into, including ones you've never substantiated.
We've spent two years making Averi a recognized entity in the AI-content-platform category. The security citations are a side effect of that, not a result of any security content.
Why a citation gap is a real risk
Being cited for something you can't back up is not a neutral event.
When a buyer follows an AI citation to your site expecting the security story the AI implied, and finds nothing, you've created a credibility gap at the worst possible moment, mid-evaluation. Worse, when the model has no substantiated source to ground its claim, it's more likely to generate a vague or inaccurate statement about you. You don't control what AI says you do; you only control what you've published for it to cite. 44.2% of AI citations come from the first 30% of a page, which means if your security content is thin or buried, the model fills the gap with inference. Inference is where hallucinated claims about your product come from.
There's a brand-trust dimension too. Security and compliance are trust-sensitive topics. Getting surfaced for them with nothing behind it is the kind of mismatch a careful buyer notices, and it undercuts the authority you were trying to build. Promotional or unsubstantiated content correlates negatively with citation quality, and the same logic applies to claims you imply but never prove.
Why a citation gap is also an opportunity
Here's the reframe that makes this worth acting on rather than just worrying about.
A citation gap is measured demand. AI is already routing 6,027 security-related queries toward Averi without us lifting a finger.
That's not a hypothesis about what buyers might want; it's a count of what an answer engine has already decided we're relevant for. Most content teams guess at topics, publish, and wait to see what sticks.
The grounding report inverts that: it shows you exactly where demand already exists and where your content can convert it. That's a validated content roadmap, not a brainstorm.
Closing a citation gap is also one of the highest-return content moves available, because the hard part, earning the citation, is already done. You're not fighting to get surfaced for security. You're already surfaced. You just need to publish content strong enough that the citation lands on a page that substantiates the claim and converts the reader. The demand is captive. The content is the only missing piece.
How to run an AI citation gap audit
Here's the process we now run quarterly. It takes under an hour.
1. Pull your grounding queries. In Bing Webmaster Tools, open the AI Search Queries report for the last 90 days. This is the list of queries that pulled your content into AI answers, with citation counts. If you haven't verified your domain, do that first and wait two weeks for data. It's the most developed first-party AI-search reporting available.
2. Cluster by theme and attribute. Group the queries into themes (security, pricing, integrations, automation, analytics) and note total citations per theme. Pay special attention to evaluation attributes like "best," "strong," "secure," and "affordable," since those are what AI retrieves on. If a theme is new to you, check how it maps to the platforms buyers compare you against in platform-specific GEO.
3. Sort every theme into three buckets:
Bucket | Definition | Action |
|---|---|---|
Backed | AI cites you, and your content substantiates the claim | Maintain and deepen. This is working. |
Exposed | AI cites you, but your content is thin or silent on it | Risk. Publish substantiated content fast, or you're being cited for nothing. |
Untapped | High citation demand for a theme you have no content on | Opportunity. Validated demand waiting for a page. |
4. Prioritize by citation volume. Fix your highest-citation Exposed themes first, then build for your highest-volume Untapped themes. Our security cluster is both Exposed and high-volume, which makes it priority one.
5. Write to substantiate, not just to rank. For each gap, publish content that makes the attribute claim explicit and backs it with specifics: a number, a mechanism, a process, a real example. Front-load the proof and structure it for extraction with a self-contained FAQ. A model can only cite a substantiated claim if you've actually published one.
What we're doing about our security gap
In the spirit of showing the work: our security cluster is now bucket one on the content queue. We're publishing our actual data-handling and privacy practices as substantive content, not a buried trust badge, so that the 6,027 security citations land on a page that earns them.
We're also adding security as a recurring attribute in our comparison and category content, because the grounding data says buyers are evaluating on it whether or not we've been part of that conversation.
The broader lesson held for us and will hold for you: the citation count tells you that you're being seen. The grounding queries tell you whether what you're being seen for is something you can stand behind. Only one of those is actionable, and most teams aren't looking at it.
Who this is for
If you're a founder-led B2B startup with an established category presence, you almost certainly have citation gaps right now, because a strong entity gets inferred into attributes it never claimed. Audit before you assume your citations are all earned, ideally as part of your first 90 days of GEO.
If you're an early-stage brand still building category recognition, your gaps will be more Untapped than Exposed, which makes the grounding report a ready-made content roadmap.
If you operate in a trust-sensitive category (fintech, healthtech, security, compliance), treat Exposed citations as urgent: getting surfaced for a trust claim you haven't substantiated is a real liability.
We built Averi to score every draft for citation-readiness before it publishes, so the content you write to close a gap is structured to actually land the citation. The audit finds the gap. The engine helps you fill it.
What to do next
Pull your Bing Webmaster Tools AI Search Queries report and sort every theme into Backed, Exposed, or Untapped. The Exposed rows are the ones to act on this week. Then run your gap-closing content through the Averi engine so it's structured to land the citation, not just publish into the void.
FAQs
What is an AI citation gap?
An AI citation gap is the distance between what answer engines cite your brand for and what your content actually substantiates. It runs two ways: being cited for claims your content can't back up (a risk), and high citation demand for topics you've published nothing on (an opportunity). You find it by auditing your grounding queries.
How did Averi get cited for security without security content?
Answer engines infer attributes by association. Because Averi is an established entity in the AI marketing platform category, the model pulled us into attribute-specific queries like "strong data security" even though we never made that claim. AI reasons from category adjacency, not only from what you've explicitly published.
Why is being cited for something you don't cover a problem?
Two reasons. A buyer who follows the citation expecting substance finds nothing, creating a credibility gap mid-evaluation. And when the model has no real source to ground a claim, it's more likely to generate a vague or inaccurate statement about you. You only control what AI cites by controlling what you publish.
How do I find my own AI citation gaps?
Open the AI Search Queries report in Bing Webmaster Tools for the last 90 days. Cluster the queries by theme, note citations per theme, then sort each into Backed (cited and substantiated), Exposed (cited but thin), or Untapped (demand with no content). Prioritize fixing high-citation Exposed themes first.
Is a citation gap always bad?
No. An Untapped gap is pure opportunity: AI has already measured demand for a topic and is waiting for content to cite. Closing it is high-return because the hard part, earning the citation, is done. Only Exposed gaps, where you're cited for unsubstantiated claims, carry real downside risk.
How often should I audit my grounding queries?
Quarterly is a sensible default for most B2B teams, since citation patterns shift as your content and category presence change. The audit takes under an hour once your domain is verified in Bing Webmaster Tools. Audit more frequently if you're publishing heavily or operate in a fast-moving or trust-sensitive category.
What should I publish to close an Exposed citation gap?
Content that makes the attribute claim explicit and substantiates it with specifics: a number, a mechanism, a documented process, or a real example. Front-load the proof in the first 30% of the page and add a self-contained FAQ, since those are the sections answer engines extract and cite most often.
Related Resources
Measure what AI cites you for
How to Track AI Citations and Measure GEO Success: The 2026 Metrics Guide
Building Your Data-Source Status: How to Become the Brand LLMs Quote by Default
How to Track Your Brand's Visibility in ChatGPT and Other Top LLMs
Understand how AI retrieves and infers
Publish content that substantiates the claim
Building Citation-Worthy Content: Making Your Brand a Data Source for LLMs
Content Formats That Win With LLMs: Snippets, Q&A, Tables, Structured Outputs
