Reverse AI Search

AI keyword gap: queries your competitor wins and you don't

The answer-layer version of a keyword gap analysis. Compare two domains' citation footprints and the difference is your highest-leverage worklist.

Updated May 20267 min read
The short answer

Your AI keyword gap is the set of buyer queries that AI assistants — ChatGPT, Gemini or Grok — cite a competitor on but do not cite you on. It is the answer-layer equivalent of a traditional keyword gap analysis: instead of comparing two domains’ organic rankings, you compare their AI citation footprints and isolate the queries only the competitor wins. You find it with reverse AI search — run a reverse domain lookup on your domain and on a rival’s, then subtract. The remainder is a ranked worklist of real questions where the models already trust someone else to answer for your category. Each gap query is a concrete, high-intent opportunity: the demand is proven (a model answers it), the source slot exists (a competitor fills it), and your job is to become the better answer.

What is an AI keyword gap?

In classic SEO, a keyword gap analysis lists the terms your competitor ranks for that you don’t. The AI keyword gap moves the same idea to the answer layer. Rather than rankings, it compares citations: the questions where ChatGPT, Gemini or Grok name a competitor as a source or recommendation and leave you out entirely. Those queries are the cleanest possible targets — you don’t have to guess whether demand exists, because a model is already answering the question and choosing someone else.

How do I find my AI keyword gap?

It is a three-step subtraction:

  1. Map your footprint. Run a reverse domain lookup on your own domain to get every query you are cited on. The free Domain Check does this across all three models; the concept is your AI citation footprint.
  2. Map the competitor’s footprint. Run the same check on a rival domain.
  3. Subtract. Queries in their list that are absent from yours are the gap. Queries in both are your overlap — useful for a different reason (those are the head-to-head battles).

The mechanic that makes this possible — the inverted index queried by domain — is explained in reverse domain lookup for AI citations.

What does a gap analysis actually look like?

The output is a side-by-side table: for each query, whether you are cited, whether the competitor is cited, and whether that leaves a gap. The sample below is an illustrative example (not live index data) showing the shape of the comparison.

Illustrative example — AI keyword gap, you vs one competitor (not live index data)
QueryYou cited?Competitor cited?Gap?
best CRM for small agenciesNoYesGap — close this
CRM with client portalYesYesOverlap — defend
how to track leads without a CRMNoYesGap — close this
your-brand vs competitor pricingYesNoYou win outright
free CRM for freelancersNoYesGap — close this

The rows marked “Gap” are your worklist; the “Overlap” rows are your competitor query overlap. As a worked example of the counting behind it: if your footprint covers 120 cited queries and a rival’s covers 180, with 70 shared, then your overlap is 70 and your gap against them is 110 (180 − 70). Those 110 queries — sorted by intent — are where you start.

How do I prioritise the gap?

A raw gap list can be long. Sort it so you work the highest-value rows first:

  • By intent. Commercial and transactional questions (“best X for Y”, “X pricing”, “X alternatives”) move revenue faster than purely informational ones. Win those first.
  • By model spread. A query where all three models cite the competitor is a stronger consensus — and a bigger miss — than one where only Grok names them.
  • By proximity to what you already win. Gaps adjacent to topics you’re already cited on are easier to close than gaps in a new area, because the models already associate your domain with the neighbourhood.

How do I actually close a gap query?

Closing a gap is rarely “write one more blog post.” The competitor is cited because of something specific, and your fix should target it:

  • Read the actual answer. See what the model says and why it names them — the phrasing usually reveals the source type and the angle it rewards.
  • Match and beat the extractability. Give the question a cleaner, self-contained answer block than the competitor’s page has.
  • Address the trust signals. If the competitor wins on third-party mentions, reviews or community presence, those are part of the gap too. Walk through the full diagnosis in why ChatGPT recommends your competitor and not you.

Why the AI keyword gap is the most actionable reverse-search output

A visibility score tells you that you’re behind. The AI keyword gap tells you exactly where, against whom, and on which questions — which is the difference between a status report and a plan. It is also the natural foot-in-the-door audit for agencies: run a prospect’s domain against their main rival before a pitch and walk in with their gap already mapped. Start by running the free Domain Check on your domain and a competitor’s, then subtract.

Frequently asked questions

How is an AI keyword gap different from a Google keyword gap?
A Google keyword gap compares organic rankings; an AI keyword gap compares citations — the questions where ChatGPT, Gemini or Grok name a competitor as a source or recommendation and leave you out. The demand is already proven because a model is answering the question and choosing someone else.
How many gap queries is normal?
There is no universal benchmark, and we will not invent one. Gap size depends on category breadth and how broad each domain’s content is, so the meaningful number is relative — how many queries this specific rival wins that you don’t — not an absolute target.
Which gap queries should I close first?
Sort by intent (commercial and transactional first), then by model spread (a query all three models cite the rival on is a bigger miss), then by proximity to topics you already win. Closing adjacent gaps is easier than entering a new area.
Does closing a gap just mean writing another blog post?
Rarely. Read the actual answer to see why the rival is cited, then match and beat its extractability and address the trust signals behind it. The full diagnosis is in why ChatGPT recommends your competitor and not you.