Does AirOps Actually Move AI Citations?

Every AirOps review on page one is published by a company selling a competing platform. Profound wrote one in April. AthenaHQ wrote one on August 17. Vizup, Mint and TrySight each run an "alternatives" page. The verdict was written before the testing was.

We sell no platform. We run the measurement and the work behind it for clients, so the only question we care about is whether the thing you are about to buy moves the number you are buying it for.

For AirOps that comes down to one figure, and AirOps published it themselves.

Does AirOps actually move AI citations?

Partly. AirOps is a content production engine with visibility tracking attached, so it moves the half of AI citations that comes from pages you own and publish. AirOps' own research puts up to 85% of AI search influence on content you do not own. The task meter cannot reach that 85%.

That last sentence is the whole post.

AirOps is not selling you citations. It is selling you throughput on the smaller half of them.

The billing unit is a task, and a task is a unit of output

Every other tool in this category meters prompts, engines or markets. AirOps meters work done. That single design choice decides what the subscription optimizes for, and no published review prices it.

Tasks are consumed by producing pages, not by measuring whether they got cited

AirOps' published pricing runs a free Insights tier at $0, then Solo, Pro, Pages and Enterprise on task-based billing. Solo includes 20,000 tasks a month, Pro includes 75,000, and overage on Solo is listed at $0.025 per task.

A grid that refreshes 800 pages burns tasks whether those 800 pages were the reason you were invisible or not. The meter runs on volume shipped, and volume shipped is not the variable under test.

The entry tier watches one engine, which is the tier most people buy

AirOps names five surfaces on its site: Google, ChatGPT, Claude, Gemini and Perplexity. Its pricing page lists Solo as ChatGPT insights only, with multi-engine coverage arriving on Pro and Enterprise.

Profound's competitive teardown went further in April, reporting that Solo and Pro exclude Claude, Meta AI, Grok, DeepSeek and Copilot, and that international coverage requires Enterprise. That post is a competitor's, and it is six months old, so treat it as a question for the call rather than a quote. The question is worth asking.

One persona and one region is one buyer in one country

Solo and Pro both list a single tracked persona and a single region. Enterprise is where multiple regions, personas and languages appear.

For a US-only company selling to one job title, that is a fair trade and costs nothing. For anyone selling into Europe, or to both a practitioner and an economic buyer, the entry product is instrumented for a narrower company than the one paying for it.

What a plan comparison asks:

• •How many tasks do I get?
• •What does the next tier cost?
• •How many engines are included?
• •Does it publish straight to my CMS?

What a citation audit asks:

• •What share of my category's cited domains can I publish to at all?
• •Which domains are answering for me right now instead of my site?
• •How many pages would have to change before the standing changes?
• •What happens to the meter when the answer is "none of them"?

Every published review answers the first list. The second list is the one that decides whether the renewal is worth signing.

6 things a publish-more program cannot fix

None of these are AirOps defects. Each one follows from attacking AI visibility with a content engine, which is a real lever aimed at part of the problem.

Blind spot #1: Most of the source pool is not yours to publish to

AirOps states that up to 85% of AI search influence comes from content you do not own. Our concluded CITE Index study points the same way from an independent corpus: 500 buyer prompts run nightly through ChatGPT, Gemini and Google AI Mode for 63 days, producing 90,132 answers across 10 consumer categories.

Four of the twelve most-cited domains in that corpus were brand-owned sites. The other eight were publishers, communities and marketplaces. A CMS integration reaches one third of that list on a good day.

Blind spot #2: Reddit is one site to your CMS and a hundred publications to the model

Reddit was the single most-cited source in our corpus, drawing 14,698 citations and appearing in 13.6% of all 90,132 answers. That is a larger share than any brand-owned domain achieved.

The engines did not treat reddit.com as one site either. They pulled specialist subreddits per category, the way an editor pulls trade titles. We worked through what that means for placement in where to place citations when you have no domain authority and in whether Reddit actually helps AI citations.

Blind spot #3: Category standings barely move, so shipping more pages rarely changes them

Across 63 days, the category leader flipped on only 18.7% of day pairs, and in four of ten categories the leader never changed once. The average category leader appeared in 78.2% of its category's answers. The full corpus is in the final report.

That is an incumbency problem, not a throughput problem. A platform that lets you refresh a thousand pages in an afternoon does not change how firmly the current answer is held.

Blind spot #4: The engines cite at different base rates before you publish anything

Google AI Mode cited a source in 97.4% of our answers, ChatGPT in 92.5%, and Gemini in 79.1%. A brand tracked on Gemini starts with roughly one answer in five that cites nobody at all.

Source types move the same way. Profound's analysis of 11.84 billion citations across eight models found Google AI Overviews cites a social source about once every four answers and Copilot once every 29. Community work that pays on one surface barely registers on another, and the single-engine entry tier cannot show you which one you are in.

Blind spot #5: The 40% claim behind the product has no published method

AirOps' own comparison post states that brands earning both a citation and a mention are 40% more likely to resurface in AI answers than citation-only brands. It is an interesting claim and it matches what we see in client work.

It also ships with no sample size, no date and no method. Neither does the 85% figure. Both are load-bearing for the product's argument, and both should be asked about on the call rather than quoted in your business case.

Blind spot #6: Nothing in the meter tells you when to stop

A prompt tracker runs out of prompts. A market tracker runs out of markets. A task meter has no natural stopping point, because there is always another page to refresh.

The discipline has to come from you, and the pricing model is not going to supply it. We covered where the real refresh ceiling sits in how often to update content for AI search.

A content engine measures how fast you can act. It does not measure whether acting was the constraint.

What AirOps does better than the trackers it competes with

Fit is more useful to you than a verdict, and there are four things here worth saying plainly.

The execution layer is real. Grids apply changes across hundreds or thousands of pages at once, Workflows chain repeatable steps, and Quill runs content refresh and AEO playbooks as an agent rather than a checklist. No pure-play tracker does any of that, and most teams stall on execution rather than on insight.

Page360 joins AI citations to Google Search Console and GA4 data on the same page record. That join is the reporting most teams build badly in a spreadsheet, and having it native is worth real money.

Brand Kit governance matters more than it sounds when the output volume is this high. Bulk generation without a voice constraint is how a library gets worse quickly.

And the free Insights tier is a genuine on-ramp. You can see your own citation and share-of-voice picture at $0 before any conversation about tasks. Very few competitors let you do that.

Read what the vendor publishes about its own limits. AirOps published the 85% figure, and it is the most useful sentence on their site.

Sizing an AirOps program before you sign

The diagnostic half is done. Here is the sequence we run with clients before they commit to any content platform, AirOps included.

Step 1: Split your citation gap into owned and unowned before you buy a content engine

Run your twenty highest-intent buyer prompts and log every domain cited in every answer. Sort that list into domains you can publish to and domains you have to earn.

If the unowned side dominates, and our data says it usually does, a content engine is the second purchase rather than the first. Buy against the bigger half.

Step 2: Price the task meter against your real publishing cadence

Count the pages you actually shipped or refreshed in the last two quarters, not the number in the plan. Multiply by the tasks each one would consume, then add the grid refreshes you would run because the tool makes them cheap.

That third number is the one that breaks budgets. At $0.025 per task on Solo overage, an enthusiastic first month is easy to underestimate.

Step 3: Run your ten highest-intent prompts by hand across all five engines

Before you pay, open Google, ChatGPT, Claude, Gemini and Perplexity, and run the same ten shortlist-stage prompts. Record where you appear, which competitors appear instead, and every domain cited.

Two hours of this tells you whether the single-engine entry tier can see your buyers. No dashboard replaces that first pass, and our AI visibility platform buyer's guide covers the six jobs to score any platform against afterwards.

Step 4: Set the movement threshold before the first refresh ships

Decide now what size of change you will act on, and write it into the reporting template. Our data suggests a single day's leader change is not that threshold in most categories.

Without one, a platform built for speed will generate a weekly meeting about noise, and the meter will bill you for the response.

Step 5: Fund the off-page half from the same budget line

Whatever you spend on production, hold back a matching share for the work that puts you in other people's pages: earned mentions, comparison placements, community presence, review-site position.

Split the budget when you sign, not after the first flat quarter. That split is the entire reason a managed GEO agency sits next to tooling rather than inside it.

When AirOps is the right buy, and when it is not

Situation	Verdict	Why
Large existing content library, thin or stale, one owner who can ship	Strong fit	Grids and Quill are the fastest route from a refresh list to shipped pages, and this is the case where owned content genuinely is the constraint.
You already have a tracker and stall at the "now what" step	Strong fit	The execution layer is what pure-play trackers deliberately do not build, and Page360 joins the citation data to GSC and GA4 natively.
Your cited-source pool is dominated by communities and publishers	Wrong first purchase	The meter cannot reach those domains. Fund earned placement first and buy the content engine once the owned gap is the binding one.
You sell to developers or enterprise Microsoft buyers	Check the tier before comparing	Copilot and Grok are outside the named engine list, and the entry tier is ChatGPT only.
Multi-region or multi-persona B2B	Price Enterprise, not Solo	One persona and one region on the self-serve tiers means the instrument is narrower than the business.
You want to know whether you are visible at all	Start free	The $0 Insights tier answers that question without a task budget or a contract.

The tools in this bracket each constrain a different thing. Peec AI caps engine coverage at three of six on self-serve. Profound sells sampling depth and the question is how many answers produced each score. Otterly's constraint is geography. AirOps is the only one whose constraint is not measurement at all. It is scope: the meter reaches your pages and stops there. The rest of the field is mapped in our survey of GEO tools for 2026.

FAQ

What is AirOps?

AirOps is a growth platform for AI search that combines visibility tracking with content production. Its named modules are Insights for citation and share-of-voice dashboards, Action for content execution, Grids for bulk operations across large page sets, Workflows for repeatable step chains, Playbooks for guardrailed agent strategy, Brand Kit for voice governance, and Quill, an agent that drafts briefs and runs content refresh and AEO playbooks. It monitors Google, ChatGPT, Claude, Gemini and Perplexity.

How much does AirOps cost?

AirOps publishes a free Insights tier at $0 and then bills on tasks rather than seats or prompts. Solo includes 20,000 tasks a month with overage listed at $0.025 per task, Pro includes 75,000 tasks, and Pages and Enterprise are custom quoted. Solo covers ChatGPT insights only, with multi-engine coverage on Pro and above. Solo and Pro each track one persona and one region, while multiple regions, personas and languages sit on Enterprise. Published figures change often, so confirm on a call before they enter a spreadsheet.

AirOps vs Profound: which should you buy?

They solve different halves. Profound is a measurement instrument built on real user prompt volume across up to nine engines, and its risk is thin sampling per prompt on self-serve tiers. AirOps is a production engine with tracking attached, and its risk is that the meter only reaches pages you own. Buy Profound if you need a defensible number. Buy AirOps if you already know the number and cannot ship fast enough to change it. Most teams that buy AirOps first end up buying measurement second.

What are the best AirOps alternatives?

The names that come up most are Profound, Peec AI, Scrunch AI, Otterly, AthenaHQ, Evertune, Semrush AI Visibility and Ahrefs Brand Radar. Almost all of them are trackers rather than content engines, so the honest comparison is not feature for feature. Decide first whether your constraint is knowing or doing, then compare inside that group. If it is doing, the real alternatives are a content team and an off-page program, not another dashboard.

Which AI engines does AirOps track?

AirOps names five: Google, ChatGPT, Claude, Gemini and Perplexity. Microsoft Copilot, Grok, Meta AI and DeepSeek are outside that list. Engine coverage is also tier-gated, with the Solo plan listed as ChatGPT insights only. Check which assistants your buyers actually use first, because engines differ sharply in what they cite: our study found Google AI Mode cited a source in 97.4% of answers, ChatGPT in 92.5%, and Gemini in 79.1%.

The bottom line

AirOps built the best execution layer in this category and priced it in a unit that quietly assumes the answer. Tasks measure how much you publish. Citations are decided mostly on pages you will never publish to, and AirOps is the vendor that told us so.

Do the split in step 1 before you sign anything. If your cited-source pool is mostly communities, publishers and review sites, buy the earned half first and come back for the content engine when owned pages are genuinely what is holding you back. If your library is large, stale and yours, this is the fastest tool on the market for fixing it.

Then go do the work the meter cannot bill for. Nothing in the software earns the third-party mention, wins the comparison-page slot, or gets you quoted in the subreddit your buyers actually read. An AI visibility audit will show you which half your gap sits in before you commit to a year of anything.