AirOps Review: What I found testing the platform hands-on

I built a keyword-research workflow in AirOps, published it, and ran it for real. I also reviewed the Power Steps library, set up a Brand Kit, and ran the prebuilt content-automation campaign.

The visual builder made multi-step automation straightforward, and the workflow ran to completion exactly as configured. It still returned keywords I could not use, and that gap between execution and judgment is the thing to understand before you pay for AirOps.

This review covers the workflow builder, Grids, Brand Kits, and AI-search reporting, plus what the plans include and what AirOps leaves off its pricing page.

By the end you'll know whether AirOps fits your team, how much setup it expects from you, and which parts to test during the 14-day trial before you commit.

AirOps review TL;DR

In my testing, AirOps worked as a flexible no-code platform for turning an existing SEO process into repeatable workflows.

What stands out:

• The workflow builder chains AI steps, data sources, and prebuilt Power Steps into a pipeline you can rerun on demand.

• Grids run one workflow across many rows at once, like a spreadsheet where every row is a content job.

• AI-search visibility monitoring is native, so prompt and engine tracking sits beside the content work instead of in a separate tool.

The tradeoffs:

• The keyword workflow I built ran to completion and still returned weak keywords, so configuration and output review both stayed with me.

• The onboarding I tested did not provide a guided SEO methodology, so the strategy behind the workflow remained my responsibility.

• Cost tracks qualifying task usage. Solo includes 20,000 tasks and Pro includes 75,000, but AirOps does not publish their subscription prices.

Best fit: teams with a proven SEO process and a reason to run it at volume. Poor fit: teams that still need help deciding what to create and how to evaluate it.

How this review was put together

I used AirOps directly for the claims I could test, using RankUp's site and brand context as the working example.

Claims about plans, allowances, integrations, and AI visibility come from AirOps' published product information. I identify anything I did not validate firsthand.

Tested firsthand:

• The full onboarding sequence, including the competitor list AirOps generated and the AI Search report card it produced for my domain

• The drag-and-drop workflow builder

• A keyword-research workflow I built with Copilot, then configured, tested, published, and ran for real, including inspecting the keywords it returned

• The Power Steps library, including SERP and topical analysis steps

• The prebuilt content-automation campaign, including its outline stage and the article it wrote

• The Brand Kit, which I set up after the initial content work because onboarding did not direct me there first

• The AI-visibility side: the generated prompt list, the Visibility analytics views, the Opportunities module, and having Quill generate campaign opportunities

Not tested:

• Support responsiveness, since I never contacted the team

• CRM integrations such as HubSpot and Salesforce

• Grids; I saw them offered as a workflow step but never built or ran one

• Independent validation of AI-visibility accuracy

• Subscription pricing and billing mechanics, which come from AirOps' published pricing page rather than a purchased plan

The published performance of anything AirOps generated, since I never posted it

What is AirOps now?

AirOps is a no-code platform for AI search, SEO, and content operations. Teams can build custom workflows, run content work at scale, refresh pages, and monitor AI-search visibility.

AI-search reporting sits alongside the workflow and content features rather than in a separate product. The plan comparison sets tracked prompts, monitored pages, answer engines, and opportunity frequency per plan.

• Page360: Combines page-level GA4, Google Search Console, and AI-search signals in one view.

• Sentiment tracking: tracks sentiment in AI answers

• Query fan-outs: expands a query into related prompts for analysis

The product now covers workflow automation, content creation and refresh, AI-search monitoring, social integrations on relevant plans, and custom agent builds for Enterprise.

How does an AirOps workflow work?

I built one workflow end to end: a keyword-research sequence that I assembled, configured, tested, published, and ran for real. What follows is that run, including what it gave me back.

My honest starting point was not knowing what to build. AirOps opens on an empty canvas with an Input Settings node, an Output node, and a Copilot panel, so I asked Quill to build something.

Once I had a starter flow, I picked a real job for it: keyword research. Each step handles one defined task, and you wire each output into the next step's input.

Copilot asked what I wanted before it added anything: which data sources the workflow should read, seed keywords or domains, and what shape the output should take. I answered topic clusters.

Power Steps cover the common jobs so you don't build that logic yourself:

• SERP analysis and topical analysis

• Outline generation and article drafting

• Lookups against connected data providers

Scrolling that library further took me past SERP and topical analysis into refresh suggestions and keyword analysis, including Cluster Keywords and Identify Target Keywords. Assembling them is the easy part.

Copilot was genuinely useful for finding and placing steps. It was less useful for judging them: when I got stuck on what belonged in the second input field, Quill gave me an answer, and I had no way to tell whether it was right.

I took its recommendation, published the workflow, and ran it for real on the seed keyword "seo ai agents". The run completed all four steps, finishing on Group Keywords into Clusters.

The sequence worked. The keywords did not.

One cluster repeated "keyword find" eight times. Others held generic fragments like "relevant keywords" and "your keywords", plus a truncated "keyword re".

That is the part I would want to know before buying. AirOps executed my workflow exactly as configured, and configuring it well enough to produce usable keywords stayed entirely with me.

Once the sequence works on one input, a Grid runs it across many. Rows are content items, columns are the fields each step reads or writes.

Runs can fire on a trigger instead of a click, so a status change in a connected source starts the sequence and sends structured output toward your CMS.

Two layers shape what comes out the other end:

• Brand Kit stores positioning, tone, personas, competitor notes, writing rules, and sample content that steps can pull from

• Knowledge Base connects docs, past articles, and product pages so generation draws on your own material. How many Brand Kits and Knowledge Bases you get varies by plan

Brand Kit context is inherited by relevant workflow steps, so positioning and writing rules do not need to be added step by step.

That layer went deeper than I expected. Scrolling through mine, I found:

• Foundations, plus brand voice and tone

• Audiences and regions

• Content types, product lines, and visual guidelines

Each is configurable in detail, which told me something about the product. AirOps is not only a blog creator at its core.

Review remains explicit. You add a pause where someone should check output, and fact-checking and final formatting stay with a person.

In the workflow I tested, three points were worth stopping at:

• After outline generation, before any draft gets written

• After the draft, before optimization

• Before the CMS publish step

The same Flow controls panel decides what happens when a step fails, so one broken step does not cost you the whole run.

What did the prebuilt content campaign produce?

Building my own workflow meant choosing every step myself. The prebuilt content-automation campaign is the other route in, so I ran that too.

It arrived staged as a playbook called AEO Content Creation, with research sequenced ahead of any writing:

• Identify AI Citations and Google Results ran a Query Versus Report across the SERP and the AI answers for the target query

• Topic Research and Information Gain pulled positioning and audience straight out of the Brand Kit

Nothing needed wiring. The steps came in order, which is the opposite of the empty canvas I started on.

The outline stage was where it felt strongest. It produced a full brief for an article on tracking competitor SERP movements.

The brief carried target keywords, the audience it was written for, and a live to-do list of the steps still running behind it.

Then the campaign wrote the article. I never published it, so I have no ranking or traffic data on it.

Reading it was another matter. Set against what I have seen work in SEO content, it missed the fundamentals, and facts, commercial framing, and formatting all still needed a person.

That is the honest shape of the guided path. It removed the setup work I hit building my own workflow, and it did not remove the judgment about whether the output was worth publishing.

What does AirOps' AI-search reporting show?

Onboarding produced the first output I could judge: an AI Search report card for my domain. It led with "Your competitors are outperforming you in AI Search", put my brand rank at 6, and showed a 0.0% mention rate and a 0.0% citation rate.

The rankings table under that headline complicated it. Surfer SEO sat at 13% and Frase at 4%, while WriteSonic, Scalenut, Jasper, and my own domain all sat at 0%.

Four of six brands tied at zero is thin ground for the urgency in the header. Check the underlying numbers before treating a report card like this as a mandate.

The prompt set is where the report starts

AirOps builds the tracked prompt list from the brand, audience, site content, and competitor details it collects, then measures how often you appear across it.

Mine came back grouped by topic, with a Query Fanouts count on every row, a United States region filter, and five platforms selected.

Reading that list is where my expectations and the product diverged. I came in for AI search, and got traditional rank-tracking questions: "How do I track daily keyword rankings?", "What is the best rank tracking software?", "Compare leading rank trackers for agency use."

Those are category queries about SEO tooling. They are not the questions I would have picked to represent how buyers ask for what I do, and every number in the report inherits that choice.

What the analytics show once the prompts run

The Visibility view reports mention rate, share of voice, and average position, each benchmarked against the competitor set on the account.

Coverage breaks out by platform, so you see where a mention came from rather than only that it happened. Mention Rate by Platform spanned ChatGPT, Gemini, Perplexity, Google AI Mode, and Google AI Overview, filterable by region and prompt type.

How much of this you get is set by the published plan comparison: tracked prompts, monitored pages, answer engines, opportunity frequency, regions, personas, and data export all vary by plan.

Opportunities, and the number that was missing

The Opportunities view sorts suggestions into creation, refresh, outreach, and community groups, led by Popular Prompt Gaps: content aimed at popular queries with high relative volume scores.

I could not see any of them. Prompt Volume was not included in my plan, so the module that ranks opportunities by demand sat behind an upgrade prompt.

That is the gap in the whole exercise. Suggested topics arrived with no search volume attached and no first-party demand evidence behind them, and the one metric that would have supplied it was the paywalled one.

Handing opportunities to Quill

I also had Quill generate opportunities as a campaign, scoped to zero-citation topics: queries where my domain had no mentions and no citations while other domains were being cited.

It returned three, each framed as a content hub: Competitor Keyword Analysis, SEO Search Visibility, and Rank Tracking & Monitoring. Beside each sat the domains currently holding those citations, including Semrush, Ahrefs, and Search Engine Land.

I selected all three and the configuration panel was ready to turn them into pages. The speed was real, and so was the fact that nothing in that flow told me whether anyone searches for those hubs.

What AirOps would not tell me

The measurement itself is where I stopped trusting the output. AirOps did not explain the sources behind its prompts, the logic that selected them, or how the mention and citation rates are calculated.

Without that, a 0.0% mention rate is a figure I cannot audit. Two things need validating before a report like this becomes a content plan:

• Prompt relevance - Whether the generated prompts match questions your buyers actually ask, rather than the category queries the generator reaches for first.

• Opportunity relevance - Whether suggested topics have real demand behind them, which on my plan meant sourcing search volume somewhere else.

What does using AirOps realistically require?

AirOps is ready to evaluate when you have an SEO process, someone who can design the workflow, and an internal reviewer who understands the brand and search goals.

The platform supplies configurable execution machinery. The tested onboarding did not decide which topics deserved attention or how the workflow should judge them.

Three things need to exist before the platform pays off:

• A content process that already works at small scale

• An internal reviewer who understands the brand and SEO strategy well enough to approve or reject the output

• Comfort designing multi-step workflows, including inputs, research steps, drafting logic, and approval points

The setup work is concrete:

• Configure and test the workflow on a representative input before scaling it.

For publishing, map the workflow fields to your CMS, test the connection, and monitor failed sends. The integration list varies by plan.

What does AirOps cost, and what are you really paying for?

AirOps pricing combines a plan's task allowance with incremental usage. Subscription prices for Solo and Pro are not publicly disclosed, so task math alone cannot predict the full bill.

Current published plan structure:

The visible pricing page prices three plans: Solo, Pro, and Enterprise. Older FAQ copy still refers to Scale and Agency accounts, so confirm which structure applies to your account.

A task is a qualifying workflow action that completes successfully. Not every step counts, and usage varies with the step type and data processed.

A Grid repeats those actions across its rows. More model queries, data sources, and qualifying steps can increase the task use of each run.

Model task use with one representative workflow before committing. Solo overages are published at $0.025 per additional task; higher-plan overage rates require confirmation.

Confirm these terms in writing before purchase:

• Subscription price: the billing rate and term for your account.

• Task allowance: included tasks and the rules for overages.

• Task definition: which workflow actions consume a task.

• Coverage: the engines and markets included in your account. Solo publishes ChatGPT-only Insights, while Pro adds Google, Perplexity, and Google AI Studio.

Needing to ask for a subscription price is common in this category. RankUp publishes its entry point instead, a $25 paid trial that includes $50 in agent credits.

Ongoing plans get built in-app after that trial, so neither company publishes a standing subscription price for the work that follows.

Where does AirOps create friction in real use?

The friction I observed appeared during onboarding, workflow setup, and topic validation. Untested scale and integration concerns belong on your trial checklist, not in the firsthand findings.

Onboarding

• Onboarding generated a competitor list and an AI Search report card, then dropped me into the platform. From there I had to work out where to begin, because it assumes you already know what you want to build.

• The prebuilt workflows were starting points, not ready-to-run templates. I needed configuration and test runs before they produced usable output.

• Nothing prompted me to build the Brand Kit before generating content or explained how that setup would affect the output.

Execution

The workflow ran correctly and still returned weak keywords, including one cluster that repeated the same term eight times. Budget review time for the output, not just for building the sequence.

• Before purchase, test any workflow that depends on several integrations. Confirm field mapping, error handling, and what happens when a connected service fails.

• I did not benchmark large datasets. Run a production-like row count during the trial if bulk Grid execution is central to your use case.

Cost and coverage

• Test task use with a representative workflow before rollout. More qualifying actions across more rows can consume the allowance faster.

• Price transparency is another constraint. You need written terms to compare the subscription cost and included task allowance.

• Coverage is not one setting. Confirm the models and markets the account will track before treating its visibility data as a complete view.

Data trust

• AirOps did not explain the sources behind its generated prompts, or how it calculates mention and citation rates. Check whether the configured prompts match questions your buyers actually ask before acting on the numbers.

These tradeoffs are manageable when your team can configure and validate the workflow. They matter more when you need the platform to supply the strategy before execution begins.

Who is AirOps best for, and who should skip it?

AirOps suits experienced SEO and content-operations teams that need configurable execution at volume. Teams still deciding what to create and how to judge it will need strategy outside the platform.

The content workflow I tested did not ask how the product should be positioned or how commercial messaging should appear. Those details needed explicit brief inputs before publication.

Good fit if:

• SEO manager: You run refreshes at volume and can evaluate the inputs before the workflow runs.

• Content-operations team: You need repeatable execution across a backlog and can validate the workflow before it runs.

• Systems-minded operator: You already have a workflow worth encoding.

• Editorial reviewer: Someone in-house reviews the output before it ships.

Skip it if:

• You are new to SEO and need the strategy decided for you.

• You are a solo operator without a content strategy in place.

• You expect the tested content workflow to produce publish-ready output without fact, brand, and formatting review.

Should you pick RankUp instead?

AirOps and RankUp solve different starting problems. The deciding question is whether your bottleneck is workflow capacity or the process itself.

RankUp begins with market and SERP research. Magnus identifies competitors and target keywords, then groups primary and secondary terms into clusters.

Those clusters become a topical map with priority scoring, so publishing order comes from search data rather than whoever argued hardest in the planning meeting.

The resulting plan groups those targets into monthly batches, with search volume and a priority score on every keyword. What to publish next is already decided.

That is where the two approaches part ways. An execution-first platform waits for you to bring those planning decisions; RankUp arrives with them already made.

Execution runs on that same plan, not on a separate brief you write up afterwards. The agent team picks up each target in the priority order the plan set.

The Content Blueprint turns the chosen topic, research, and writing guidance into an article-ready structure. Cedric drafts the article from that blueprint using your established brand guidance, section by section, with research and self-review in the same run.

What each run learns about your positioning and editorial decisions goes back into the knowledge base. Later articles and updates start from that instead of rebuilding the context every time.

RankUp also works on content already published. Google Search Console data supports keyword mapping and recurring strategy audits, so the plan can respond to actual search performance.

Lyra identifies pages that need attention, explains the issue, and routes the writing work to Cedric. The resulting changes stay inside the same review process as new content.

Choose AirOps when you already have a proven process and need flexible automation. Choose RankUp when the missing piece is the strategy, writing, and ongoing improvement built around your site.

The $25 trial builds that groundwork from your site, covering brand voice, knowledge base, and topical map, plus $50 in agent credits for the first article.

If strategy, writing, and continous improvement are the parts you need handled, start trial for $25.

More questions about AirOps

Does AirOps offer a free trial or free plan?

AirOps offers a 14-day free trial with access to Scale-plan features, and no payment details are required until it ends. The trial closes when the trial tasks run out, the 14 days pass, or you upgrade early.

Solo and Pro both start through a Start for Free button, but neither publishes a subscription price. There is no permanent free plan for live production work.

AirOps also documents 50,000 workflow-testing credits for Scale, Enterprise, and 14-day trial accounts. Because the visible plan cards now show Solo, Pro, and Enterprise, confirm eligibility where that legacy wording conflicts.

How does AirOps billing work?

AirOps bills against a plan's task allowance, then charges incremental usage after the allowance runs out. Solo includes 20,000 tasks, Pro includes 75,000, and Enterprise uses a custom limit.

Allowances reset at the start of each calendar month. Solo publishes a $0.025 per-task overage rate, while higher-plan rates and subscription prices require confirmation.

Track usage under Settings, then Billing, and check workflow-level consumption through Monitoring. Run one representative workflow first because task use depends on qualifying actions, rows, model calls, and data lookups.

What happens if you hit usage limits?

When included tasks run out, AirOps says usage can continue and incremental charges apply. Solo publishes a $0.025 rate per additional task; higher-plan rates are not public.

I found no official support for a hard-stop setting, so monitor usage and confirm account-specific controls before a large Grid run. Testing credits are separate and documented only for Scale, Enterprise, and 14-day trial accounts.