GetXEO vs AirOps

AirOps is the strongest execution engine in this comparison. It tracks visibility, surfaces the work, and ships the content, which is more of the chain than most platforms attempt. Two links are still open. What gets published is a queue of pages rather than a connected mesh, and nothing audits whether engines can read your site at all. That is where this comparison lives.

Arjun Shenoy

Co-Founder and CPO at GetXEO

Evaluates AI visibility tooling as part of the product role, and has run this category comparison across ten platforms.

31 August 2026 · 10 min read · 2,259 words · 9 cited sources

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This is a closer table than the rest of the category. AirOps has a real answer on four of the five rows, and on the last one it covers AI engines and Google together. The argument narrows to two things: what shape the output takes, and whether anybody checks that engines can read your site.

AirOps describes itself as a growth platform for AI search, and it runs as a loop. Insights tracks AI visibility across answer engines and Google together, with citation tracking, competitor intelligence and share of voice.

Those signals feed an opportunity queue. Sacra, which profiles the company independently, groups that queue into four kinds of work: create content on topics the brand does not cover, refresh pages losing visibility, run outreach to third party publishers, and take part in community spaces where answers get shaped.

Execution is the part AirOps is known for. Selected work moves into a grid where hundreds of URLs are processed in bulk, and its agent runs the campaign, drafts, routes for human approval and publishes back into a connected content system. AirOps distinguishes a workflow, which runs the same way every time, from a playbook, which lets the agent reason inside guardrails you set.

The published plans track one hundred prompts and pages on the entry tier and two hundred and fifty on the main paid tier, with custom limits above that. Every tier refreshes daily. The entry tier reads ChatGPT only, and the main paid tier adds Google, Perplexity and Google AI Studio.

GetXEO reads the brand first. It crawls the domain, classifies every URL and builds a brand profile every downstream writer reads. It then derives the question universe and curates twenty five queries for each of the three pillars, mapped back to the keyword or question each came from.

Those queries produce one composite score. The dashboard carries AEO, GEO and SEO sub scores, model citation rates and the change since the last scan. AEO polls answer engines, GEO covers AI summarized search and weights paragraph position, and SEO tracks the classic Google result page alongside AI Overviews.

Then three technical audits, one per pillar. The GEO audit checks entity declarations, Organization schema, semantic HTML and knowledge graph signals, and they roll into a composite audit score with a page by page heat map and a prioritized action list. Fixes arrive as a per page checklist a developer can work through.

The output is fifty blogs a month on the single workspace plan, produced as one interconnected mesh and sequenced on a twelve week calendar that also carries refreshes and technical fixes.

This is the difference that decides the comparison, and it is easy to miss because both platforms produce content.

A queue is a list of good decisions taken one at a time. Each item is chosen because a signal said it was the best next move, which is a sound way to work and it is why velocity platforms show results quickly. What a queue does not carry is a relationship between the pages it produces. Page forty does not know page nine exists.

A mesh starts from the opposite end. The set is designed first, so every post is written to support the others and the internal structure is part of the specification rather than a cleanup task afterward. Pages carrying figures, citations and quotes get named far more often, and a connected set gives each page more of those to point at.

Sequence matters for the same reason. A calendar that ships foundational pages before the comparison pages that depend on them compounds. A queue reordered by whichever signal is loudest this week does not, and the AI visibility it earns arrives one page at a time.

• Execution that actually ships. Grids process hundreds of URLs at once, agents run campaigns with approval gates, and the work publishes straight back into a connected content system. AirOps puts median time to first shipped outcome at fourteen days
• Earned mentions are a first class surface. AirOps puts eighty five percent of AI visibility, in SaaS top of funnel, on content a brand does not own, and it finds the influential publishers, runs the outreach and measures the lift. GetXEO publishes nothing here at all
• It fits the stack you already run. Forty or more AI models, ten or more data providers and seven named content system integrations, plus project tools, and an MCP server so the whole thing runs inside Claude or Cursor
• A program, not just a product. A university with certification, live cohort trainings that step up by plan, a research team, and an embedded content engineer among the custom solutions on offer

The company is scaling behind it. Sacra puts AirOps at roughly thirteen million dollars in annual recurring revenue for 2025 and about one hundred and eighteen million dollars raised in total, including a round in May 2026 aimed at its agent layer.

On three operational rows AirOps publishes a figure and GetXEO does not.

Customizable means GetXEO publishes no fixed figure and sets it per workspace, so ask rather than assume. Not published means the capability does not appear in the feature catalog at all, which is the larger gap and worth naming as one. Two of these three rows are real advantages for AirOps.

• The whole chain in one place. Question set, mesh design, extraction craft, technical audit and measurement together, so no step falls to a second product or to nobody in particular
• One composite across three layers. AEO, GEO and SEO roll into a single figure with model citation rates and the change since the last scan, rather than three views a marketer reconciles by hand
• Delivery is verified rather than assumed. Three audits produce one score with a page by page heat map of issues and a fix list a developer can work through without a marketer translating
• Output that connects. A designed mesh on a sequenced calendar, so authority accumulates across the whole set instead of starting again at every URL

One limit is worth knowing before committing. GetXEO does nothing about earned mentions on sites you do not own.

This is a narrower edge than the rest of the category, and stating it narrowly is the point. AirOps measures well and executes better than anyone here. Two links in the chain are still open, and both of them decide whether the execution pays off.

The delivery. AirOps reports bot traffic through CDN and analytics connections, which tells you which crawlers arrived. It does not tell you whether the page they arrived at declares its entities, carries Organization schema, or renders without a script. Each AI operator crawls under its own user agent and OpenAI says a robots change is picked up in about a day, so this layer is both the cheapest to fix and the one nobody owns.

The structure. A queue of well chosen pages and a designed mesh are not the same artifact, and only one of them compounds. This is the difference that shows up in month six rather than month one, by which point the AI visibility gap between the two is structural rather than temporary.

And the questions underneath both. Tracked prompts tell you where you stand on questions somebody already thought to track. Modeling how a buyer behaves is meant to surface the situations they describe instead. We hold that as a design judgment rather than a measured finding. What the research settles is the scale: most B2B buyers now use AI in the buying process and more than half compare vendors with it.

It matters more than it looks, because almost all buyers who research with AI check what it tells them before trusting it. The cited page is the one they land on, so it has to survive being read by a person straight after being lifted by a model.

GetXEO fits a marketing team that already publishes often and cannot show it in the answers. The second sign is that nobody owns the technical layer. It lists at one thousand dollars a month, half that with the discount on the page. AirOps has two self serve tiers that start at nothing, and it fits a team with production capacity that needs more of it, or one whose visibility gap sits on sites it does not own.

All were published or last updated within the past twelve months. A competitor page appears only as a record of that vendor’s own published terms.

1. Machine Relations, AI search citation factors research
2. Machine Relations, how B2B buyers research vendors with AI
3. MarketScale, on the TrustRadius finding that buyers fact check AI
4. OpenAI, the crawler and user agent documentation
5. Cloudflare, the AI crawler bot reference naming each operator, category and user agent
6. Sacra, the independent research profile of AirOps, revenue, product layers and funding
7. AirOps, the published platform page, read 27 August 2026
8. AirOps, the published pricing page and plan comparison, read 27 August 2026
9. AirOps, the published offsite solution page, read 27 August 2026

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