After a couple of years of AI search reshaping how people find information, the enterprise question has moved on. It's no longer "should we invest in AI visibility?" It's "which platform do we run our AEO program on?" Two names that come up often in that conversation are Profound and AirOps.
They approach it from opposite ends, and that origin still defines what each does best. AirOps started as a content-automation platform and added AI visibility features on top, and, over the past year, an increasingly hands-on services layer. Profound was built from the ground up as an agentic marketing platform for the AI era, purpose-built for Answer Engine Optimization (AEO). It’s known for pairing best-in-class visibility data with an agentic layer that acts on it.
The comparison got more interesting in 2026, when AirOps launched Quill, its own background agent, and rebuilt Brand Kit as a governed record of the brand. Both platforms now watch the data and propose work rather than waiting to be triggered, but in different ways: Quill proposes the work, while Profound’s AI Marketer (Aim), also a background agent, orchestrates it, planning each project and building the Agents that carry it out.
This article breaks down where each one excels, so you can decide on the best fit for your team.
Profound vs. AirOps: Data foundation and prompt intelligence
The success of an AEO strategy hinges on the quality of the data underneath it. You can have the cleanest monitoring dashboard on the market, but if the prompt intelligence feeding it is imprecise, you're building a content strategy on assumptions. This is the biggest divergence point between the two platforms, and, increasingly, the reason the rest of the comparison breaks the way it does.
AirOps: Estimated demand, limited coverage
Pros:
- Pro plans track 7+ answer engines, including ChatGPT, Google Gemini, Google AI Mode, Google AI Overviews, and Perplexity; Claude is on by default only for Enterprise
- Sentiment now breaks down by theme ("customer support," "pricing flexibility," "product reliability"), not just brand-level positive/negative
- Citation tracking includes a "Top 10 Cited URLs" view with per-page citation share
Cons:
- No measured prompt volume. AirOps rates prompts in relative tiers that blend AI search data with traditional search volume, rather than counting how often real users ask them
- Prompt volume is only a relative tier, so you can see which prompts cite a page but not how much demand sits behind them
- The free Solo plan covers ChatGPT only
AirOps' dashboard has gotten more specific. Sentiment used to be brand-level only; now it breaks down by theme, filterable by platform, region, and persona. Citation tracking added a "Top 10 Cited URLs" view showing citation share per page instead of a single aggregate number. Both are real gains, and worth acknowledging.
Neither addresses the underlying issue. AirOps still has no measured prompt volume; its tiers blend AI search data with traditional search volume, a relative signal rather than a record of how often people ask. Everything downstream of that number, from which gaps the platform flags to which topics look worth pursuing, inherits the same uncertainty. And because volume is only a relative tier, AirOps can tell you which questions cite a page but not how much demand sits behind them.
Coverage compounds the issue. Pro plans track 7+ answer engines. For a discipline where the whole point is seeing what real users see, that’s a foundation built on inference.
Profound: 2B+ real user prompts
Pros:
- 2B+ real user prompts, licensed from double-opt-in consumer panels across ChatGPT, Gemini, Claude, and Perplexity
- Prompt Volumes breaks demand down by intent, age, income, country, and platform
- Up to nine answer engines on Enterprise, with 150+ regions and 30+ languages
- Prompts run daily through front-end browsers, mirroring exactly what real users see in their local context
Cons:
- The free trial covers 50 prompts on three engines for seven days; full engine coverage and Prompt Volumes come with an Enterprise plan
- The depth of the dataset means there's a learning curve in knowing which dimensions (intent, region, demographic) to prioritize first
The foundation of Profound's advantage is 2B+ real user prompts: actual conversations people are having with answer engines, refreshed weekly and modeled to correct for demographic and geographic bias, not topic-level proxies. Prompt Volumes surfaces the specific phrasing, adjacent topics, and intent patterns that traditional keyword research never captures, then lets you break the same prompt down by intent, age, income, country, and platform, so a single query can tell a different strategic story depending on which audience is driving it.
Coverage is built to match. Profound tracks up to nine answer engines on Enterprise, including ChatGPT, Claude, Perplexity, Google AI Overviews, Google Gemini, Google AI Mode, Microsoft Copilot, and DeepSeek, and adds new ones quickly as the market shifts.
Global coverage spans 150+ regions and 30+ languages. And every prompt runs daily through front-end browsers instead of APIs, because front-end and API responses from the same model can differ: the front-end reflects the full behavior a real user encounters. Running prompts the way users actually run them means the data reflects what's happening, not an approximation of it.
Profound vs. AirOps: Agentic layer and brand context
AirOps runs a background agent, Quill, that monitors the data and proposes work. Profound runs one too, Aim, which does the same and then orchestrates the work, building the Agents to carry it out. Both keep a structured record of the brand. The comparison turns on what each one watches, what it ranks against, and how far its work reaches.
AirOps: Quill, working from Brand Kit and estimated demand
Pros:
- Quill tracks prompts, citations, and page performance together
- Brand Kit is a governed source of truth for product lines, audiences, regions, tone of voice, writing rules, and visual identity
- Quill learns from what a team accepts or declines and flags its actions for human review before publishing
Cons:
- Quill ranks opportunities against AirOps' estimated popularity scores rather than real user prompt volume
- Its opportunities span new content, refreshes, outreach to cited domains, and Reddit threads, but execution runs through content Playbooks
- The visibility layer underneath trails Profound on engine coverage and real-user grounding
Quill is AirOps' front door now, and a capable one. It tracks prompts, citations, and page performance together, flags stale pages and gaps competitors have filled, and watches third-party domains such as Reddit, YouTube, and LinkedIn for sentiment and citation problems.
Accepted opportunities become content refreshes, new pages, outreach, or campaigns built around a sentiment theme, with a person reviewing before the content goes live. Brand Kit backs it with a well-governed record of product lines, audiences, regions, voice, and writing rules.
The constraint is what Quill ranks against. Its sense of which opportunity is most relevant rests on AirOps' popularity estimates and a visibility layer that covers fewer engines than Profound's.
Profound: AI Marketer (Aim), grounded in real demand and brand context
Pros:
- AI Marketer, or Aim, is an orchestrator: it monitors visibility, citations, sentiment, FactCheck accuracy, and competitor movement across every engine you track and proposes projects in priority order, each with the case for doing it
- Rankings rest on 2B+ real user prompts rather than relative demand tiers
- Projects reach past content into accuracy corrections, PR pitches, forum answers, and customer questions
- Context Manager holds six Brand Records, including Market and Strategy alongside Offerings and Audience, and Aim reads them when deciding what to propose
- Aim builds the Agents that carry out an accepted project and learns from your team's feedback, saving it in Context Manager for the next project
Cons:
- Early proposals are broader; they sharpen as Context Manager fills in and monitoring history builds
Aim is Profound’s orchestrator. Rather than doing the work itself, it decides what work matters and builds the Agents that do it. To do so, Aim continuously monitors visibility, citations, sentiment, FactCheck accuracy, and competitor movement across every engine you track. Based on what it finds, Aim brings your team a short list of projects in priority order, including evidence and reasoning.
Aim’s range runs beyond content: it can suggest you correct a pricing detail an answer engine keeps repeating, respond to a sentiment drop traced to one review site, or pitch a publication answer engines cite in your category. Once you approve the suggestions, Aim divides the work and builds the Agents that execute it. All you have to do is review the output.
Brand Kit and Context Manager are the closest point of comparison, and AirOps' is strong. Brand Kit is built as governance. It keeps voice, writing rules, visual identity, product lines, and audiences consistent across every workflow. Context Manager covers that ground and adds records for your market and strategy, and Aim reads all six before proposing anything, so your competitive position and priorities shape which projects rise to the top.
Aim also works in Slack as well as in Profound, and it saves your team's feedback in Context Manager for the next project. AEO is where Aim spends most of its effort for now; executive reporting, product detail pages for commerce teams, and social repurposing are early signs of where else it is heading.
Profound vs. AirOps: Content execution and workflows
Once a project is accepted, someone or something has to do the work. AirOps built its reputation on workflow infrastructure. Profound builds Agents around each project. What that takes from your team is where they separate.
AirOps: Powerful workflows, a steep learning curve
Pros:
- A visual workflow builder chains research, brief generation, and drafting into pipelines
- Grid runs workflows across hundreds of URLs at once; Power Agents package recurring jobs into forkable templates
- Strong workflow governance: technical teams build complex workflows that non-technical users run without touching the backend
Cons:
- Steep learning curve. Setup takes real time before workflows run smoothly, even with AirOps University and an embedded Copilot
- Output still needs editorial review; reviewers report catching incorrect stats and product details
When a team accepts one of Quill's opportunities, the work runs on AirOps' execution layer: a visual workflow builder where research, brief generation, and drafting chain into pipelines. Grid runs those across hundreds of URLs at once, and Power Agents package multi-step jobs into reusable templates. The scalability is something reviewers compliment.
Getting there takes time, though. The builder is hard to learn, and even with AirOps University cohorts and an embedded Copilot, the feedback clusters on the same issue, and it has carried into 2026: G2 groups 30+ reviews around AirOps' steep learning curve, and AirOps itself says most enterprise teams take about four weeks to go live. As one reviewer put it: "AirOps has a pretty steep learning curve. The initial setup wasn't easy, and getting to the point where workflows are running smoothly took a significant time investment. I felt that for teams without dedicated technical resources, the ROI just took too long." Content quality is decent but still needs editorial review; reviewers report catching incorrect stats and product details.
Profound: Agents built around each project
Pros:
- For a content project, Aim builds the Agents to deliver it: research against live citation data, drafting against your Brand Records, an optimization pass, and a publish-ready version for approval
- Several dozen templates cover recurring jobs well beyond writing, from AEO Content Refresh and FAQ generation to cannibalization checks, PR target lists, and brand health reports; the content templates are built on analysis of millions of the most-cited pages
- Teams can build a custom Agent in a drag-and-drop builder, without engineering support
- Post-publication citation tracking feeds back into what Aim proposes and what the Agents produce
Cons:
- Content generation requires editorial review before publishing
- The self-learning loop needs published volume before it reaches full strength, so teams starting fresh watch the benefit build over time
In Profound, the unit your team manages is the approval rather than the workflow. For a content project, Aim builds the Agents it needs, and those Agents research the pages winning the prompt, draft against your Brand Records, run an optimization pass, and return a publish-ready piece for review. In AirOps, Quill drafts a Playbook from a request, and a team reviews and publishes it before it runs. Aim builds what the project in front of it requires, so the team's job is approving the output.
Teams that want to drive the work directly still can. Ask Aim for a specific Agent, like a weekly brand health report, and it builds one outside any project. Or, if you’d rather assemble it yourself, you can use the intuitive drag-and-drop builder without leaning on engineering support.
The Agent template library also gives your team ready-made starting points that reach well past articles: research Agents that check for cannibalization and mine customer questions, optimization Agents that add internal links and audit citations chunk by chunk, reporting Agents that assemble brand health reports, and outreach Agents that build a media target list and draft the pitch.
After publication, Profound records whether AI crawlers reached the page, whether it earned citations, and for which prompts, and routes that evidence back into what Aim proposes next. The more you publish, the more signal the system holds about what earns citations and drives visibility in your category.
Profound vs. AirOps: Agent analytics and ROI attribution
Publishing AEO content is a starting point, not an outcome. The harder question is whether that content is being picked up by AI systems, and whether the investment is producing anything a CFO would recognize as a result.
AirOps: Citation counts with a narrower infrastructure layer
Pros:
- Per-URL citation tracking shows citation share by page, not just an aggregate number
- Refresh opportunities auto-populate a relevant Grid workflow, a useful shortcut from insight to action
Cons:
- Prompt volume is a relative tier, not a measured count
- Crawler analytics cover Cloudflare, CloudFront, Netlify, and Vercel only, so teams on Akamai or Fastly can't see which AI crawlers reach their pages
- Outreach opportunities flag the domains answer engines cite most, but AirOps ships no outreach template, so teams build their own Playbook to find contacts and draft the pitch
AirOps can tell you which of your pages earn citations and how often, and for content-refresh opportunities, selecting one auto-populates a Grid with a relevant workflow, which is a rather useful shortcut. But the measurement is narrower. Prompt volume is a relative tier, and crawler analytics cover Cloudflare, CloudFront, Netlify, and Vercel, with no documented Akamai or Fastly integration.
Quill now ties prompts, citations, and page performance together, and AirOps tracks outcomes on the URLs it refreshes, which narrows the gap between recommendation and result. What it can’t match is breadth. Enterprise stacks on Akamai or Fastly get no crawler view, and AirOps’ tags are flat: you can filter by topic, tag, region, and persona, but you can’t nest them to roll insight up across product lines or business units.
Profound: CDN-level attribution with a closed loop
Pros:
- Agent Analytics uses CDN-level integrations (Akamai, AWS CloudFront, Cloudflare, Fastly, and more) plus GA4 to track AI crawler behavior and its influence on human traffic in one place
- Every recommendation includes the rationale: competitive landscape around the prompt, expected visibility impact, and where you currently stand relative to cited pages
- Outreach opportunities come with drafted emails, suggested targeting, and prioritization logic; multi-level segmentation by product line, region, or business unit
- Crawler behavior feeds back into content recommendations. Analytics and the content layer are connected by design
Cons:
- Full GA4 attribution setup requires some technical configuration, adding onboarding time for teams without technical resources
- Attribution clarity improves as content and crawler data accumulate, so newer accounts see less signal early on
Agent Analytics answers the question that comes after the dashboard: did our efforts pay off? CDN-level integrations with Akamai, AWS CloudFront, Cloudflare, Fastly, and others show precisely when AI crawlers access your content, which pages they prioritize, and how often they return, while the GA4 extension brings sessions, key events, and revenue into the same view, so you can connect AI crawler activity and citations to the traffic and conversions that follow. That's a measurement story that holds up in a budget conversation.
Recommendations carry the same depth. Each opportunity surfaces not just what to do but why: the competitive landscape around the prompt, the expected impact, and where you stand relative to the pages being cited. Outreach comes with drafted sample emails, targeting criteria, and prioritization logic, and segmentation runs across product line, region, business unit, or any custom dimension.
Users highlight how much that changes the work. One reviewer noted that "AEO felt like shooting in the dark before Profound, and now we have actionable insights that are driving real uplift." None of it lives in isolation: crawler behavior informs what to create next, and citation patterns inform which templates get prioritized. The loop is closed by design (observe, adjust, measure, repeat), because Profound treats analytics and content as the same workflow.
Profound vs. AirOps: Platform capability vs. a services layer
One of the clearest signals of where a product stands is how much human capital it needs to deliver. Over the past year, AirOps has leaned further into a services layer. That choice says something about the platform underneath it, and it's also where the two companies' support models diverge most.
AirOps: A services layer filling in for the product
Pros:
- Enterprise customers get real hands-on help: a dedicated account manager, 1:1 expert onboarding, live cohort training, and custom agent builds by a Solutions Architect
- For teams that want a done-for-you motion, or agencies productizing AEO for clients, that support model is useful
- AirOps has also courted agencies as a channel for repeatable client delivery
Cons:
- The hands-on support is gated to the top enterprise tier, the same tier that unlocks full prompt tracking
- Needing a Solutions Architect and custom agent builds to reach publish-ready output is a tell: the platform doesn't fully stand on its own
- It leaves AirOps between two models. It’s not a full-service agency that owns your outcomes, and not a self-sufficient platform whose weakest layer is its own data
AirOps helps enterprise customers with people, not just software. The enterprise tier includes a dedicated account manager, 1:1 expert onboarding, live cohort training, and custom agent builds by a Solutions Architect, and AirOps’ homepage promises a team that "stays hands-on from setting strategy through execution." AirOps has also courted agencies as a channel for repeatable client delivery.
For some buyers, that's a plus. A team that wants content produced, or an agency building AEO into its service line, gets real leverage from that model.
But if a platform needs a Solutions Architect and custom builds to produce publish-ready work, that isn't a strength of the product. It's a gap the product can't close on its own. The same pattern shows up on the data side: AirOps' measurement rests on relative prompt-volume tiers and narrower engine and CDN coverage, which is why its data foundation trails, as the sections above detail.
So AirOps ends up caught in the middle. It’s not a strategic agency that owns your AEO outcomes, and it’s not a product self-sufficient enough to deliver without a services team behind it. None of that makes the support bad. It makes it necessary, and that's an important distinction when you're deciding what you're going to invest in.
Profound: An agentic platform, plus dedicated support
Pros:
- Execution lives in the product: Aim scopes the work and builds the Agents that carry them out, so the platform does the doing that AirOps routes to a content team
- Enterprise customers get dedicated support, with options for a dedicated engagement manager, a dedicated Slack channel, and a specialist on a 24-hour SLA
- The human layer is pointed at strategy: competitive intelligence, prompt and gap prioritization, and adapting as engines change rather than making the tool function
- Training through Profound University for teams building internal capability
Cons:
- High-touch strategic support pays off most when your team engages with it; a fully hands-off buyer won't use everything on offer
Profound also puts people around its customers, but the division of labor is inverted. The action happens in the platform: Aim brings the team ranked projects, and the Agents it builds research, draft, and optimize, with a human approving before anything is published. The human layer isn’t there to make the software usable; it’s there to make your strategy sharper.
Enterprise customers work with a dedicated Profound team, providing, as Ronak Patel, Head of Marketing at CRS, describes it, "strategic counsel on how to adapt as answer engines evolve and how to optimize our content for LLMs." That team shares competitive intelligence and helps prioritize the prompts and gaps with the highest impact.
That's the substance behind the "platform vs. services" question. With AirOps, the humans fill in for what the product can't yet do. With Profound, the product handles execution, and the humans elevate the strategy.
Profound vs. AirOps: Compliance and security
For enterprise organizations, especially in healthcare, pharma, finance, and other regulated industries, compliance is a gate.
Profound holds SOC 2 Type II certification and has completed an independent HIPAA compliance assessment conducted by Sensiba LLP, validating enterprise-grade safeguards across the stack: AES-256 encryption at rest, TLS 1.2+ in transit, multi-factor authentication, role-based access controls, comprehensive audit logging, and automated disaster recovery. It also supports SSO via SAML and OIDC and integrates with enterprise infrastructure including GA4, Cloudflare, Akamai, AWS CloudFront, Fastly, Netlify, and Vercel.
AirOps is SOC 2 Type II compliant and documents AES-256 encryption, TLS 1.2+, SSO, and role-based access. But it has no public HIPAA assessment, and its terms of service bar customers from submitting HIPAA-regulated data. For teams running procurement in healthcare and other regulated industries, that can disqualify a vendor on its own.
Profound vs. AirOps: Final verdict
AirOps is a capable content-automation platform, and Quill and Brand Kit make it a more goal-oriented and better-governed one: a background agent that watches pages, citations, and third-party sentiment, and a structured record that keeps output on brand. For content and demand-gen teams whose main job is producing content at scale, it’s a solid tool.
But two things separate a content engine from an agentic AEO platform. The first is data: AEO lives or dies on knowing what people actually ask answer engines, how those engines decide what to cite, and which of your pages earn citations over time, and that's the layer AirOps has historically kept at the standard rather than the frontier. An agent is only as sharp as the signal it watches. The second is what it takes to get results: if a platform needs a services team tacked on in order to deliver, you're buying a tool and an agency-lite at once, and getting the full version of neither.
Profound is built the other way around. It's an agentic marketing platform, and the whole workflow lives in one place:
- 2B+ real user prompts power Prompt Volumes, giving content teams demand signal no topic-level estimate can match
- Aim, an orchestrator that decides the work and builds the Agents to do it, ranks projects against that data and your six Brand Records, reaching across content, accuracy, sentiment, and earned media
- Agents carry those projects out end-to-end
- Agent Analytics closes the loop at the CDN level, measuring which pages the engines actually rewarded so the next round is sharper
- And the human layer (dedicated support for Enterprise customers) is pointed at strategy
Previously, teams serious about AEO ran two tools: Profound for data and something like AirOps for content orchestration. That's no longer necessary. Profound puts AI visibility data built on 2B+ real user prompts, agentic execution, and a closed measurement loop in a single platform, with no services team required to make it deliver.
Profound has raised a $180M Series D at a $1.8B valuation, was named the only Leader on G2's first-ever AEO Grid (Winter 2026), and is trusted by teams at Figma, MongoDB, U.S. Bank, DocuSign, and Walmart, among many others.
If your team needs comprehensive AI visibility data, agentic content creation, and a closed measurement loop in one platform, without renting a team to operate it, Profound is the clear choice.
Ready to take your AEO program further? Book a demo with our team.
Profound vs. AirOps FAQs
What’s the main difference between Profound and AirOps?
AirOps started as a content-automation platform and added AI visibility on top; Profound is a purpose-built agentic marketing platform for AEO. Both propose work unprompted: AirOps through Quill, a background agent, and Profound through Aim, a background agent that also orchestrates the work and builds the Agents to do it, and both keep a structured brand record, AirOps’ Brand Kit and Profound’s Context Manager.
They diverge on what each one ranks against and how far their work reaches. Aim ranks against 2B+ real user prompts across every engine you track, reads market and strategy records alongside brand guidelines, proposes projects across content, accuracy, sentiment, and earned media, and closes the loop with CDN-level Agent Analytics. AirOps runs on relative demand tiers and a thinner visibility layer, and increasingly relies on a hands-on services team to get enterprise customers to publish-ready output.
Can Profound replace AirOps for content creation?
Yes. Profound Agents cover the same content use cases as AirOps (brief generation, FAQ creation, content refresh, and optimization), powered by real AI citation and prompt data, and a template library extends them into research, reporting, and outreach. Quill and Aim both propose the work; Aim ranks it against real user demand rather than estimated popularity, and Agents built for each project replace the Playbooks AirOps teams review and maintain. Because Profound also records what earns citations and what AI crawlers read after publication, its Agents improve against the signal that matters most: which AI crawlers read a page and which prompts it earns citations for, measured against real prompt volume.
Is AirOps a software platform or a content agency?
AirOps is a software platform, but its enterprise motion increasingly leans on a services layer, a dedicated account manager, 1:1 onboarding, and custom agent builds by a Solutions Architect. That’s useful for teams that want a done-for-you motion. For an in-house enterprise buyer, though, it’s worth asking why the platform needs that scaffolding to deliver. Profound’s approach is to put the execution in the product (Aim plus Agents) and use its human layer, dedicated support for Enterprise customers, to improve your strategy rather than make the tool work.