AirOps vs Profound: Content Engine or Visibility Tracker?

An airops vs profound shortlist usually gets ranked on price and engine count. That ordering assumes a buyer picks one and gets the same job done either way, which is not how the two products were built. AirOps turns AI search findings into governed content work at scale, with an insights layer feeding the queue. Profound explains what is happening in AI search itself: which engines recommend a brand, which prompts carry demand, which sources earn the citations, and how AI agents crawl a site. Both labels are shorthand rather than a hard boundary, because AirOps now carries substantial visibility functionality and Profound now ships agents and content templates. The useful question is which center of gravity matches the half of the loop that is currently broken.

AirOps vs Profound at a glance

Execution and intelligence answer different questions

The profound vs airops distinction matters because the two capabilities respond to different interventions, and buying the wrong one leaves the actual constraint untouched. AEO execution is everything between "an opportunity exists" and "a corrected or new page is live and measured": refresh, creation, answer-ready structure, internal linking, approval, publishing, and the measurement of whatever changed. Visibility intelligence is the layer that tells a team which opportunities exist at all: which prompts matter, which engines answer them, which sources are cited, which competitors take the citation, and whether AI crawlers are reaching the site. Neither layer substitutes for the other, and a team that buys intelligence while production is the bottleneck will hold a better report and the same publishing cadence.

Terms that should stay distinct

Both vendors publish metrics under similar names, and the names do not always mean the same thing.

• Mention rate: how often an AI answer names a brand. AirOps uses the term explicitly, while Profound's materials discuss brand visibility in answers, so definitions should not be assumed to match across vendors.
• Citation rate or citation share: how often an answer links to a page, or how much of the citation set belongs to a brand. A brand can be named without receiving the source link, which is why the two numbers move independently.
• Share of voice: relative visibility against competitors, meaningful only when the prompt universe, engine set, geography, date range, and calculation are held constant.
• Crawler or agent analytics: server or CDN evidence that AI bots visited and retrieved a page. This is an upstream signal and a separate activity from asking an engine for an answer.

Every one of these figures is a sample drawn from a configured prompt set at a moment in time, and a dashboard number is defensible in a board deck only when the prompt universe, the engines, the geography, and the window travel with it.

AirOps: content engineering with an insights layer underneath

AirOps positions itself as an AI search and AEO platform that ships content designed to get cited and tracks LLM visibility alongside it. Its main surfaces are Studio, Inbox, and Insights, and it also exposes itself through an MCP connection inside Claude, Cursor, or another MCP client. The operating model runs as a loop: read visibility, citation, SEO, and engagement data, decide which metric to move, let the agent layer execute governed programs, bring people in for review, then publish and measure the change.

What the Insights layer measures

AirOps Insights reports mention rate, citation rate, share of voice, sentiment, and position, filtered by platform, persona, and region. Prompt-level views break volume, mention rate, and citation rate down by platform, while page-level views combine AI citations with Search Console and GA4 data, so owned pages can be read across AEO, SEO, engagement, freshness, and conversions in one place. External URL discovery extends that to third-party pages cited in the category, with citation influence scores and page-type categories attached, alongside markers connecting content changes to traffic and citation movement.

AirOps names ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, and broader AI search coverage across its public materials. The current pricing table makes only two engine gates explicit: Solo has ChatGPT Insights only, and Pro has multi-engine insights. A clean engine-by-engine list for every paid tier is not publicly exposed, so a buyer with a specific engine requirement should ask for it in writing.

The execution layer is the real differentiator

The connection between insight and work is where AirOps separates itself in any aeo platform comparison. Playbooks are repeatable, governed programs for content refresh, creation, gap-fill, and entity reinforcement. Quill is the agent layer that runs those Playbooks against live data, brand context, and guardrails. Grids handle bulk operations and structured content creation, with conditional logic, version control, human approval steps, and collection-level processing. A refresh program can pull current AI citations, Search Console performance, and engagement data, generate a targeted update, route it through human review, and publish to a CMS as a draft, a staged page, or a live one. CMS collections can be imported, processed, and exported in bulk across hundreds of pages.

Output is connected to Brand Kits, knowledge bases, live data, internal documents, databases, and sitemaps. The public pages do not publish a word-count, quality-score, or publish-ready guarantee, so the honest description is governed content workflows rather than finished articles with no editing. The integration list is unusually broad for this category: Webflow, WordPress, Contentful, Sanity, Contentstack, Ghost, Strapi, and HubSpot for publishing, Semrush, Ahrefs, Moz, and Search Console for research, BigQuery, Postgres, and Snowflake for data, plus more than forty AI models, all included in every plan at no additional cost.

Pricing and task economics

AirOps prices on tasks rather than articles, and tasks are the usage currency for actions such as content generation or data extraction. The official pricing page lists Insights starting at $0 per month and exposes the task limits without displaying every sticker price in parsed text. Two dated 2026 secondary analyses report Solo at $200 per month with 20,000 production tasks and 100 tracked prompts and pages, and Pro at $2,000 per month with 75,000 tasks and 250 tracked prompts and pages. Solo is a single user with one Brand Kit, three knowledge bases, and ChatGPT-only Insights; Pro adds multi-engine insights, unlimited seats, five knowledge bases, and broader integrations. Tasks reset monthly, usage continues past the included balance at a reported Solo overage of $0.025 per task, and a 14-day trial is offered with 50,000 free credits.

The reported jump from $200 to $2,000 is material, and it deserves its counterweight: the higher tier is not simply more seats, it adds 75,000 tasks, multi-engine insights, and the broader integration and support surface. The harder constraint is that a task is not an article. Content-heavy workflows consume usage differently from simple analyses, and no public per-article consumption schedule exists, so a buyer cannot translate 20,000 tasks into monthly publishing capacity without vendor confirmation. A dated third-party review also reports G2 reviewers commonly describing two to three weeks before their teams felt productive, which is review evidence rather than a controlled study, and is consistent with a platform offering this much configuration.

Profound: visibility intelligence across engines, sources, and crawlers

Profound describes itself as a full-stack marketing platform for understanding, analyzing, building, and measuring in the AI era. Its public inventory includes Monitor, Answer Engine Insights, Prompt Volumes, Agent Analytics, Create Agents, Operate, Aim, an AEO Report, and a set of agent templates. The strongest part of the public evidence is the visibility layer rather than an editorial production pipeline.

Answer Engine Insights and citation analysis

Profound's materials describe AI visibility, citation share, ranking position, and sentiment, broken down by themes and product attributes, with citation visibility reported by engine, frequency, and prompt. The citation layer is the part a general tracker rarely matches: cited sources are categorized as Owned, Competitor, Earned Media, PR Wire, Social, or Institution, with Owned, Competitor, and Custom definable by the customer and the rest auto-assigned and overridable. Citation share can then be analyzed by platform, topic, and prompt, competitor comparisons show where a rival takes the citation instead, and outreach intelligence identifies the publishers and authors most cited in a category. For a brand whose AI visibility is shaped by pages it does not own, this is a genuinely different diagnostic from a mention counter.

Profound names Perplexity, ChatGPT, Claude, Gemini, Grok, Microsoft Copilot, Meta AI, DeepSeek, and Google AI Overviews across its materials, states that Enterprise covers ten or more engines out of the box, and says the entry-level plan reaches more than fifty countries and fifteen languages. Two qualifications belong with that breadth. The Answer Engine Insights page describes monitoring a RAG-based search approach across ChatGPT, Perplexity, Microsoft Copilot, and Google AI Overviews, while Agent Analytics tracks crawling by a partly different set. These are different activities, and no complete engine-by-tier table is public, so a reader should not assume every metric is available for every engine on every plan.

Prompt Volumes, Query Fanouts, and Agent Analytics

Prompt Volumes is Profound's attempt to attach demand to AI search work, positioned as an estimate of AI search volume so that content strategy aligns with what people actually ask. A Profound page describes a dataset of more than 1.3 billion real-user conversations with filters including age, income, and region. That figure is a product claim rather than an audited market statistic, and the public pages do not explain every sampling rule behind the daily prompt runs. Query Fanouts exposes the underlying queries an engine derives from a prompt, without a disclosed calculation method.

Agent Analytics is the second differentiator, graphing how much a site is visited by AI bots and how it is interpreted and crawled, with named CDN integrations including Akamai, Amazon CloudFront, and Cloudflare. It lets a team separate "the page was crawled" from "the page was cited," a real distinction that most dashboards collapse. It is not a substitute for answer monitoring, because a crawl does not guarantee a mention or a citation, and a citation can appear without the team ever seeing the crawler event that preceded it.

Agents, and where the public evidence stops

Profound is not only a tracker. Its public site includes templates for content generation, an AEO-Optimized FAQ Generator, and agents for demand generation, brand work, content refresh, and optimization suggestions. Those agents are fed by citation, prompt-sentiment, and real-user prompt data rather than working in isolation, and Profound describes a feedback loop in which post-publication citation tracking informs future generation. Aim surfaces suggested and tracked projects with prioritized tasks, which is more useful to a marketing lead than a dashboard with no next action.

What the reviewed public materials do not establish is a native end-to-end production pipeline: a content calendar, automatic article generation on set dates, internal-link insertion, metadata and schema generation, cover images, or direct publishing to a named CMS. Publicly named integrations are Akamai, Amazon CloudFront, Cloudflare, Slack, and a REST API with a default limit of 600 requests per hour per key. That is a boundary in the public evidence rather than proof the private product cannot do these things, and it is exactly the boundary a buyer should test in a demo.

Pricing and credit economics

Profound's pricing is less completely exposed than its feature marketing. An April 2026 independent analysis and a G2 listing both report Starter at $99 per month and Growth at $399 per month, with Enterprise custom. G2 shows 100 credits per month on Starter and 400 on Growth, while the official pricing text shows 50 tracked prompts alongside 100. Agents are priced on credits, and credit consumption per agent run is not disclosed, so monthly article capacity cannot be inferred from a credit balance. A secondary review describes Starter as ChatGPT-only.

The public evidence supports one clear statement about price: Profound is lower-priced at entry than the reported AirOps Solo plan. It does not support a complete total-cost comparison, because prompt limits, engine tiers, seats, projects, annual billing, trial terms, overage rates, and API entitlements are not all public. A buyer should request the full plan sheet before treating either headline number as the decision.

The cost models do not compare directly

AirOps bills tasks, Profound bills credits and tracked prompts, and DeepSmith bills articles, tracked prompts, seats, and engine tiers. A profound vs airops price ranking built on headline monthly figures means nothing, because the units underneath are not convertible. The defensible method is to estimate the workload first, in articles created or refreshed per month, prompts tracked, engines required, and seats needed, then ask each vendor what that workload consumes in its own currency. Any airops alternative comparison that skips this step is comparing sticker prices rather than cost.

Two cautions apply to both vendors. Customer-result figures on either site are case-study claims rather than expected outcomes, and no neutral source establishes that one vendor's visibility metrics are more accurate than the other's. Vendor comparison pages also disagree by design, so neither is a benchmark.

DeepSmith: visibility and production on one loop

Any airops alternative comparison eventually reaches the reader whose answer to airops or profound is "both halves are the problem." DeepSmith is one platform for AI search analytics and content production, built so the measurement and the work run on the same data rather than in two systems joined by a human handoff.

The AEO module reports mention rate, citation rate, share of voice, sentiment, and visibility trend, with per-platform breakdowns, full answer history per tracked prompt, the pages AI actually cites, and a competitor leaderboard showing which rival wins which prompt on which exact page. Content Map crawls the owned site and unlimited competitor sites onto one shared taxonomy of topics and funnel stages, then reports coverage gaps where a competitor publishes more and untapped topics where the brand has nothing at all, with sitemaps re-checked every 24 hours. Opportunity Agents read that data and return ideas with the justifying data point attached, which is the difference between a brainstormed backlog and one a marketing lead can defend in a planning meeting.

Content Studio carries those ideas through New Ideas, Planned Content, and Produced Content. The Writer turns one planned idea into a finished, brand-grounded article, researched, internally and externally linked, and delivered with a cover image and publish-ready metadata. Autowrite runs the same production on a scheduled date with no one in the app, which converts a content calendar from an aspiration into a system. Produced Content handles review and editing, then publishes directly to WordPress, Webflow, Strapi, Sanity, or Contentful, or to webhooks, and the Apps Library adapts each finished article to LinkedIn, X, Medium, Substack, newsletter email, Reddit, and more.

Deep IQ is what keeps output on-brand at volume, storing company positioning, a profile per product with claims to make and avoid, buyer personas, brand voice, visual guidelines, and content types as structured context that shapes every draft. Pricing is published rather than quoted: Pro at $99 per month, Grow at $199, and Scale at $399, or $80, $160, and $299 on annual billing, with 20, 40, and 90 articles, 50, 100, and 200 tracked prompts, and five, seven, and ten seats respectively. A seven-day trial provides real data and real drafts before payment, with no long-term contracts and no cancellation fees.

Engine coverage is a tier question rather than a catalogue one. DeepSmith tracks the same ten engines a broad monitoring program expects, ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, Google AI Mode, Grok, Meta AI, Microsoft Copilot, and DeepSeek, and coverage rises with the plan: Pro tracks ChatGPT, Grow adds Perplexity, Scale adds Gemini, and all ten sit on Enterprise or Custom. So the trade is not breadth against production but when breadth arrives. A team that needs every engine measured from day one should price Enterprise, while a team whose actual constraint is publishing gets the production loop attached to that same engine roster. DeepSmith is still not a like-for-like replacement for Profound's prompt-demand dataset or CDN-level crawler analytics, and a buyer whose central requirement is either should evaluate Profound on that basis.

Which should you choose

The airops vs profound decision resolves faster once a team names the broken half rather than the preferred vendor.

Choose AirOps when the content program already exists and the pain is executing against it. A team with a substantial CMS estate, a refresh and gap-fill backlog, and a need for branching workflows, approvals, bulk operations, Brand Kits, and enterprise governance will get more out of AirOps than out of any tracker. Budget for the learning curve and for validating task consumption against real workloads.

Choose Profound when the first priority is understanding AI search rather than publishing into it. A visibility, brand, PR, or SEO team that needs to compare many AI surfaces, classify citation sources, estimate prompt demand, or monitor AI-agent crawling has a stronger case here than anywhere else in this aeo platform comparison. Confirm credits, prompt quotas, engine tiers, seats, and CMS options before assuming it can replace a production system.

Choose DeepSmith when one team owns both the visibility number and the publishing cadence. The fit is strongest where evidence-backed opportunities need to become on-brand, SEO- and AEO-ready articles without another handoff, and where the bottleneck is editing, internal linking, metadata, brand context, scheduling, publishing, or distribution after the idea is found. Transparent article and prompt limits matter more to this buyer than having every engine on the entry plan.

Teams weighing whether airops or profound closes the gap can test the combined approach directly: start a DeepSmith free trial and compare real visibility data and real drafts against a two-tool stack before committing budget to either half.