AirOps vs Peec AI: Content Production vs AI Visibility Analytics

Most AirOps vs Peec AI shortlists begin with a category error. The two products get compared as if a buyer picks one and gets the same job done either way, when in practice they answer different questions. AirOps is built to operate a content program at scale, with an insight layer that routes findings into work. Peec AI is built to measure how AI engines answer questions about a brand, its competitors, and the sources behind those answers. A team that buys the measurement layer and expects finished articles will be disappointed, and the reverse purchase leaves the reporting question open. The useful framing is content vs visibility analytics: which half of the loop is currently broken, and whether the other half is already covered.

AirOps vs Peec AI at a glance

Content production and visibility analytics are different jobs

The content vs visibility analytics distinction matters because the two categories respond to different interventions, and buying the wrong one leaves the actual constraint untouched.

Production is everything between "an opportunity exists" and "a useful asset is live." It covers research, briefs, keyword coverage, heading structure, brand context, drafting, editorial review, linking, schema and metadata, images, publishing, and the distribution assets that follow. A production system earns its price by removing the manual work around writing, not by producing an ungrounded first draft that a marketing lead must rescue.

Visibility analytics is the measurement layer. A team defines the questions buyers actually ask, selects AI platforms and models, runs those prompts on a schedule, stores the answer history, records whether the brand was mentioned or cited, and compares competitors on the same prompt set. Tracking establishes where a brand is present and where it is absent. It does not write the article, enforce the brand voice, update the CMS, or distribute the piece.

The categories are complementary rather than competing. A production tool without measurement publishes more without knowing whether any of it earns AI mentions or citations. A tracker without production shows a competitor winning a prompt without providing a dependable route to closing the gap.

Metrics that should not be conflated

Buyers tend to compare headline percentages that are not measuring the same thing.

• Visibility or mention rate: whether the brand appears in an AI response at all. Peec defines visibility as the percentage of tracked responses in which the brand appears.
• Position: how prominently the brand appears within the answer, as the tracker represents it.
• Sentiment: whether the answer frames the brand positively, neutrally, or negatively.
• Citation rate: how often a page or domain is explicitly shown as a source.
• Source usage against citation: Peec separates a source informing an answer from a URL being explicitly cited. These are different outcomes, not synonyms.
• Share of voice: the brand's share of mentions among the tracked brands in the configured prompt set. It is not market share, revenue share, or a universal ranking, and the way share of voice is framed in AI search deserves scrutiny before it reaches a board deck.

Every one of these numbers is a sample. Bing's AI performance documentation states that its citation activity does not measure ranking, authority, or importance, and describes the data as better suited to trend analysis than to precise accounting. Google's reporting for generative features breaks out impressions, pages, countries, devices, and dates, with scope subject to change. The same caveat applies to any dashboard number from Peec, AirOps, or DeepSmith: report the prompt set, the models, and the window.

AirOps: configurable content operations with an insight layer

AirOps homepage, captured August 13, 2026.

AirOps describes itself as an AI content platform for answer engine optimization and SEO, organized into three pillars. Insights covers AI-search performance, competitive position, citations, prompts, and opportunities. Actions turns those findings into structured, full-funnel content workflows. Context makes the output reflect the brand, its proprietary information, content types, audiences, and regions.

The insight side is more substantial than a content generator normally carries: analytics for visibility, citations, and sentiment; onsite analysis of owned pages; offsite analysis of the domains and communities that influence answers; a pages view combining search and AI-search performance; and agent analytics covering how AI crawlers access the site. The public pages name ChatGPT, Perplexity, Gemini, and Google in connection with visibility, though they do not publish a complete current engine-by-tier matrix, so exact coverage is a question for the sales conversation.

The action surface is where AirOps concentrates its differentiation. Playbooks combine natural-language instruction with sections, tools, inputs, artifacts, triggers, review checkpoints, and publishing steps. Grids provide a spreadsheet-like command center for running those Playbooks at scale, and Workflows offer a lower-level visual builder for branching logic. Power Agents cover recurring tasks such as SERP analysis, keyword research, briefs, article generation, and competitive gap analysis. Brand Kits capture identity across foundations, product lines, content types, audiences, and regions, and Knowledge Bases make uploaded documents, scraped sites, and connected databases searchable by the AI layer.

The integration surface is wide: CMS connections spanning Webflow, WordPress, Contentful, Sanity, ContentStack, Ghost, and Strapi; SEO and data connections including Semrush, Ahrefs, DataForSEO, and Google Search Console; planning tools such as Notion, Airtable, and Google Sheets; Slack, Gmail, YouTube, and Reddit; and an MCP server that exposes AirOps to Claude, Cursor, or another compatible client.

AirOps pricing

Tasks are the usage currency. Only certain workflow-step types consume them, and the amount depends on the step type and data processed, so two articles may not cost the same. Allowances reset each calendar month, and a workspace that exhausts its allowance keeps running and is charged for the overage, published at $0.025 per task on Solo.

Price transparency is limited on the production tiers, and the counterweight is real: task allowances, context resources, trial terms, and the Solo overage rate are public, which is enough for a high-volume team to model usage before asking for a package. Configuration overhead is the second consideration. Playbooks that branch, trigger, review, and publish require design and governance, an investment that pays back for a team engineering a repeatable program and sits idle for a team that wants a fixed article pipeline. AirOps also publishes marketing claims, including that up to 85% of AI visibility comes from content a brand does not own. No methodology accompanies it in the researched material, so it reads as vendor positioning rather than an independent benchmark.

Peec AI: prompt-level measurement and source analysis

Peec AI homepage, captured August 13, 2026.

Peec AI is an AI-search analytics product built around a customer's own prompt set. Its current pages describe analysis across ChatGPT, Perplexity, and Gemini, with wider documentation covering Google AI Overviews, Google AI Mode, Microsoft Copilot, and Grok depending on plan and add-on. ChatGPT is tracked through interface simulation, and the OpenAI search model is a separate unlock.

The metric set is the product's substance. Visibility reports the percentage of tracked responses in which the brand appears, position reports prominence within the answer, and sentiment reports framing. Share of voice reports the brand's percentage of mentions against all tracked brands in the configured responses. Sources and citations report which URLs and domains AI engines access and cite, classified as Editorial, Corporate, UGC, Reference, or Own Website. Competitor benchmarking compares share of voice against named rivals on identical prompts, and source-gap analysis identifies the sources where competitors are cited and the brand is not.

Prompt management is equally structured. Prompts can be organized by topic, funnel stage, or market, tagged, and set Active, Suggested, or Inactive. Suggested prompts draw on real search-volume data and expose an estimated prompt volume score, a meaningful difference from a keyword list repurposed as a tracking list. Competitors surface automatically after appearing alongside the tracked brand at least twice, and can be added manually with aliases or regular-expression matching.

One mechanic deserves attention before any dashboard number is quoted upward. Peec executes each prompt once every 24 hours on every selected model, with plan-level daily or weekly configurations. A Peec dashboard is therefore a repeatable sample of the chosen prompts, models, countries, and cadence, not a live feed of every answer served to every user. Answers vary with time, model version, location, personalization, and retrieval, so the defensible claim is bounded by the tracked window.

Reporting runs through CSV export, a Looker Studio community connector, API access, MCP, and white-label client dashboards for agencies, with API, MCP, and SSO reserved for the Comprehensive tier. Peec also documents crawlability checks against more than 40 AI bots, which is a technical visibility capability distinct from prompt-result tracking.

Peec AI pricing

All three brand plans include unlimited seats and list ChatGPT, Perplexity, and AI Overviews, with support escalating from email to Slack to phone. Unlimited seats read well on a procurement sheet, though Starter's 25 tracked prompts bound what a wide team can actually watch. Search-model add-ons are priced separately by tier, including OpenAI's GPT-4.0 Search Preview at €19, €49, and €149, plus Claude and Gemini search variants. Model menus change, so the exact version and price should be confirmed at purchase.

Agency pricing is demo-led. The published tiers show 10,000, 25,000, and 65,000 monthly credits for Essential, Growth, and Scale, at roughly 111, 277, and 722 prompts, with Comprehensive offering unlimited credits. One prompt against one model for one day equals one credit, so a prompt tracked on one model for a month consumes 30. Credits are allocation slots, not spend-down currency: the pool is shared across projects, can be reallocated, and does not expire.

Any Peec AI alternative comparison turns on the same limitation. The researched public pages document analytics, recommendations, dashboards, sources, exports, and integrations, and do not document a comparable system for generating a brand-grounded article, inserting internal links, producing a cover image, scheduling publication, or publishing to a CMS. That narrowness is an advantage for the buyer whose production is already solved, because a focused measurement layer is easier to run and defend than a broad platform used at ten percent of its surface. It is a hard constraint for the buyer whose bottleneck is output.

DeepSmith: tracking and production in one loop

DeepSmith homepage, captured August 13, 2026.

DeepSmith is an AI search analytics and content production platform in one. The framing that matters for this decision is the loop: see where the brand appears in AI answers, find the gaps, and close them with on-brand content built from the same data. It is a production engine rather than a writing assistant: the output is a finished, publish-ready article, not a first draft to rescue.

The AEO module covers the measurement half. Overview reports mention rate, citation rate, share of voice, sentiment, and visibility trend, with a per-platform breakdown, a competitor leaderboard, and the sources AI cites most. Prompts carries per-prompt mention and citation rates with full answer history, and Discover Prompts generates a starter set from product, persona, and buyer-stage context. Pages shows which owned pages AI actually cites and the prompts driving them, and competitor citations shows who wins, on which exact pages, and by platform.

Content Map turns the brand's site and unlimited competitor sites into one topic and funnel taxonomy, crawled, classified, and re-checked every 24 hours. It exposes coverage gaps where a competitor publishes more, untapped topics where the brand has nothing, and per-topic depth by funnel stage. Opportunity Agents read that data alongside the visibility data and return ideas with the justifying data point attached, the difference between a brainstormed backlog and a defensible one.

Production runs from Opportunities to New Ideas to Planned Content to the Writer to Produced Content. The Writer produces a researched, brand-grounded article with internal and external links, a cover image, and publish-ready metadata. Autowrite writes a configured article on its scheduled date with nobody in the app. Produced Content handles review, revision, and publishing to WordPress, Webflow, Strapi, Sanity, Contentful, or webhooks. Deep IQ stores the positioning, product profiles, personas, brand voice, and content types that ground every output, and the Apps Library turns each finished article into channel-native assets.

The scope boundary is worth stating plainly. Engine coverage rises by tier: Pro tracks ChatGPT, Grow adds Perplexity, Scale adds Gemini, and all ten named engines are Enterprise and Custom. A team needing breadth across every engine on day one is choosing Enterprise, not Pro. The counterweight is that entry coverage is concentrated where buyer research actually begins, and the price is published rather than quoted, with a 7-day free trial that returns real data and real drafts, and no long-term contracts.

Where each option wins

The Peec vs AirOps verdict changes by situation, and DeepSmith enters wherever both halves of the loop are in scope.

Which should you choose

The Peec vs AirOps question resolves faster once the team names the broken half rather than the preferred vendor.

Choose AirOps when the immediate problem is operating a large, governed content program and the team has the appetite to engineer it. Bulk creation, refresh, branching logic, approval gates, and direct CMS workflows are its strengths, and its insight layer keeps the work informed rather than arbitrary. Budget planning requires a sales conversation and a view on task economics, a reasonable trade for a genuinely custom workflow.

Choose Peec AI when production is already covered by writers, an agency, or an existing platform, and the missing piece is measurement and reporting. Prompt-level detail, competitor benchmarking, source-gap analysis, and client-ready dashboards are the center of the product, and its narrower scope is the reason it is good at them. A Peec AI alternative comparison that ends here is a sound one for that buyer.

Choose DeepSmith when the same team owns both problems, which describes most mid-market content functions. Tracked gaps become evidence-backed ideas, ideas become publish-ready articles with links, images, and metadata, and published pages feed back into the citation data that started the cycle. Coverage is tiered and capacity is capped, so the plan should be chosen against the required engines, prompts, seats, and article volume rather than bought at the entry tier by default.

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