AirOps
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About
AirOps is an AI-powered content engineering platform designed to help marketing teams scale their content operations while maintaining high quality and strict brand alignment. Unlike basic generative tools that produce generic one-off drafts, AirOps focuses on a systematic approach to strategy, creation, and performance tracking. It unifies SEO data, AI search visibility (AEO), and analytics to help brands understand exactly where they are being cited and where they need to improve to stay visible in an era of evolving search engines and LLMs. The platform operates through a suite of integrated tools including Grids, Workflows, and Power Agents. Grids provide a powerful interface for team collaboration at scale, allowing users to manage thousands of articles or updates from a single dashboard. Workflows enable the creation of precise, multi-step content processes that incorporate live data sources and brand-specific knowledge bases. These processes often include human-in-the-loop review steps to ensure accuracy. Power Agents act as pre-built, agentic building blocks for common tasks, such as refreshing stale content or conducting keyword analysis, which can be customized to fit specific brand requirements. AirOps is primarily built for high-growth marketing teams, SEO specialists, and content agencies who need to manage large-scale content libraries efficiently. It is particularly effective for organizations looking to increase their citation rates in AI search results from providers like Perplexity or Google’s AI Overviews. Case studies from major brands like Webflow and Ramp demonstrate the platform's ability to significantly increase content velocity—often refreshing dozens of pages per month—and improving conversion rates without requiring additional headcount. What sets AirOps apart is its emphasis on the entire content lifecycle rather than just the initial generation phase. Its Page360 feature unifies GA4 and SEO data to identify stale content that requires refreshing to maintain visibility. By integrating directly with popular CMS platforms like Webflow, WordPress, and Contentful, the tool eliminates manual handoffs. The ability to govern brand voice through dedicated Brand Kits ensures that all AI-generated output remains uniquely tailored to the company’s specific goals and technical requirements.
Pros & Cons
Supports multi-engine monitoring including Perplexity, Google AI Studio, and OpenAI.
Allows for human-in-the-loop steps to maintain high editorial standards for AI content.
Grids enable the management and updating of thousands of pages simultaneously.
Deeply integrates with enterprise CMS stacks like Contentful, Strapi, and Webflow.
Offers pre-built Power Agents that handle common, repetitive SEO tasks immediately.
The Solo plan is restricted to ChatGPT/OpenAI insights only.
Task-based billing can be complex to estimate for multi-step automated workflows.
Access to the Solo plan is limited to a single user only.
Using personal API keys on the Solo plan requires an additional add-on fee.
Use Cases
SEO Managers can identify stale content through Page360 and automate the refresh process to maintain search visibility.
Growth Marketers can build workflows that monitor Reddit and external sites for brand mention and citation opportunities.
Content Agencies can use Grids to manage high-volume content production for multiple clients without increasing team size.
Enterprise Teams can use Brand Kits to centralize their unique tone and knowledge for consistent global content output.
Marketing Analysts can develop geo-audit workflows to consolidate technical and content analysis across different regions.
Platform
Task
Features
• customizable workflows
• pre-built power agents
• direct cms integrations
• multi-engine ai search monitoring
• page360 analytics integration
• knowledge bases for proprietary data
• brand kits and voice governance
• grids for team collaboration
FAQs
How is AirOps different from other generative AI tools?
Most tools focus on producing one-off drafts, but AirOps provides a complete system for visibility, prioritization, and performance tracking. It allows you to bake in proprietary knowledge and brand voice to ensure content is accurate and uniquely yours.
What are tasks in the AirOps platform?
Tasks are the currency used to complete specific actions like generating content or extracting data within a workflow. You only pay for the tasks your workflows actually run, and usage can be tracked via the platform settings.
What types of content can I create with AirOps?
The platform supports a wide range of content including blog posts, product detail pages, technical documentation, and pSEO content. It also identifies opportunities to show up in external domains like Reddit threads that LLMs trust.
Can I use my own AI model API keys?
Yes, Scale and Agency plan users can bring their own model API keys as a standard feature. Solo plan users also have the option to add this capability to their accounts by contacting the support team.
How does the AirOps Free Trial work?
The 14-day Free Trial provides access to all Scale plan features without requiring payment details upfront. The trial ends when you either run out of allotted tasks, the 14-day period elapses, or you upgrade to a paid plan.
Pricing Plans
Solo
Unknown Price• 100 Tracked Prompts & Pages
• ChatGPT Insights Only
• Monthly Opportunity Reports
• 20,000 Tasks for Content Production
• 1 Brand Kit
• 3 Knowledge Bases
• Basic CMS & SEO Integrations
• Single-User Access
Pro
Unknown Price• 250 Tracked Prompts & Pages
• Multi-Engine Insights (Google, Perplexity, OpenAI)
• Weekly Opportunity Reports
• 75,000 Tasks for Content Production
• 1 Brand Kit
• 5 Knowledge Bases
• AEO, Social & Project Integrations
• Unlimited Seats
Enterprise
Unknown Price• Custom Prompts & Pages Limits
• Multiple Regions & Personas Tracked
• Custom Agent Builds
• Unlimited Knowledge Bases & Brand Kits
• Dedicated Account Manager
• 1:1 Expert Onboarding
• Unlimited Seats
• SSO Support
Job Opportunities
Technical Support Lead
Drive growth and AI search visibility through automated content engineering that combines multi-engine insights, brand-specific knowledge bases, and team workflows.
Benefits:
Equity in a fast-growing startup
Competitive benefits package
Flexible time off policy
Parental Leave
A fun-loving and nerdy team
Experience Requirements:
4-6+ years in technical support, solutions engineering, or CSM
2+ years of people management experience
Other Requirements:
Hands-on technical expertise with LLMs and prompt engineering
Experience with workflow automation tools (Zapier, Make, n8n)
Player-coach mentality
Responsibilities:
Recruit, hire, onboard, and coach a technical support team
Design and implement an AI-first support strategy
Serve as the ultimate escalation point for complex technical issues
Establish SLAs and quality metrics
Partner with Solutions Architects for customer handoffs
Show more details
Director of Data
Drive growth and AI search visibility through automated content engineering that combines multi-engine insights, brand-specific knowledge bases, and team workflows.
Benefits:
Equity in a fast-growing startup
Competitive benefits package
Flexible time off policy
Parental Leave
A fun-loving and nerdy team
Experience Requirements:
8+ years in data roles
3+ years leading teams
Other Requirements:
Strong background in both data engineering and data science
Expert SQL and Python proficiency
Responsibilities:
Own end-to-end data platform (batch and streaming)
Run the data warehouse with strong performance and cost management
Build ML systems that analyze AI search behavior
Partner with Product and GTM teams on tracking plans
Build and lead the data team
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Lead Applied Scientist
Drive growth and AI search visibility through automated content engineering that combines multi-engine insights, brand-specific knowledge bases, and team workflows.
Benefits:
Equity in a fast-growing startup
Competitive benefits package
Flexible time off policy
Parental Leave
A fun-loving and nerdy team
Experience Requirements:
5+ years building production machine learning systems
Strong background in NLP and search/recommendation systems
Other Requirements:
Deep expertise in classical and modern deep learning architectures
Ability to take models from research to production
Responsibilities:
Design and deploy end-to-end machine learning systems
Build ML systems that analyze AI search behavior
Partner with product managers to translate requirements into solutions
Influence architecture decisions and improve team practices
Show more details
Ratings & Reviews
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