AirOps launches Page360 to track content performance across search, AI, and community platforms

AirOps has introduced a new analytics product, Page360, aimed at helping brands assess how their content performs across traditional search engines, AI-powered search tools, and online community platforms that influence generative models. The launch reflects growing pressure on marketers to adapt content strategies as AI search increasingly shapes how users discover products, services, and information.

The shift toward AI-driven search has created new challenges for brands. While tools like ChatGPT, Gemini, and Claude offer additional pathways to reach consumers, they also fragment visibility metrics. Performance signals are now spread across Google Search Console, web analytics platforms, AI citation data, and content management systems, making it difficult to determine which pages need updates and why.

Page360 is positioned as an attempt to consolidate those signals. The tool combines traditional search data such as rankings, clicks, and impressions with AI-specific metrics, including which prompts trigger citations of a brand’s pages in chatbot responses. It also incorporates engagement data from analytics tools and freshness indicators that show when content was last updated.

According to AirOps, there is no single data source that accurately reflects how a page performs across these environments. Page360 is designed to provide a unified view, allowing teams to evaluate the relationship between Google search performance, AI visibility, and user engagement without relying on multiple disconnected dashboards.

The product also reflects a broader shift in how content is managed. Rather than treating pages as static assets, Page360 frames them as ongoing projects that require frequent updates to remain relevant in AI search results. As AI systems process and reference large volumes of content, outdated or incomplete pages are more likely to lose visibility, even if they previously ranked well in traditional search.

Page360 collects AI search data through automated prompts sent to multiple chatbots, asking common questions related to a brand or product category. These results are then mapped against first-party search data and server logs that indicate when a page is cited by an AI system. The combined dataset is intended to help teams identify gaps between what users are asking, what AI systems are referencing, and what existing content actually covers.

Some early users are applying the tool to large-scale content refresh projects. By analyzing which pages are cited by AI tools, how old they are, and how they perform in search, teams can prioritize updates instead of revisiting content manually. The workflow can import existing pages from a CMS, generate updated briefs based on current signals, and rewrite content with an emphasis on clarity, structure, and current information, with human review steps built into the process.

Beyond refreshes, Page360 is also designed to support planning decisions. By showing whether performance declines are tied to reduced discovery, weaker engagement, or aging content, the tool aims to give marketers clearer signals about what to fix next. This is particularly relevant as AI search rewards concise answers, clear sourcing, and up-to-date information over legacy keyword-heavy approaches.