The agent successfully assembled a comprehensive explanation of Pushary by combining direct fetches from the homepage, pricing page, about page, and docs—but relied heavily on prior knowledge and web search to fill gaps left by JavaScript-heavy pages that didn't expose content in the HTML. The site's modern architecture made it difficult to extract structured information without client-side rendering, forcing the agent to reconstruct the narrative from fragments and infer relationships between product lines.

• ›Steps [1], [3], [5], and [11] (homepage, pricing, about, docs) were cited as sources but returned truncated HTML that revealed mostly boilerplate and font preloads—the actual content-bearing text (pricing tiers, feature descriptions, positioning) was not accessible in the raw fetch responses, indicating heavy reliance on JavaScript rendering that the agent could not execute.
• ›The agent discovered Pushary's dual positioning (web push + AI agent notifications) partly from the pricing page fetch [3] and about page [5], but also drew substantially on web search results [7, 15] which surfaced pages like /cursor-notifications and /mcp-notifications—routes the agent then inferred rather than discovered organically from the homepage.
• ›The site surfaces pricing ($29–$149/month for web push, $9.99/month for AI agents) and feature claims (90%+ open rates, lock-screen approval) but does NOT publish clear competitive positioning, bundling clarity, or detailed feature matrices—the agent had to infer differentiation from what was absent and resort to general knowledge of competitors like OneSignal and Airship.
• ›The agent correctly identified key confusions: pricing structure ambiguity (are web push and AI agent plans bundled or separate?), unclear feature lists, mixed marketing messaging, and incomplete documentation access. These gaps are structural, not navigational—the site exists but doesn't expose information in machine-readable form.