The agent partially satisfied the discovery-evaluation task by assembling a functional overview of Kaizenify from the homepage, web search results, and third-party comparison sites. However, the site's React-based architecture prevented direct access to pricing details and feature specifics, forcing the agent to rely on search snippets, prior knowledge, and inferred information. The agent delivered a coherent narrative about what Kaizenify does and who it's for, but explicitly flagged missing pricing tiers and feature breakdowns.

• ›Step [1] (homepage fetch) returned only the React shell with metadata hints (logos, stylesheets) but no readable content—the agent could not extract product details, pricing, or features from the native site directly.
• ›Steps [5] and [7] (web searches) became the primary sources: search result titles and snippets provided positioning (shop floor management for cabinet shops), alternative products (Cabinet Vision, 2020 Technologies), and references to 350+ customers. The agent cited these external sources in its final response, indicating it bypassed the site's own content.
• ›Step [6] (kaizenify.app/select-plan) also returned only a shell, and the agent never successfully loaded the pricing table; the pricing ranges cited ($12.75–$89/month, $38–$45/month) came from search metadata and prior knowledge, not from a fetched page with actual pricing cards.
• ›The agent worked around the React barrier by pivoting to third-party review and comparison sites (SaaS Browser, GitNux), which provided structured information about features and alternatives that the primary domain did not expose in a machine-readable form.
• ›The site is poorly navigable for agents: it does not publish static HTML versions of pricing, features, or differentiators; does not include structured data (schema.org) for pricing or features; and relies entirely on client-side rendering, which defeats typical agent access patterns.