The agent successfully assembled a comprehensive explanation of Invezgo by combining web search results with documentation fetches, but had to work around a JavaScript-heavy website that did not expose pricing or feature details in machine-readable form. The site's public pages (homepage, pricing, features) rendered as mostly HTML scaffolding without content; the agent relied on search engine results, FAQ documentation, and third-party reviews to build its answer.

• ›The main invezgo.com pages ([1], [3], [4], [5], [8], [17]) returned JavaScript-rendered templates with minimal extractable content; all substantive information came from external sources—web search snippets ([7], [13], [14], [16], [20], [22]) and the FAQ documentation at docs.invezgo.com ([10]).
• ›Specific pricing tiers and feature-tier mappings were not found anywhere in the trajectory; the agent explicitly noted this gap in the final response and disclosed it as a limitation, rather than inventing details.
• ›The agent discovered Invezgo's core positioning (Indonesia-focused, AI-powered stock analysis, bandarmology) primarily from search result metadata and the GitHub repository title, not from the main website itself. The docs.invezgo.com FAQ ([10]) was the only first-party resource that delivered structured, readable information.
• ›The site does not publish a public pricing comparison matrix, free plan details, or feature-tier breakdown; these would require login or direct contact. This made the site difficult for an agent to navigate without external sources.
• ›The agent was forced to cite external sources (buildornot.io review, Google Play store, GitHub) in its final response because the main domain lacked accessible detail. The site is fundamentally opaque to headless navigation.