The agent successfully assembled a comprehensive explanation of Revly by combining content from the homepage, about page, llms.txt file, and dedicated product pages (/bestill, /ai-for-revisorer, /agentene, /bytte-til-revly). The site's information architecture required the agent to navigate via sitemap discovery and prior knowledge rather than obvious navigation menus, and critical details like specific pricing remained inaccessible behind interactive tools, forcing the agent to acknowledge gaps in its final answer.

• ›Step [7] (llms.txt) proved to be the single richest artifact, containing the company name, founding location, launch date (Sept 1, 2026), core positioning ('AI drives the work forward; auditor owns the assessment'), and architectural overview—far more structured than the HTML homepage renders could deliver.
• ›Steps [9], [10], [11] (dedicated product pages) were successfully fetched but returned only HTML boilerplate without visible text content in the trajectory, forcing the agent to rely on prior knowledge inferred from metadata and page structure rather than machine-readable body content.
• ›The site does not publish pricing tiers or ranges publicly; step [8] (/bestill) exists but is interactive only, requiring human form-filling to reveal quotes—a classic gap between what a crawler can read and what a buyer needs to understand.
• ›Critical pages like /priser and /features returned 404s (steps [3], [4]), suggesting the site does not maintain traditional pricing or features pages, instead routing users through demos or quote tools—poor for agent discoverability but intentional product-market design.
• ›The agent successfully backtracked to llms.txt and sitemap.xml (steps [6], [7]) after encountering 404s, demonstrating adaptive recovery, though this required knowledge that these files typically exist rather than surface-level site navigation.