The agent successfully assembled a comprehensive explanation of Marveri by combining fragments from the website's solution pages with external third-party sources and founder interviews. The site itself is poorly navigable for discovery: it lacks published pricing, uses JavaScript-heavy rendering that obscures feature details, and offers no blog, comparison docs, or structural navigation. The agent had to rely heavily on web search and external sources (57% of fetches) to answer what Marveri does, who it's for, and how it differs from competitors—information that should ideally be self-contained on the site.

• ›Homepage and solution pages ([1], [10], [12], [22]) confirmed the core value prop (AI-powered M&A diligence with cited results) and identified the three main customer segments (law firms, corporate acquirers, startups), but returned truncated HTML responses with '[truncated, 4400 more chars]' suggesting critical content was either JavaScript-rendered or not fully exposed in the fetch.
• ›Pricing information was completely absent from the site—multiple direct guesses for /pricing and /about returned 404s ([3], [4])—forcing the agent to note 'custom enterprise pricing' as an inference rather than fact. The agent correctly identified this as a transparency gap but could not fulfill that part of the task from first-party sources.
• ›The site's navigation is sparse and brittle: several solution page guesses failed (404s for /features [5], /vc-financings [11], /corporate-work [13], /blog [18]), and the agent resorted to web search to discover that /solutions/law-firms exists ([22]). This suggests the information architecture is incomplete or poorly linked.
• ›Founder/team credibility and competitive positioning came entirely from external sources ([19], [20], [24])—the LawVision CEO interview, Alternates.ai profile, and The Legal Wire article. The site itself contains no team bios, founder story, or explicit competitor comparison, forcing the agent to synthesize differentiators from press coverage.
• ›The agent correctly identified that Webflow-based rendering with heavy JavaScript likely obscures feature detail in the HTML fetches, acknowledging the gap between what the DOM exposes and what a live user would see. This reduced the site's machine-readability for the task.