The agent successfully gathered enough information to explain Ravnur's core offering, target audience, and key differentiators, though it worked around a largely broken website to do so. Direct site navigation yielded only the homepage and a few working pages (/use-cases, /ai-powered-video-features, /azure-media-services-replacement); pricing and product details had to be sourced from Microsoft Marketplace listings, GitHub, and third-party comparison articles. The site's thin content, missing standard pages (About, Pricing, Features all 404), and lack of clear product positioning made it substantially less agent-ready than typical SaaS sites.

• ›Step [1] (homepage) was the entry point but contained mostly metadata; no detailed product info was extractable from the page itself. The title 'Live Streaming and Video Archive Management' was the only substantive clue.
• ›Steps [2]–[4] revealed systematic content gaps: /pricing, /about, /products all returned 404 errors. Standard navigational structure was absent, forcing the agent to guess or search.
• ›Steps [10], [22], [24] (use-cases, AI features, Azure RMS replacement) were the only content-bearing pages the site actually served. These provided scattered product info but lacked pricing, feature lists, and clear positioning.
• ›Steps [11], [14], [15], [17], [19], [21], [23] were web searches that became the primary information source. Search results pointed to Microsoft Marketplace, GitHub, and third-party comparison articles—none of which are Ravnur's own site.
• ›Step [12] (/cloud-video-experts, labeled 'About Us') was discovered via search result, not site navigation, yet was the closest thing to company information available.
• ›Step [16] (muvi.com article on Ravnur alternatives) and GitHub links found via search became critical sources for understanding RMS pricing ($499/month), product differentiation, and API compatibility—information not published on ravnur.com itself.
• ›Step [20] (Microsoft Marketplace direct link) hit a WAF challenge and was unreachable, adding friction to vendor-native pricing discovery.
• ›The agent had to infer government focus, product naming, and pricing from scattered search results and external marketplaces rather than from a coherent on-site narrative. This represents poor agent-readiness: critical facts (two distinct products, pricing tiers, compliance features, target personas) were not machine-discoverable from the canonical domain.