The agent assembled a comprehensive explanation of Affogato's purpose, target audience, pricing model, and competitive differentiation, but had to work around a largely non-functional website. The site's homepage and pricing page returned only raw HTML scaffolding with no readable content, forcing the agent to rely on web search results and third-party review sites (56% of sources) to answer all core questions. The agent succeeded in delivering an informed overview, but affogato.ai itself did not directly surface the information—it was reconstructed from external sources.

• ›Direct site fetches ([1], [3], [9]) returned only Next.js boilerplate HTML with no readable content, making the site functionally non-navigable for content extraction. All substantial information came from search results and third-party review pages.
• ›Web search queries ([6], [10], [16], [18]) and third-party review sites ([12], [14]) provided 100% of the factual content cited in the final answer: pricing tiers, FaceLock/TrueTouch features, target personas, and competitive positioning. The agent had to abandon the primary site and assemble knowledge from secondary sources.
• ›The /about and /features pages returned 404s ([4], [7]), further limiting the site's capacity to surface identity/positioning information. No structured data (schema.org, OpenGraph metadata) was extractable from the homepage fetch.
• ›Pricing details remain ambiguous in the final answer despite effort; the agent noted that pricing page HTML was truncated and could not extract specifics like simultaneous render limits or per-tier model access. This reflects the site's actual inability to deliver structured pricing data.
• ›The agent accurately identified and disclosed gaps (free tier duration, Affogato Agent specifics, FaceLock limitations) that it could not resolve even through search, indicating honest assessment of remaining unknowns despite successful task completion.