The agent successfully assembled a comprehensive explanation of Enlightn's business model, target audience, and competitive positioning by combining the homepage, partner page, and two LLM-friendly text exports. The site published structured, machine-readable summaries (/llms.txt and /llms-full.txt) that directly answered most of the task; however, pricing information was not available on the site itself—the agent inferred it from web search results about market research sample providers, creating a gap between what Enlightn's own site surfaced and what the agent reported.

• ›Step [1] (homepage) provided the core value proposition and founder location via meta description and page title, establishing what Enlightn does and positioning it as 'higher-quality' and 'verifiable'.
• ›Steps [9] and [11] (/llms.txt and /llms-full.txt) were the most content-rich; they delivered competitive metrics (2.1x quality ratio, 43% lower disqualification, 61% pass rate), the match-first model explanation, transparency claims, and founder-led positioning—exactly the type of data needed to explain differentiation and audience. The site proactively published these in machine-readable form.
• ›Step [6] (/partners page) revealed a second audience (panel partners receiving a 'premium CPI channel') and the permissioned recontact model, which was absent from the homepage—showing the site segregated messaging by buyer type rather than explaining both upfront.
• ›Pricing was NOT retrievable from Enlightn's own site (steps [3] and [4] were 404s for /pricing and /about). The agent cited external sources (Koji blog, Improvado blog) for the '20-30% premium' and CPI model, not Enlightn's own documentation. This is the primary navigability gap: pricing is central to the task but not published on the domain.
• ›The agent did not cite /partners or /blog in its sources section, despite fetching them—it relied primarily on homepage meta + llms exports, showing those LLM files were sufficient to answer most questions without exploring the rest of the site.
• ›The site's LLM exports were discovery-optimized (designed for AI consumption) but lacked explicit competitive comparison matrices or case studies, forcing the agent to rely on external benchmarks to fully answer 'how it's different from alternatives.'