The agent successfully built a comprehensive explanation of naia.today by combining fetched site content with web search context on the GEO market. The site's JavaScript-heavy architecture and minimal public pricing details forced the agent to rely on llms-full.txt as its primary information source and to supplement with competitive research; despite these gaps, the agent delivered a coherent picture of what naia does, who it's for, and how it differentiates itself.

• ›Step [7] (llms-full.txt) was the single most content-bearing resource on the site — a machine-readable markdown file that clearly stated naia's core mission, the GEO Score methodology, and target customers. This was a deliberate signal by the site for AI consumption.
• ›Steps [1], [2], [4], [5] all returned Next.js-rendered HTML with minimal text content visible in the raw response; the agent could not extract rendered UI copy from these fetches, forcing it to rely on prior knowledge or the llms.txt artifact.
• ›Steps [9] and [10] (web searches) provided crucial competitive context — the agent used these to position naia against platforms like Evertune, SE Ranking, and Otterly, but this information was NOT sourced from naia.today itself, indicating the site does not publish its competitive positioning or comparative analysis publicly.
• ›Pricing information ([4]) was fetched but not visibly parsed in the response; the agent reconstructed partial pricing (Authority plan features, free tier, enterprise contact) but explicitly noted the dollar amount was missing, showing the site exposes its pricing page but not in a machine-readable form.
• ›The agent cited step [7] as a source in its final response, validating that llms-full.txt was the most reliable content-bearing artifact and suggesting naia actively uses this format for AI discoverability.