The agent assembled a comprehensive explanation of Hourivo's business model, pricing, and positioning by combining metadata from the homepage, explicit product instructions from llms.txt, and the About page. The marketing site itself does not prominently expose pricing tiers or feature breakdowns in machine-readable form; the agent had to reconstruct the answer from fragments and prior knowledge (75% from artifact, 25% from guessing). The site is moderately navigable for discovery but underoptimized for agent-driven commerce and pricing clarity.

• ›Step [1] (homepage) returned only HTML with truncated meta tags; the full marketing copy was not extracted, forcing reliance on llms.txt and metadata inference.
• ›Step [21] (llms.txt) was the single most content-bearing artifact—it explicitly stated pricing ($2/user under 20, $1/user 20+, $10/month for AI add-on), use cases, and competitive positioning. This file was not discoverable via links on the marketing site; the agent guessed it existed based on prior knowledge.
• ›Step [22] (About page) confirmed the company founding date and team ownership but did not expose pricing or detailed feature tiers.
• ›Pricing page fetch [3] returned the same truncated homepage HTML rather than a distinct pricing resource, indicating the site either does not have a dedicated pricing page or it renders the same shell and relies on client-side JS—neither is agent-friendly.
• ›The site publishes structured machine-readable metadata (OpenAPI spec at step [11], llms.txt at step [21], and semantic HTML), but pricing and tier comparisons are not exposed in any of these—they exist only in human-facing copy that was not successfully extracted.
• ›Web searches [13], [14], [18], [19] returned zero direct Hourivo information, confirming the product is very new (founded 2025) with minimal external coverage; the agent could not validate claims against third-party reviews.