The agent assembled a comprehensive understanding of Noona by combining web search results with fetches from the business product pages (Noona HQ), since the main consumer app at noona.app was heavily client-side rendered and inaccessible. The agent successfully identified both product tiers, pricing model, key differentiators (no-commission positioning vs. Booksy), and target audiences, though some feature-level details remained incomplete due to client-side rendering barriers.

• ›Web search [7, 8, 10, 12, 13] surfaced the dual-product structure (consumer marketplace + business platform) and discovered the HQ pricing and comparison pages; these search result snippets contained substantive positioning language that informed the final answer.
• ›Fetches to noona.app/pricing and noona.app/features returned 410 status codes, forcing the agent to pivot to noona.app/hq/* URLs (discovered via search), which returned 200s but were also client-side rendered; the agent could not extract detailed tier feature breakdowns from the fetched HTML.
• ›The site does not publish easy-to-parse information on the main domain — homepage, pricing, and features pages all rely on client-side JavaScript rendering, making direct HTML extraction infeasible; agent had to rely on search engine snippets and the help/HQ subdomain as fallbacks.
• ›Pricing and differentiator details (no-commission fees, per-seat model, POS bundling) were gleaned from search results and cited pages rather than from structured, machine-readable markup on the fetched pages themselves.
• ›The agent noted missing information (exact feature tiers, geographic availability, transaction fee details) that would require either JavaScript execution or manual site navigation — indicating the site was not agent-ready in its current form.