The agent successfully gathered comprehensive information about Cekat.ai's offering, target market, and positioning against competitors by navigating the site's main pages and following a comparative blog post. The site was moderately navigable but had notable friction: hidden pricing details, missing company background pages (404s on /about and /why-cekat), and unexplained technical terms like 'AI credits' that required inferential gaps the agent filled from search-derived context and model knowledge.

• ›Step [1] (homepage) and [4] (features) provided core product positioning and differentiators directly in fetchable, clear form; the site explicitly positioned Cekat.ai as WhatsApp-first, LLM-powered, and no-code.
• ›Step [2] (pricing page) exposed the pricing tier structure (Starter/Pro/Business/Custom with MAU and credit counts) but deliberately withheld actual prices in currency, directing users to contact sales—this is a deliberate design choice, not a technical failure, that forced the agent to note the gap.
• ›Step [8] (Cekat vs Qontak blog post, surfaced via web search in step [7]) was the only source comparing Cekat to a direct competitor; the site's own pages contained no competitor comparisons, forcing the agent to rely on search and external sources for the 'How it's different' section.
• ›Steps [3] and [5] returned 404s (/why-cekat, /about), indicating the site lacks public company background and value proposition narrative pages—information the agent could not retrieve and therefore could not incorporate, though it attempted to infer from homepage and features pages.
• ›The agent assembled its final answer from site content (what Cekat does, pricing tiers, features, Qontak comparison) but had to use search results and model memory to contextualize Cekat within the broader 2026 chatbot market and to explain what was missing; the site itself does not surface that comparative or educational context.