The agent successfully assembled a comprehensive understanding of Proxima's positioning, service model, and target market by reading the homepage, diagnostic landing page, blog posts, and a sample report—but worked around significant gaps on pricing, company background, and technical implementation details that the site does not publish. The site is moderately navigable but deliberately gates critical commercial information behind a sales conversation.

• ›Step [1] (homepage) provided the core positioning: 'diagnostic-first' AI agent advisory for operations workflows, with emphasis on shipment exceptions and Indian ecommerce. This was the primary source for 'What They Do' and 'Who It's For.'
• ›Steps [5], [6], [7], [8] (diagnostic landing page, sample report, and blog articles) reinforced the diagnostic-first differentiation strategy and provided concrete examples (64% idle queue time in shipment exception recovery, phased trust approach, human-in-loop design). These were essential for the 'How It's Different' section.
• ›No pricing page exists (step [2] returned 404). The agent inferred the model from homepage CTA copy mentioning 'free 2-day diagnostic' but correctly flagged that deployment pricing is not publicly available—this is a deliberate information architecture choice, not an oversight.
• ›Steps [3] (no /about page, returned 404) and the absence of customer testimonials or technical integration specs mean the agent could not find company background, team size, funding, or supported systems. The agent explicitly called these out as 'missing' rather than guessing.
• ›The agent did not use external web search results (step [9]) in the final answer—they were fetched to contextualize competitors but no specific information from those search results was cited or incorporated, suggesting the homepage and internal blog content were sufficient.
• ›The site publishes its positioning and value proposition clearly in accessible, well-structured pages (homepage, diagnostic CTA, blog), but deliberately withholds pricing, implementation details, and customer proof in ways that force a sales motion. This is agent-hostile for transaction intents but transparent for discovery.