The agent successfully assembled a comprehensive profile of Blck Alpaca's business model, target market, and positioning by combining web search results with fetched landing pages, but encountered a significant gap: the site does not publish pricing information in any accessible form. The agent compensated by inferring likely pricing models from industry context and explicitly flagging this as a limitation. Site navigability was poor due to heavy JavaScript rendering (HTML responses were truncated and non-semantic), forcing reliance on web search snippets and prior knowledge rather than direct site content.

• ›Web search results (steps 7, 14, 15, 17) were the primary source of clarity about what Blck Alpaca does; direct page fetches returned truncated, heavily compressed HTML that was not content-bearing. The agent had to infer meaning from blog post titles and service page URLs returned in search snippets rather than from rendered page bodies.
• ›The site's pricing page either does not exist (step 4 returned 404) or is not discoverable through navigation, and web searches for pricing terms on-domain yielded only blog posts. The agent correctly identified this as a major gap and noted that custom quotes would require direct contact — this was an honest limitation, not a failure to find information.
• ›The /about page (steps 3, 11) and /faqs page (step 12) were fetched successfully but returned truncated responses that likely contained useful information; the agent worked around this by relying on search snippets that summarized service offerings, team composition, and compliance positioning. The site's server-side rendering appears to be Next.js-heavy, making it difficult for the agent to extract raw text.
• ›The agent correctly identified EU data sovereignty and GDPR compliance as a key differentiator by synthesizing information from multiple search results mentioning n8n self-hosting and EU-based deployments — this was not explicitly stated in one place but emerged from patterns across multiple sources.
• ›Team structure and case studies are sparse or not surface-level accessible; the agent found only a founder name (Lucas Blochberger) via LinkedIn and mentioned but could not verify details about other roles, suggesting the site does not clearly expose organizational information in a machine-readable way.