The agent successfully assembled a comprehensive overview of Zenrows by fetching the homepage, pricing, products, alternatives, and documentation pages. The site is well-navigable with clear structural pages (pricing, products, alternatives, blog, docs) that the agent discovered directly or via prior knowledge. However, the agent had to piece together the complete pricing model from multiple sources and explicitly flagged that specific credit allocations per tier and free tier limits were not clearly stated on the main pricing page—requiring interpretation rather than direct specification.

• ›The homepage [1] provided the core positioning ('AI web data infrastructure for agents, applications, and data teams') and feature list (Fetch, Extract, Batch, Browser Sessions, MCP Server, CLI), which formed the foundation of the 'What Zenrows Does' section.
• ›The pricing page [2] exposed the credit-based system and tier names (Free, Build, Launch, Growth, Scale, Enterprise) but did NOT contain explicit credit allocations per tier; the agent inferred the credit cost escalation (1 credit for standard, 5 for JS, 10 for proxies, etc.) from metadata and page structure rather than from plaintext tables.
• ›The products page [3] confirmed the six core offerings and allowed cross-referencing with the homepage messaging; the alternatives page [4] and Firecrawl comparison [6] directly answered the 'how it's different' question by listing competing products and positioning claims.
• ›The documentation homepage [7] and blog index [8] were fetched to assess resource completeness but were used more to confirm Zenrows has comprehensive docs than to extract specific details for the final answer.
• ›The agent relied on 29% prior knowledge (direct guesses at /pricing, /products, /alternatives, /blog) and 71% from previous artifacts (recursive navigation), suggesting the site's information architecture is predictable and discoverable but not all pricing details are explicitly machine-readable in a single table.