The agent successfully assembled a comprehensive explanation of GrantAuthority by combining direct site fetches (homepage, pricing, about, intelligence hub) with external comparison articles and search results. The site's JavaScript-heavy rendering limited static content visibility, forcing the agent to rely on meta descriptions, schema data, and external sources (Fundsprout comparison article) to answer pricing and differentiation questions. Core positioning and database size were well-surfaced, but feature details and verification methodology remained unclear even after exploration.

• ›Step [1] (homepage) and [3] (pricing page) provided core product messaging and tier structure, but pricing page returned truncated HTML that likely contained full details — the agent had to infer tier names and costs from meta descriptions and schema markup rather than visible page content.
• ›Step [4] (about page) and [12] (intelligence hub) were cited as sources but returned script-heavy responses; the agent could not extract team details, founding date, or technical verification methodology from the static HTML despite these being the logical places to find them.
• ›Step [14] (external Fundsprout comparison article) was the only source that explicitly compared GrantAuthority to named competitors with specifics (e.g., 'Granted AI: 85,000 grants'; 'Candid Search: 1.9M organizations'). The site itself does not publish a competitor comparison, forcing reliance on third-party aggregators.
• ›The agent correctly identified confusion about AutoGrant.ai vs. AutoGrant feature — this ambiguity exists because GrantAuthority.org itself does not clarify the relationship on accessible pages.
• ›Search results (steps [9], [10], [16]) were routing-only and did not materially change the answer; the agent's understanding came from direct fetches and one external article, not from search content itself.