The agent successfully gathered enough information to explain Giga's purpose, audience, pricing, and differentiation, but had to assemble the answer largely from web search results rather than the site itself—direct fetches of getgiga.com failed initially, and only the pricing page ([11]) loaded successfully. The site proved difficult to navigate programmatically; the agent relied on search engines to discover product information and had to infer details about features and integrations from incomplete sources.

• ›Only [11] (pricing page) and [21] (search result linking to pricing and homepage) returned actual site content. The pricing page HTML was truncated and didn't fully expose pricing tiers or credit mechanics.
• ›The agent never successfully fetched the homepage or main feature pages directly; it discovered product positioning through web search results and third-party reviews (Shyft, G2, SoftwareAdvice, Y Combinator) rather than from getgiga.com's own docs.
• ›Site discoverability is poor from a bot perspective: direct URL guesses ([1], [2]) failed, and search queries about 'getgiga.com' returned unrelated products (TechGig, GetGo, Get-Gigs). The agent had to use 'Giga AI Workspace' and 'Giga Slack' queries to find relevant results.
• ›The agent explicitly flagged missing documentation: no app integration list, unclear credit consumption model, undefined feature names ('Tracks,' 'Trails,' 'Connections'), and vague positioning vs. competitors. These gaps suggest the site does not publish detailed product specification in an agent-accessible format.
• ›The agent's final answer relies heavily on citations to getgiga.com homepage and pricing page, but the actual content retrieved from those pages was minimal—most specifics came from third-party product review sites and comparisons.