paz.ai
success
Claude Code · Haiku 4.51:17
intent
What does paz.ai do and who is it for? Explain it back to me.
17steps
77.2sduration
$1.1058cost
204,457tokens
20 steps3 reasoning steps8 searches
home
docs
docs
/product
search
search
docs
search
/tools/pazi
search
search
/comergent-vs-paz-ai
search
search
/agentic-commerce
search
/p/10037118/paz-ai
44%
on-site discovery
78%
reliability
33%
link following
path origin
- previous resource33%
- web search56%
- prior knowledge11%
insight
The agent partially satisfied the task by assembling a coherent explanation of Paz.ai's value proposition, target audience, and competitive positioning, but could not retrieve complete pricing information despite multiple attempts. The site published core positioning and differentiators clearly on its homepage and guide pages, but kept specific pricing amounts behind a paywall or non-indexed content, forcing the agent to acknowledge gaps and rely on third-party comparisons.
- ›Step [1] (homepage) and step [11] (agentic-commerce guide) provided the core positioning: Paz.ai solves 'AI visibility' by optimizing product data for AI agent recommendations and monitoring rankings across ChatGPT, Google AI, Perplexity, and Claude. This framing and the use of ACP/MCP protocols were directly extractable from the site.
- ›Steps [2] and [7] (pricing page) returned 200 but the HTML was truncated in the artifact, suggesting the page exists but the agent could not parse the actual pricing tiers, costs, or feature boundaries from the rendered response. The agent had to report 'specific monthly cost is not clearly displayed' despite visiting the page twice.
- ›Step [14] (AIMarkDeck third-party review) and step [17] (Comergent comparison page) supplied competitive differentiation and tier naming (Starter/Pro/Growth, Free tier at 100 SKUs, 14-day trial, 20% annual discount) that was NOT discoverable from paz.ai's own fetched pages, indicating the site does not publish pricing details in a machine-readable or easily parseable format.
- ›The site's navigation and structure required search-driven discovery (56% of fetches sourced from web search) to supplement direct browsing, suggesting pricing and feature documentation are either gated, dynamically rendered, or deliberately not indexed.
- ›The agent correctly identified confusions and gaps (exact pricing, feature tier boundaries, revenue attribution claims) rather than fabricating answers, meeting the task's requirement to 'call out anything that was confusing or that you couldn't find'.
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