trustfoundry.ai
success
Claude Code
intent

What does trustfoundry.ai do and who is it for? Explain it back to me.

17steps
68.7sduration
$0.9545cost
186,639tokens
24 steps7 reasoning steps7 searches
home
docs
/product
/features
docs
openapi
search
search
docs
docs
docs
search
docs
search
search
search
search
80%
on-site discovery
80%
reliability
30%
link following
path origin
  • previous resource30%
  • web search20%
  • prior knowledge40%
  • other10%
insight

The agent successfully assembled a comprehensive explanation of TrustFoundry's core business, target users, pricing, and differentiation, but had to work around JavaScript-rendered pages and missing pricing details by combining the OpenAPI spec, web search results, and press releases. The site's main pages exist but are client-side rendered (returning only HTML scaffolding), making direct content retrieval difficult; the agent relied heavily on prior knowledge, web search, and external press coverage rather than clear on-site documentation.

  • ›The OpenAPI spec [9] was the only direct on-site fetch that contained substantive, machine-readable information about the product (legal research API, authentication, streaming responses). This single artifact was more useful than the homepage or pricing page.
  • ›The homepage, pricing page, about page, and blog pages all returned 200 status but with truncated JavaScript-heavy HTML that contained no readable content. The agent had to rely on web search [10, 14, 20, 21] to surface press releases and news articles that described features, pricing tiers ($49/month Basic, $99/month Pro), case treatment, and hallucination detection—information that should have been on the site itself.
  • ›Pricing specifics (what's included in Basic vs. Pro, API usage models, free trial terms) were never found on the site, forcing the agent to note these gaps explicitly. The agent correctly identified this as a navigation/content gap rather than guessing.
  • ›The site provided no structured product overview, feature comparison, or competitive positioning—all of which the agent had to infer from press coverage and the API spec. The Microsoft Word add-in was discovered via web search [21] rather than site navigation.
  • ›The agent's reliance on prior knowledge (40%) and web search (20%) to answer a discovery-evaluation task indicates the site does not self-publish its own positioning, pricing, or feature information in a form accessible to automated tools.

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