The agent assembled a reasonably complete picture of Dunn Infrastructure Group's business (what they do, who they serve, geographic scope) by fetching the homepage and exploring key pages like services, about, and projects. However, the agent could not answer the pricing question—a dedicated pricing page returned 404—and had to work around limited JavaScript-rendered content and sparse team/credential information. The site is navigable but presents itself as a lightweight marketing presence rather than a comprehensive information resource.

• ›Steps [1], [3], [4], and [8] returned 200 status and contained enough HTML metadata and semantic structure for the agent to extract service categories (water/sewer/storm drainage), geographic service area (Georgia/South Carolina coastal region), and that the company was founded in 2024. The agent relied on HTML structure and inferred company founding date, likely from metadata or schema.
• ›The pricing question could not be answered because /pricing returned 404 (step [5]). The agent correctly noted this gap and explained that construction contractors typically use project-by-project bidding. This is a legitimate business model inference, but the absence of ANY pricing guidance (tier information, typical project sizes, service boundaries) on public pages means pricing discovery failed.
• ›The agent noted that project details, team bios, and case studies were not visible in the HTML it could access, suggesting they are rendered dynamically via JavaScript. A modern Next.js site was fetched but the agent could only see the scaffold, not the rendered content. This is a critical agent-readiness issue—the site is not serving meaningful content in a machine-readable form.
• ›The site provided no explicit competitive differentiation statement. The agent inferred positioning (young, locally focused, full-service) from what was present, but the site itself does not articulate how they differ from alternatives. This required agent interpretation rather than direct disclosure.
• ›The agent successfully discovered contact information and geographic metadata, suggesting the site does include basic business facts. However, the lack of structured data (JSON-LD, schema.org), explicit team bios, or public case studies means the agent had to construct credibility signals from fragments.