Imagine your AI agent finishes a website fix late at night. The next day, the client asks what changed and how you checked it.
The evidence might be split between an audit export, Excel, screenshots and a chat thread. Rebuilding that context costs attention and can add repeated AI investigation.
We're building Sitelemetry around audit targeted fix re-test: structured findings you can review through ChatGPT, Claude or MCP, then hand to a coding agent for a focused repair.
Security, SEO, AI visibility, accessibility, performance and integration coverage depends on the plan; protected audits require authorized, verified targets.
AI can build and ship a website quickly, but it runs is not the same as it s ready.
Before you publish, which checks still slow you down most: security, SEO, AI visibility, accessibility, performance, or integrations?
I m building Sitelemetry to audit those areas and turn measured findings into actionable fix prompts. Review findings in ChatGPT or Claude, or use an MCP-compatible coding agent such as Codex or Claude Code to apply fixes, then re-test with Sitelemetry. Audit coverage and usage depend on your plan. The Local Agent can also audit localhost before a public domain exists.
Sitelemetry is agent-native web assurance for teams shipping with AI. It audits security, technical SEO, AI readiness, accessibility, performance and integrations; turns measured findings into one agent-ready fix prompt through MCP for Codex and Claude; and re-tests the same evidence to prove what changed. Audit public sites, verified domains or localhost through the loopback-only Local Agent, with workspaces, monitoring and reports for the full lifecycle.
