Methodology
How AuditHQ turns checks into an action plan
Create an account to start a Full Audit with supporting evidence and a prioritised action plan.
Findings come from checks
AuditHQ runs deterministic checks against public website signals first. AI helps explain the result; it does not invent the finding.
Severity drives priority
Critical and high issues are promoted when they block visibility, trust, security, conversion, or client-ready handoff.
Evidence stays visible
Every useful issue should show the URL, header, page element, structured-data signal, score, or crawler result behind it.
Reports are handoff outputs
PDFs and shared reports are how the work is explained. The product itself is the issue queue, action plan, verification, and tracking loop.
AI narrative boundary
AI explains. It does not decide what failed.
The checks decide whether a signal passed, warned, or failed. AI is used after that to make the finding readable: what it means, why it matters, and how a person should approach the fix.
Plain-English issue anatomy
What an audit can and cannot prove
- A free scan is a screening sample. Full-audit access covers deeper checks and reporting; the applicable checks still depend on the website, selected suites and available evidence.
- Public website evidence does not reveal every internal system, authenticated workflow or business process. A security or privacy signal review is not a penetration test or a legal compliance certification.
- Missing or inconclusive evidence is not proof that a check passed. Review the recorded outcome and its scope before treating an issue as resolved.
- AI-search readiness checks examine crawler access, content and entity signals. They do not measure an actual mention rate or guarantee that an AI answer will cite the website.
Use the findings to choose the next investigation and verify changes with fresh evidence. A score describes the observed scan; it does not establish a traffic, revenue or security outcome.