ScanHaz was built to reduce manual inspection administration while preserving the role of competent human review and verification.
Workplace safety evidence is often fragmented across photos, notes, checklists, emails, and follow-up tasks. That makes it harder to review what was observed, who owns remediation, and whether a fix was actually verified.
ScanHaz uses AI to assist with visible-evidence review and documentation. Site photos can produce suggested findings, but those suggestions remain pending until an authorised person confirms, edits, dismisses, or supplements them.
We believe AI should augment human judgment, not replace it. The product is designed around that separation: AI can assist with analysis, while people retain authority over confirmed findings, remediation verification, compliance decisions, and external escalation.
Every feature we build must make workplaces genuinely safer — not just easier to document.
Capture and remediation tools should be straightforward, while inspection and sign-off authority still follows the organisation’s competence and role requirements.
Reviewed findings, remediation proof, timestamps, audit history, and integrity metadata should be preserved clearly.
AI suggestions do not become canonical findings until a person reviews them.
Evidence Packs capture reviewed records and use SHA-256 integrity metadata plus a verification token.
Tenant scoping, role permissions, backend-only integration credentials, and fail-closed privacy processing are built into the workflow.
Plan, checkout, and trial availability are controlled server-side and shown on the Pricing page.
Confirmed actionable findings can be assigned, supported with proof, human-verified, and retained in the audit trail.