Review supplier risk with an evidence trail

A critical supplier has fragmented operational and assurance records. Procurement needs a defensible review before renewal, onboarding or a change in dependency. Similar supplier names can be confused, evidence can be stale and external claims may be unverified. A single opaque risk score hides which dependency and business consequence actually matter.

We build a supplier review where every finding shows its source, date and verification status.

• The system resolves supplier identity and collects permitted evidence from authorized identity, dependency, performance and assurance records.
• Each fact is classified by source, date and verification status, then compared to the organization's explicit review criteria.
• Issue summaries are drafted, and missing or conflicting facts route to the right specialist. The owner's decision and conditions are recorded, and the AI summary is never treated as approval.
• A name mismatch prevents evidence from being combined. A missing current document becomes a request, not an inferred pass. If a source changes or is withdrawn, affected findings reopen and the previous decision record is kept.
• Supplier workspaces are restricted, and unverified allegations stay clearly marked. Commercial terms and risk notes stay in the agreed environment, and any external data service is declared.

• A supplier evidence dossier. Every fact is labeled with its source, date and verification status.
• A concern register. Missing facts, conflicts and unverified claims are listed and routed to the right specialist.
• A reviewed decision. The owner's onboarding or renewal decision and its conditions, kept with the earlier decision record.

• Procurement confirms entity identity and business dependency.
• Security reviews technology and assurance concerns, and finance or operations owners review their own evidence.
• A person owns any rejection or risk acceptance.

Existing procurement and vendor-risk platforms may be enough for standard questionnaires. Custom work fits the cross-system evidence and review gaps around material dependencies.

• AI automation