Evidence availability is not the same as evidence readiness.
Procurement review slows when the organization cannot show which artifacts belong to the reviewed system, how those artifacts support recognized requirements, who accepted the relationship, and what limitations remain.
Evidence exists but is fragmented
Policies, tickets, test reports, architecture records, and approvals may be distributed across different systems with no bounded inventory for the reviewed AI system.
Control claims are not traceable
A questionnaire or policy may describe a control, but the supporting artifact is not tied to a system version, reviewer decision, or defined review context.
Package changes are not governed
A prior conclusion can remain in circulation even after supporting evidence changes, expires, or is replaced unless the relationship is explicitly re-examined.
Remediation is not ordered
Teams receive a flat list of observations rather than a sequence tied to procurement dependency and evidence readiness.
Define what the vendor must provide before the review begins.
Use the vendor-neutral AI assurance evidence requirement to specify system scope, provenance, integrity references, accepted mappings, gaps, remediation, continued reliance, AI processing boundaries, and reviewer limitations.
Open AI Vendor Evidence RequirementsFive evidence outputs designed for procurement review.
The outputs remain bounded to the agreed AI system, supplied evidence set, recognized review context, and point-in-time assessment.
Evidence Inventory
A structured register of supplied artifacts, their source, format, date, owner, and content hash.
Mapping Summary
Human-verified links between supplied evidence and the review requirements within the agreed scope.
Gap Register
A bounded record of missing, unclear, stale, or insufficiently supported evidence items.
Ordered Remediation Plan
Sequenced evidence actions organized around review dependencies and submission priorities.
Chain-of-Custody Statement
A record of evidence handling, SHA-256 content hashing, and review activity during the engagement.
The package should answer six questions directly.
- Which AI system, version, owner, and deployment boundary does this evidence describe?
- Which supplied artifacts support each recognized requirement or procurement question?
- Which relationships were accepted by an attributable human reviewer?
- Which evidence gaps, limitations, or unresolved conditions remain open?
- What changed between package versions, and which relationships require renewed examination?
- Can another reviewer reconstruct the evidence position without relying on mutable spreadsheets or verbal explanation?
Candidate mapping supports organization. Human acceptance establishes the package relationship.
AGICOMPLY uses deterministic and hybrid mapping methods to identify candidate relationships between supplied evidence and recognized requirements. Candidate scores support prioritization; they do not establish compliance or control effectiveness. A qualified reviewer records acceptance, rationale, authority, and limitations before the relationship is treated as package support.
Review the evidence methodologyWhat procurement and assurance teams usually need to know.
What is an AI procurement evidence package?
It is a bounded collection of supplied AI system evidence, accepted requirement mappings, open gaps, remediation priorities, limitations, and integrity information organized for reviewer examination.
How is this different from a compliance platform?
A compliance platform may support continuous program operations. AGICOMPLY’s fixed-scope Baseline produces a point-in-time evidence package for a defined AI system and review context. No annual software license is required for the engagement.
Can the package support federal AI procurement?
The evidence can be organized using review language associated with federal acquisition, OMB 2025 AI governance and acquisition memoranda, NIST AI RMF, and NIST SP 800-53. The purchasing or authorizing organization retains decision authority.
Does the package certify compliance or control effectiveness?
No. The package states what supplied evidence supports, which limitations remain, and what requires further examination. AGICOMPLY does not certify control effectiveness or issue an Authorization to Operate.
Go deeper on ATO readiness and package-change integrity.
Use the AI ATO readiness guide to understand the full evidence position. Then review how changed or removed artifacts should trigger renewed examination before reliance continues.
Read the AI ATO readiness guideReview evidence reverification after change
Preparing evidence for federal acquisition or an ATO workflow?
Review the federal procurement path for evidence categories, common gaps, and the review language used in OMB, NIST AI RMF, and NIST SP 800-53 contexts.
Review federal AI procurement readinessTurn the current AI evidence set into a package reviewers can examine.
Bring one active AI review, the review type, and the decision date. Mark will confirm whether the $750 Reviewer Evidence Check fits before any evidence is accepted.