Policies, processes, and documentation describe intended practice. Execution is evidenced through records, outputs, and other objective evidence demonstrating that the required activities have performed and that the documented controls have been applied in practice.
Simplifying proof of execution through automated evidence evaluation
Progressio.ai performs fully automated, requirement-by-requirement evaluations of the available documentation against the EU AI Act requirements for high-risk AI systems, assessing the existence, suitability, and completeness of the supporting evidence. The results provide a clear view of strengths, partial coverage, evidence gaps, and recommended remediation actions.
Find facts and link them to relevant legal requirements.
Evaluate whether identified evidence supports each requirement.
Identify where evidence is incomplete, ambiguous, or missing.
Focus corrective actions on identified evidence gaps.
Establish initial evidence position for each EU AI Act requirements for high-risk AI systems.
Track gaps and remediation progress as systems, evidence and requirements evolve.
Evidence supports the applicable requirement.
Evidence exists but is incomplete or inconsistent.
Evidence is missing or does not support the applicable requirement.
Progressio.AI performs automated, evidence-based, requirement-by-requirement conformity assessments against Articles 9, 10, 11, 12, 13, 14, 15, 17 and 18 of the EU AI Act.
Expand any Article to see the specific assessment focus, evidence typically involved, and findings that often reveal hidden exposure.
Establishes whether a documented, continuous risk management system exists throughout the lifecycle of the High-Risk AI System.
Evaluates whether data governance processes support the quality and suitability of data used throughout the AI lifecycle.
Evaluates whether technical documentation sufficiently explains how the AI system was designed, developed, validated, and maintained.
Assesses whether appropriate logging and record keeping enable traceability throughout the AI system lifecycle.
Evaluates whether users receive the information necessary to understand intended use, limitations, assumptions, and operating conditions.
Assesses whether appropriate human oversight mechanisms enable effective intervention throughout operation of the AI system.
Evaluates whether the AI system achieves appropriate levels of accuracy, robustness, and cybersecurity throughout its intended lifecycle.
Assesses whether an appropriate quality management system governs the development, deployment, and maintenance of the AI system.
Evaluates whether required documentation and evidence are retained appropriately and remain available for regulatory review.
