Progressio.AI
Autonomously assess documentation and surface the evidence needed to prove EU AI Act conformance.
Recognised by
Automated, evidence-based conformity assessments across the High-Risk AI system lifecycle.
Maintain a searchable inventory of High-Risk AI systems with ownership, metadata, and provenance across the organisation.
Autonomously analyse documentation, identify relevant evidence, and assess suitability and completeness against each obligation.
Surface gaps, prioritise corrective action, and export evidence packs so readiness is continuous, not point-in-time.
Compress months of evidence discovery for High-Risk AI systems into minutes.
Conformity assessments fail when fluent language substitutes for evidence. Progressio.AI evaluates every obligation against structured evidence, not a reassuring narrative.
Purpose-built text models evaluate evidence against each obligation. General-purpose language models are not used to decide conformance.
Documentation and the regulatory framework are decomposed into a navigable graph of fully covered, non-overlapping atomic obligations. Assessment runs as a deterministic evaluation against that structure.
We train the system to minimize errors by priority. Tier 1 is false confidence: treating a requirement as evidenced when evidence is missing or insufficient. Tier 2 covers remaining assessment errors.
Where language models fit. After the assessment is complete, a general-purpose language model articulates remediation guidance in clear, human-readable form. It does not determine evidence states, map obligations, or decide conformance.
Progressio.AI performs automated, evidence-based, clause-by-clause conformity assessments against Articles 9, 10, 11, 12, 13, 14, 15, 17 and 18 of the EU AI Act.
A clause-by-clause conformity assessment evaluates whether each applicable requirement of the EU AI Act can be objectively supported by evidence, not whether a policy, process, or document merely exists.
Submitted materials are mapped to applicable regulatory obligations, assessed for sufficiency and consistency, and translated into a clear view of strengths, partial coverage, and evidence gaps.
Map submitted evidence to each applicable obligation.
Evaluate each obligation using objective evidence.
Identify where evidence is incomplete, inconsistent, or missing.
Focus corrective action by regulatory significance and effort.
Establish a traceable snapshot for future reassessment.
Measure how the evidenced position changes over time.
Objective evidence sufficiently supports the applicable requirement.
Evidence exists but is incomplete, inconsistent, or insufficient.
Evidence is missing or does not support the applicable requirement.
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.
A clear view of AI risk posture, backed by the evidence needed for legal requirements, reputation, and confident decisions.
Ship faster without the disruption of questionnaires and ad hoc evidence requests, through audits integrated into the AI lifecycle.
Meet EU AI Act evidentiary requirements with greater speed and consistency, while tracking gaps, progress, and trends.
Penalties of up to €35 million or 7% of global annual turnover, with enforcement powers live since August 2026. The Digital Omnibus moved the High-Risk deadline to 2 December 2027. The evidence runway is shorter than it looks.
Prohibited AI practices ban in effect
General-purpose AI (GPAI) obligations apply
Transparency obligations and AI Office enforcement powers in force
High-Risk AI system obligations apply (Annex III)
High-Risk AI in regulated products (Annex I)
Timeline reflects Regulation (EU) 2024/1689 as amended by Regulation (EU) 2026/1744 (Digital Omnibus), in force since 27 July 2026. Last reviewed August 2026.
All customer data is processed and stored exclusively within the European Union. It never leaves EU jurisdiction and is never used to train our models.
Built with privacy across every layer of the platform.
AES-256 at rest and TLS 1.3 in transit.
Every action logged with tamper-proof timestamps.
We are onboarding a limited number of design partners across banking, insurance, and medtech. Early partners assess their own High-Risk AI portfolios on the platform and work directly with the founding team to shape the roadmap.
No. Customer data is never used to train our models. It is used solely to run your assessment: identifying, mapping, and evaluating the evidence needed to prove conformance with the EU AI Act for your High-Risk AI systems.
The platform is built for High-Risk AI systems under the EU AI Act. It performs evidence-based, clause-by-clause conformity assessments against Articles 9, 10, 11, 12, 13, 14, 15, 17 and 18. Each obligation is evaluated on whether objective evidence sufficiently supports it, not on whether a policy or document merely exists.
Manual High-Risk conformity work often takes months. Progressio.AI compresses initial assessments to minutes or hours once relevant evidence is available. Final timing depends on scope, the number of systems, and how readily documentation and operational records can be accessed.
The platform maps your documentation to atomic EU AI Act obligations, assesses whether the evidence is sufficient and consistent, and returns a clear evidenced position for each requirement. Gaps and partial coverage are surfaced with remediation guidance so teams know what to fix next.
No. The platform is designed to find and assess evidence where it already lives. That removes the need for technical teams to fill out endless questionnaires, chase artifacts, or recreate documentation for every audit cycle.
Most approaches rely on questionnaires, consultant-led checklists, or general-purpose AI assistants that summarize documents. Progressio.AI uses specialized text models and a structured obligation graph so assessment is deterministic against atomic requirements. We prioritize Tier 1 errors: false confidence that a requirement is evidenced when the evidence is missing or insufficient.
No. Assessment is performed by specialized text models against a structured obligation graph. A general-purpose language model is used only in post-processing to articulate remediation guidance after the assessment is complete.
Legal and compliance teams use it to establish and defend an evidenced EU AI Act position for High-Risk systems. Technology and data teams use it to reduce audit burden. Executives use it to see where High-Risk AI creates exposure and where action is required.
Yes. Every result is clause-linked and reviewable. Teams can inspect supporting evidence, see whether each obligation is Evidenced, Partial, or an Evidence Gap, and retain human ownership of the final conformance position.
Customer data is processed under strict access controls, encrypted in transit and at rest, and used only to perform your assessments. Data residency and handling controls are designed for regulated environments. Your data is not used to train our models.
Both. You can upload materials directly or connect enterprise systems that already hold technical, organizational, and operational evidence. The goal is to assess evidence in place, not force teams to recreate it for the audit.
Stop spending months buried in regulatory paperwork. Progressio.AI automates the heavy lifting so your teams can focus on what matters.
