A practical compliance platform for companies deploying AI in the EU and the consultants who support them. Built around real deployments, not abstract models.
Join 300+ professionals already exploring AI Act compliance with Aigolex
AI Act compliance is not a one-time exercise.
It's an operational problem.
That's why the platform mirrors the structure of the AI Act itself
Under the AI Act, obligations, risk classification, and roles attach to deployments not to AI models in isolation. This ensures compliance decisions reflect real-world AI use, not theoretical system descriptions.
Define where AI exists, how it's used, and who is responsible.
Misclassifying your role means misapplying every obligation that follows.
Know exactly what the AI Act requires, per deployment.
The AI Act is obligation-driven. Guessing is not a strategy.
Always-ready documentation to prove compliance.
Static documents fail the moment reality changes.
Stay compliant as AI systems, vendors, and uses change.
Most compliance failures happen after the first assessment.
Identify your AI Act obligations, classify risk exposure, and uncover compliance gaps — with a guided interactive assessment.
EU-based or EU-operating
Active AI deployments
Exposure to audits
Legal & compliance leaders
Risk & trust teams
Engineering leaders accountable for AI systems
Experience in legaltech and regulated environments
Built with legal and technical teams
Not Big-4 complexity
Insights, guides, and practical answers about the EU AI Act and AI compliance.
An overview of the EU AI Act's objectives — promoting trustworthy AI, protecting fundamental rights, and supporting innovation — along with its broad, extraterritorial scope and key exclusions.
A detailed breakdown of how the EU AI Act classifies AI systems as high-risk — through safety-component criteria (Annex I) and predefined use cases (Annex III) — plus the exceptions.
Article 5 of the EU AI Act bans AI practices that pose unacceptable risks — from manipulative techniques and social scoring to untargeted facial recognition scraping and emotion detection in workplaces.
A breakdown of the tiered penalty structure under the EU AI Act — from up to €35M or 7% of global turnover for prohibited practices, down to €7.5M for supplying incorrect information.
Responsibility for enforcing the AI Act is divided among national authorities, the European Commission (for GPAI models), and the European Data Protection Supervisor (for EU institutions).
The AI Act caps fines for SMEs and start-ups at the lower of the two thresholds (fixed amount vs. turnover percentage), and requires authorities to consider company size and economic viability.
Under the AI Act, a company is a 'provider' if it develops (or has developed) an AI system and places it on the market under its own name — but other entities can also become providers through rebranding, substantial modification, or changing the intended purpose.
A company is a 'deployer' if it uses an AI system under its authority — but must monitor for role shifts that could reclassify it as a provider with stricter obligations.
To prove AI Act compliance, companies must map their AI usage across workspaces, maintain always-ready documentation including technical files and conformity assessments, register in the EU database, and continuously monitor for changes over a 10-year retention period.
Under Article 25 of the AI Act, a deployer becomes a provider — inheriting all stricter obligations — if they rebrand, substantially modify, or change the intended purpose of a high-risk AI system.
The AI Act requires companies to retain compliance documentation for 10 years — providers must keep technical documentation and quality management records, while importers and authorised representatives retain certificates and declarations.
The AI Act requires that 10-year compliance records be available to national competent authorities, the European AI Office, and fundamental rights bodies — while enterprise customers and auditors also demand access in practice.
With the entry into force of the EU AI Act, General-Purpose AI (GPAI) models are subject to specific obligations. The goal is to ensure transparency, accountability, and risk management across the entire AI value chain.
An introduction to the AI Act — the reasons behind its introduction, the risk-based regulatory approach, and what it means for organizations developing or using AI systems.
An analysis of the key challenges the AI Act addresses — from algorithmic discrimination and lack of transparency to the need for accountability in AI decision-making systems.
An analysis of the wide scope of application of the AI Act, which goes beyond companies that develop AI and involves all actors in the AI ecosystem, with extraterritorial implications.
An analysis of the AI Act's definition of AI system — a deliberately broad definition designed to include a wide range of technologies from machine learning to generative models.
An analysis of the innovative regulatory model of the AI Act, which categorizes AI systems into four risk levels — from unacceptable to minimal — ensuring proportional obligations.
An examination of prohibited AI applications classified as unacceptable risk — systems that pose such a high risk to fundamental rights that they cannot be mitigated through technical or procedural requirements.
Among the various categories introduced by EU AI regulation, high-risk systems are likely the most relevant for businesses. Unlike prohibited applications, these systems can be developed and used, but must comply with specific requirements.
After defining what high-risk AI systems are, it is important to understand in which contexts this classification concretely applies. The European regulation identifies specific areas where the use of AI is considered particularly sensitive.
AI systems classified as high-risk represent one of the most regulated areas of the AI Act. Unlike prohibited applications, these systems can be used, but only if they comply with a series of specific requirements.
Within the requirements for high-risk artificial intelligence systems, risk management plays a central role. It is not an isolated control, but a structured process that must be integrated throughout the entire lifecycle of the AI system.
Among the requirements for high-risk AI systems, data quality represents a fundamental element. The regulation establishes that datasets must meet specific quality standards to ensure reliability and prevent bias.