Understand your AI Act obligations.
Prove compliance.
Keep it aligned as AI evolves.

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

The real AI Act problem

Companies don't know if they are AI providers, deployers, or both.

Obligations depend on how AI is used, not on the model itself.

Documentation becomes outdated as soon as systems or vendors change.

Audits, enterprise customers, and boards expect immediate answers.

AI Act compliance is not a one-time exercise.
It's an operational problem.

Compliance attaches to deployments, not to AI models

That's why the platform mirrors the structure of the AI Act itself

Company

the legal entity ultimately responsible for compliance

Workspaces

distinct operational perimeters where AI is developed, tested, or used

AI assets

the underlying AI systems or models, independent from any single feature

Deployments

how those AI assets are actually used in practice, by whom, and for what purpose

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.

Scope

Define where AI exists, how it's used, and who is responsible.

•Maps AI usage across companies and workspaces
•Identifies AI assets and their concrete deployments
•Determines your role under the AI Act (provider, deployer, or both)

Misclassifying your role means misapplying every obligation that follows.

Obligations

Know exactly what the AI Act requires, per deployment.

•Assigns regulatory requirements at the deployment level
•Based on role, risk classification, and usage context
•Generates clear, trackable checklists
•Separates applicable obligations from irrelevant ones

The AI Act is obligation-driven. Guessing is not a strategy.

Evidence

Always-ready documentation to prove compliance.

•Centralizes compliance documentation per workspace and deployment
•Includes system descriptions and risk assessments
•Tracks human oversight measures and monitoring controls

Static documents fail the moment reality changes.

Continuity

Stay compliant as AI systems, vendors, and uses change.

•Monitors changes to AI assets, deployments, and operational perimeters
•Flags when obligations or risk levels must be updated
•Alerts when documentation needs to be refreshed

Most compliance failures happen after the first assessment.

AI Act compliance readiness assessment

Identify your AI Act obligations, classify risk exposure, and uncover compliance gaps — with a guided interactive assessment.

Built for teams that can't ignore regulation

EU-based or EU-operating

Active AI deployments

Exposure to audits

Legal & compliance leaders

Risk & trust teams

Engineering leaders accountable for AI systems

Built by people who've done this before

Experience in legaltech and regulated environments

Built with legal and technical teams

Not Big-4 complexity

Prepare now. Don't scramble later.

Risk Management

The role of risk management in AI systems

Aigolex Team20 March 2026

Introduction

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.

This requirement is one of the fundamental elements introduced by the Artificial Intelligence Act and represents one of the main operational challenges for organizations.

What risk management means

Risk management, in the context of the AI Act, involves the identification, analysis and mitigation of risks associated with an AI system.

These risks can concern different aspects, including:

errors in the results produced by the system

negative impacts on fundamental rights

problems related to security or reliability.

The goal is to prevent or minimize these risks before and during the use of the system.

A continuous process

One of the most relevant aspects is that risk management does not end in the development phase.

The regulation requires that this process continues even after the AI system is put into production.

This means constantly monitoring performance, collecting feedback and intervening in case of critical issues.

Conclusion

For many companies, implementing an effective risk management system requires not only technical skills, but also the introduction of new organizational processes.

It is precisely in this step that AI Act compliance becomes a structural and continuous topic, and not an occasional activity.

Understand your AI Act obligations.Prove compliance.Keep it aligned as AI evolves.