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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

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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Documentation becomes outdated as soon as systems or vendors change.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.

Scope feature preview

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.

Obligations feature preview

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.

Evidence feature preview

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.

Continuity feature preview

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.

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Aigolex is made by Aigolex · Bologna (BO) Italy · VAT IT04292571208

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Data Governance

Data quality in AI systems: what the AI Act requires

Aigolex Team25 March 2026
Data quality in AI systems: what the AI Act requires

What the regulation requires

The AI Act establishes that datasets used in high-risk systems must meet certain quality standards.

Specifically, they must be:

relevant to the purpose of the system

representative of the context in which they will be used

accurate and as error-free as possible

complete, to avoid distortions in results.

This implies structured work of data collection, selection and verification.

The problem of bias

One of the most delicate aspects concerns the presence of bias in data.

If datasets reflect imbalances or discrimination already present in reality, the AI system risks amplifying them.

For this reason, organizations must adopt measures to identify and mitigate any distortions.

Conclusion

Ensuring data quality is not just a technical requirement, but a fundamental element to ensure the reliability of AI systems.

For many companies, this means introducing new data governance processes and more rigorous controls throughout the development cycle.

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Aigolex is made by Aigolex · Bologna (BO) Italy · VAT IT04292571208