Accomplish

Automating regulatory compliance for data and artificial intelligence in companies

ACCOMPLISH prepares companies to face growing regulatory scrutiny through the automation, simplification and integration of regulatory compliance into data and artificial intelligence (AI) operations. To do so, it offers an AI-based framework covering legal, environmental, cybersecurity and industry-specific standards, while ensuring transparency, accountability and a human-centred approach.

A key innovation is the Compliance Digital Passport, a new tool for real-time compliance assessment, certification and risk detection, which will enable organisations to navigate regulatory landscapes with confidence. The project’s innovations will be validated through real-world applications in energy, automotive, robotics and manufacturing, and aviation. They will also be tested through integration into open-source or commercial digital solutions, within established data spaces and the AI market landscape.

The project is built around two axes that work reciprocally. The first is automated compliance assessment, recommendation generation and certification: through the digital passport, previously “static” assessments will be transformed into accessible, value-added and up-to-date compliance evidence that promotes transparency and accountability for any stakeholder.

The second axis comprises trustworthy digital enablers for data and AI operations, with tools to design, run and monitor pipelines and their outputs, achieving standards-compliant data handling throughout the entire lifecycle —from data collection to model deployment— that ensures data quality, mitigates bias and reduces energy consumption.

Coordinated by TXT E-TECH SRL, ACCOMPLISH brings together a consortium of 25 partners —universities, research centres and companies— from several European countries. Eurecat takes part through its Big Data & Data Science (BDDS) unit, which leads the work on the core compliance assessment services and on bias detection, synthetic data and data-quality compliance tools. It also contributes to the development of explainable compliance recommendations based on large language models (LLMs).