Regulatory Artificial Intelligence
CUBE leverages the latest advances in deep learning to transform unstructured regulatory data into user-specific and actionable regulatory intelligence.
What RegAI means for compliance
Amplified relevance
Discover only what matters to your business, with no fear of missing out. Our models dramatically reduce false positive and false negative content.
Accelerated understanding
Our natural language processing models help you identify key points in regulatory content - so you only have to read what is most essential.
Automated workflow
Multiple steps of your regulatory change workflow can be automated based on your previous actions and our semantic representation of the content.
RegAI architecture
CUBE combines state-of-the-art machine learning techniques, including computer vision, natural language processing, and graph machine learning, to transform regulatory content into structured, enriched, and actionable intelligence.
Computer vision
Computer vision models convert text images into machine readable content and reveal structural components; everything from headers to body paragraphs to footers, resulting in a hierarchical document.
Natural language
CUBE applies deep NLP and its proprietary RegLM to translate, classify, and contextualise legal and regulatory content. This model is fine-tuned for a variety of use cases: entity extraction, citation extraction, document type classification, obligation identification, and summarisation.
Graph machine learning
The structured and enriched content is classified and enters our regulatory knowledge graph, along with user data. Graph machine learning techniques leverage the knowledge graph to recommend content to users based upon their actions.
Say hello to RegBrain
RegBrain allows clients to apply CUBE’s full AI stack, including agentic AI, summarisation, classification, and enrichment, to their own content. Delivered as APIs or via UI, RegBrain powers faster review, feedback, and action.
RegAI foundations
How we approach the use of Artificial Intelligence
At CUBE, we believe in both transparent and ethical technology. Our proprietary AI is founded upon six interconnected core principles.
Explainable
User trust and transparency are as important as model performance. We use a variety of visualisation methods to illuminate how our models make decisions.
Human-in-the-loop
Our data scientists work hand in hand with our regulatory experts. This synergy ensures that our models continuously learn from the highest quality data. Just as importantly, users directly influence the performance of our models through in-product feedback mechanisms.
Semantic
We use state-of-the-art NLP models, fine-tuned on our regulatory data. We are creating a regulatory language model fully trained on our data. Our models go far beyond keyword matching—they have a deep understanding of syntax and legal vocabulary.
Scalable
Our models are designed to handle the volume, variety, and velocity of regulatory change. This is due to both their architecture and serverless deployment in the cloud.
Secure
Given the high sensitivity of our client data, we have implemented security at three different levels: backend access to our services, frontend access to our services, and our data pipeline. All user data in the cloud is fully anonymised.
Sustainable
We are well aware of the carbon footprint of AI models trained on enormous volumes of text data. We improve our models by taking the time to carefully curate smaller, higher-quality datasets. It’s a win for accuracy and a win for the environment.
Get in touch
We always strive to listen and value feedback. If you have any questions, suggestions or would like to explore our Automated Regulatory Intelligence solutions, don't hesitate to get in touch. Our dedicated team is here to assist you.