Why We Partnered With RegGenome

AI in regulatory change management is only as good as the data underneath it. That's why the partnership matters.

In our last piece we asked the hard questions about AI in compliance - hallucination, accuracy, auditability, accountability. This piece is about how we've responded to those questions in building Single Rulebook.

The short answer is: we partnered with RegGenome. Here's why that matters.

The data problem underneath AI

When firms talk about AI in regulatory change management, the conversation usually focuses on the interface - the tool, the workflow, the user experience. What gets less attention is the data layer underneath.

AI is only as good as what it's trained on. A general-purpose language model trained on broad internet data will produce fluent, confident outputs on regulatory topics. It will also hallucinate, misinterpret technical language, and produce results that are broadly plausible but not reliably precise - which, as we set out in our last piece, is not the standard compliance requires.

The answer to that problem is not better prompting or more powerful models. It is structured, high-quality regulatory data -data that has been processed specifically for regulatory use, tagged consistently, and organised in a way that AI can interpret accurately and reliably.

That is what RegGenome provides.

What RegGenome actually does

RegGenome is a regulatory data technology company and a leader in computational regulation. Their work is rooted in the Regulatory Genome Project - a public-private initiative between RegGenome and the University of Cambridge Judge Business School - which aims to build an open-access global repository of structured regulatory data.

The core of what they do is transform regulation from human-readable text into machine-readable, machine-consumable data. Regulatory documents are processed using AI to extract, tag, and structure the content - creating a consistent, navigable dataset that covers regulatory rules, jurisdictions, and taxonomies across global financial markets.

This is not the same as scraping regulatory websites and feeding the results into a language model. It is a deliberate, structured approach to regulatory data that produces outputs AI can work with accurately - and that compliance teams can rely on.

What the partnership delivers for Single Rulebook clients

The partnership between Kaizen and RegGenome brings RegGenome's structured regulatory data directly into the Single Rulebook platform - expanding coverage significantly and underpinning the AI capabilities that power it.

In practical terms this means Single Rulebook clients can access regulatory content covering all major global financial markets - US federal regulators and agencies, European national law, and global G20 members - alongside emerging compliance requirements for cryptocurrency, cybersecurity, and digital operational resilience.

But the significance goes beyond coverage. The structured tagging that RegGenome applies to regulatory content is what makes Single Rulebook's AI outputs trustworthy. When RegPulse, our specialist AI engine, identifies a relevant regulatory change, assesses its implications, or surfaces connections across jurisdictions, it is working from data that has been deliberately structured for that purpose - not raw text processed on the fly.

That is the difference between AI that is fast and AI that is defensible.

The credentialisation answer to AI risk

In our last piece we argued that firms deploying AI in compliance need to ask hard questions about how it was built, how it handles uncertainty, and what the audit trail looks like.

The RegGenome partnership is our answer to those questions.

Specialised AI working on structured regulatory data consistently outperforms general-purpose models on the measures that matter most in compliance: accuracy, stability, cost of human review, and the reliability of outputs under regulatory scrutiny. That is not a marketing claim - it is what RegGenome's own research shows when comparing specialised approaches against general large language models on regulatory tasks.

For compliance teams, this matters because it changes what they can rely on. An AI output grounded in structured, tagged regulatory data - processed by an engine trained specifically on regulation - is a different class of output from one produced by a general-purpose tool. It is more accurate, more consistent, and more defensible when it matters most.

Why this is the right foundation

The regulatory data problem is not going away. As more jurisdictions produce more regulation at a faster pace, the gap between what firms need to track and what they can track manually will keep widening. AI is the only scalable answer to that gap - but only if the data underneath it is fit for purpose.

That is the bet we made when we partnered with RegGenome. Not on AI as a general-purpose solution, but on structured regulatory data as the foundation that makes AI in compliance actually work.

In our next piece we look at a risk most compliance teams aren't managing - the non-regulatory exchange change that moves as frequently as regulation and gets treated as ops admin.

Single Rulebook is built on RegPulse, our specialist AI engine, powered by RegGenome's structured regulatory data.

See how it works