Because we built the data first. Most vendors point a large model at PDFs and hope for the best. We spent eight years ingesting, cleaning, structuring, and modelling regulations, laws, and internal documents into knowledge graphs before the current AI hype cycle existed. Our agents query 500K+ structured obligations each source-linked, versioned, and expert-verified, instead of guessing from raw text. That's why our mapping holds at 94% accuracy, and why our answers cite the exact paragraph they came from.

Over eight years. The company was founded in 2018 on the premise of using machine learning, NLP, and chatbots to turn regulatory text into structured, usable data and that AI went straight into R&D, co-buildin, and production. It was developed in collaboration with Imperial College under an Innovate UK AI grant. So when other vendors are now "adding AI", we're in year eight of running it, past the hype cycle, and past the pilot phase.

Three layers. First, a structured obligation model: 500K+ obligations extracted from 12,800+ regulations across 160+ jurisdictions, each source-linked to the exact paragraph and versioned through every amendment. Second, knowledge graphs: the connectivity between regulations and their own versions and changes, between licensing and permissions and the requirements they trigger, and between external rules and your internal policies and controls. Third, extraction and mapping intelligence: the models that turn new regulatory text into usable obligations and connect them to your compliance map, trained and tuned on eight years of regulatory NLP.

GRC platforms store what you enter, they don't tell you what to enter, or what you've missed. Horizon, libraries, mapping, action plans, testing, and workflow are all connected in one system: AI does the production work of moving a regulation from "published" to "control proven", and humans hold the approval gates. Many customers connect FinregE to their existing GRC via API; others replace it entirely. In the demo we map how it fits your current stack.

RIG is a domain-trained AI embedded throughout the suite and not a generic chatbot pointed at documents. It answers from the structured obligation model and cites the exact paragraph. When you ask "does PS26/4 apply to us?", RIG reasons over the knowledge graph (versions, applicability, your footprint) and shows its sources. Every consequential action it drafts still goes through a human approval workflow, which the audit trail records.

The AI does the production work: monitoring, parsing, obligation extraction, mapping, drafting, testing, and orchestration. Your people hold the decisions: reviewing mappings, approving policy changes, closing controls, and owning the tasks workflows assign to them. No human is doing manual research or data entry on the critical path and no consequential decision is made without an accountable human sign-off, which the workflow captures.

Explainability is structural, not aspirational. Every obligation links to the exact source paragraph; every mapping carries a confidence score; every automated decision is written to an immutable audit log. Human approval gates sit at mapping, drafting, and closure, the AI proposes, your team disposes, and examiners can pull the full decision trail in one place.

Most customers see their first obligations and mappings within days of connecting their regulatory footprint. A full rollout with your policies, controls, testing schedules, workflows, and your team trained on the platform, typically completes in 8–12 weeks, versus 12–18 months for a manual build across multiple tools.

Yes, that's the architecture's intent. Enterprise plans include an MCP (Model Context Protocol) server plus API access to the regulatory data model, with a stable schema and RAG-ready context. Your own LLMs, copilots, and agents can query FinregE's structured obligations and knowledge graphs as a first-class tool. Because the structure lives in the data layer, you can swap or bring your own models without the obligations changing. Many customers run both: our agents for the operational lifecycle, our data for their own AI initiatives.

Regulatory change management software monitors regulatory sources for new and amended rules, works out which changes apply to your organisation, and turns them into tracked actions. FinregE's Horizon agent monitors 2,000+ sources across 160+ jurisdictions, and for every relevant change it starts an impact workflow automatically, so change management stops being a manual research job and becomes a managed pipeline.

You need structured obligations and a structured map of your policies and controls, then the matching runs itself. FinregE's MAPs agent connects each extracted obligation to the policies and controls that should satisfy it at 94% accuracy, shows exactly where you're compliant and where the gap is, with confidence scores and source citations and re-runs automatically whenever a rule changes.

A regulation database is a central, structured store of the regulations and laws that apply to your firm which is searchable, current, versioned, and linked to the exact source. FinregE's libraries hold 12,800+ regulations with 500K+ obligations extracted by AI: not just the raw text, but what applies to you and what action is required, so it doubles as both a regulation database and a regulatory research platform in one.