AI & Institutional Intelligence
The integration challenge
Organisations deploying AI face a consistent problem: dozens of overlapping rules, inconsistent terminology, and no clear way to connect what the policies say to what the systems do.
This isn’t a governance failure — it’s a language problem. Teams can’t act on requirements they can’t parse, and systems can’t enforce rules they can’t read.
What structured intelligence provides
IQ:NS approaches this by modelling policies, standards, and institutional knowledge as formal ontologies — giving both people and machines a shared vocabulary:
- Obligation clarity — which rules and models apply, what they require, where they overlap
- Cross-model alignment — one concept mapped across every relevant source in
./ontologies/v1/ - Contextual relevance — filtered by jurisdiction, sector, and AI capability
- Machine readability — agents can query the graph directly, no manual interpretation
Who uses this
- Teams building or deploying AI systems
- Organisations navigating multi-framework requirements
- Integration architects connecting compliance to operations
- Anyone building AI agents that need to reason about institutional rules
Why it matters
When rules and operational models live as structured knowledge rather than PDFs, teams can:
- Understand what applies without weeks of manual review
- See where one requirement satisfies multiple frameworks
- Keep current as regulations evolve
- Let AI agents work from the same source of truth as human teams
Getting started
The ontologies in ./ontologies/v1/ are free on GitHub. For hosted services or custom integrations, see pricing.