IQ:NS for Software Engineering
The challenge
Engineering teams build and deploy AI systems but often lack clarity on what compliance actually means in code — which tests to run, what to document, which controls matter for their specific system.
What the ontologies provide
- Requirements as structured data — framework obligations queryable via SPARQL, not buried in PDFs
- Pipeline integration — embed ontology queries in CI/CD to surface relevant requirements at build time
- Agent-ready knowledge — MCP server lets AI coding agents reason about institutional rules directly
- Shared vocabulary with compliance — same concepts, same definitions, no translation overhead
How it fits
The ontologies sit alongside your existing tools — Jira, CI/CD, model registries. They provide structured context so engineering decisions connect to institutional requirements without manual handoffs.