Grounded in running systems, not takes.
Every article here links back to a live demo, a real policy file, or a real bug we found and fixed — not generic advice. Pre-launch, no client case studies exist yet; what follows is the actual engineering and governance reasoning behind what we've built.
How a governed AI write-path actually works
Read, decide, approve, execute, audit, reject, rollback — with a real bug we caught building it.
NIST AI RMF, ISO 42001, EU AI Act: one mapping, not three checklists
Why the same evidence trail satisfies all three, if it's architectural from the start.
An MCP integration still needs the same approval gates a custom API needs
What Model Context Protocol standardizes, and what it doesn't — with real code.
What actually drives Oracle EBS → S/4HANA migration complexity
A real, reproducible scoring model from module footprint and data volume.
What a real AI cost-governance ledger looks like
88% of our own model calls settle at $0 before touching billed credit — real numbers.
Why "auto-approve everything under $X" is an AP governance anti-pattern
Scope, spend tiers, and ERP validation as independent layers — plus a rollback bug we found.
Prefer how the demos themselves were built? See the engineering writeups →
Try the live demos