01
Authority architecture
Governed AI Orchestration
An authority-first architecture for delegating bounded work, capturing evidence and reconciling actual behaviour before acceptance.
Read case studyAdelaide, South Australia
Dane
Amorosi
AI Systems & Operational Transformation
I design and orchestrate AI-enabled operational systems with explicit human authority, bounded execution and evidence-driven verification.
What I do
From ambiguity to controlled systems.
- 01
Translate operational problems into system architecture and bounded technical work.
- 02
Define source-of-truth, identity and authority boundaries across software, data and AI components.
- 03
Direct AI-assisted implementation while retaining responsibility for constraints, integration, verification, remediation and acceptance.
- 04
Design human-in-the-loop controls for agentic workflows and consequential automation.
- 05
Use local and distributed AI infrastructure where it improves privacy, resilience, cost or control.
Selected work
Systems in context.
02
Operational systems
AX4 Operational Transformation
A regulated field-service transformation combining source-of-truth and authority design with a working controlled compliance-register and QR-access delivery layer.
Read case study03
Technical delivery
Evidence-Driven Delivery
A contract-first software-factory method that tests whether delegated implementation is reproducible, portable and actually complete.
Read case study04
Local AI infrastructure
Governed Local AI Operator
A local-first assistant model combining practical utility, persistent context and proactive preparation with controlled authority.
Read case study05
Consequential automation
VPP Optimisation & Energy Sight
A staged-autonomy programme separating energy observation, analysis and simulation from any future physical command authority.
Read case study06
Operational product delivery
Vehicle Intelligence Service
From a handful of text messages to an independently verified end-to-end vehicle-intelligence proof in roughly two days — using governed AI-assisted delivery to discover requirements, interfaces and boundaries before building to spec.
Read case studyWorking method
Build to Understand.
“Hold the macro direction. Isolate the important unknown. Bound the smallest useful proof. Build or integrate. Observe actual behaviour. Compare it with the expected result. Let verified reality reshape the architecture.”
AI attribution
Transparent by design.
I use AI implementation tools extensively and transparently. My contribution is strongest in problem framing, architecture, authority design, work decomposition, orchestration, integration, verification, remediation and acceptance. I do not present upstream or AI-generated code as manual line-by-line authorship.