AI Infrastructure & Automation
AI that runs inside your systems, on infrastructure you can trust.
Most AI work stalls between the pilot and production: access to data, permissions, integration, logging and operations. That is an infrastructure problem, and it is the one we solve.
- Microsoft Partner
- AZ-104 and AZ-305 certified engineers
- Senior engineers only
- Brisbane-based, working across Australia
Where production AI usually breaks
- Data access: content spread across SharePoint, file servers and line-of-business systems, with permissions that do not map cleanly
- Retrieval: RAG that ignores document-level permissions, or returns stale and duplicated content
- Identity and secrets: model and tool calls running with more privilege than any user should have
- Integration: no route from a proof of concept into the ticketing, ERP or workflow systems that matter
- Evaluation and logging: no way to measure quality, or to reconstruct what a model did and why
- Operations: nobody owns uptime, cost, model change or rollback
What we build
- Secure landing for AI workloads in Azure: network isolation, private endpoints, managed identities, key management, policy
- Permission-aware retrieval over internal content, with access enforced at query time
- Agents and workflow automation with scoped tool access and human approval steps where the action matters
- Evaluation harnesses, prompt and model change control, audit logging and cost controls
- AI-assisted infrastructure operations: alert triage, evidence gathering, change drafting and runbook execution under review
How it runs
- Pick one workflow where the value is clear and the data is reachable, and define what good output looks like.
- Build the infrastructure, retrieval and controls first, then the model layer on top.
- Run against real data with evaluation and human review, and adjust before wider rollout.
- Hand over to operations, or run it as part of Managed Engineering.
Our own use of AI
North Ark uses the same patterns internally: investigation, context gathering, planning, execution, verification and audit under engineer control. That experience is why the operational side of AI, not just the model side, is where we are most useful.
Questions buyers ask
Where should we start with AI?
With a workflow where the data is available, the permissions are clear and the result can be checked. Often that starts with getting Microsoft 365 data ready.
Is our data safe to use with AI?
Only if permissions and labels are right. The Copilot and AI data readiness assessment shows what AI could reach before anything is switched on.
Do you build AI agents?
Yes, with approvals and logging built in, running on infrastructure you control.
Is our data used to train models?
No. We use services and settings that exclude your data from model training, and keep it inside your tenant where possible.
Do you sell AI licences or run training courses?
No. We engineer the infrastructure, data access and automation, and work alongside whoever handles licensing and adoption.
Start a conversation
Get AI out of the pilot and into operations.
Tell us which workflow you want running in production.
Talk to an Engineer