Case Study: Modernising and automating Defence simulation infrastructure with AI-driven orchestration

Client: Department of Defence, Joint Collective Training Branch

Project: Defence Training and Experimentation Network (DTEN)

How Profectus is modernising a Defence simulation environment into a modular, automated and continuously monitored platform, and building a locally hosted LLM and RAG orchestration tool that turns natural language exercise descriptions into infrastructure builds.

Client and context

The Joint Collective Training Branch is responsible for designing and delivering joint collective training for the Australian Defence Force, using the Defence Training and Experimentation Network (DTEN) as its simulation environment. DTEN is a complex, federated environment comprising live, virtual and constructive components integrated across compute platforms, simulation hosts, network infrastructure, middleware, data services and specialist peripherals, deployed across classified and unclassified domains.

The digital infrastructure behind these high-fidelity exercises had grown over time on established systems and hands-on processes. As training tempo increased, Defence set out to scale it further into an environment that could be rapidly deployed, securely managed and continuously improved.

The engagement

Profectus is engaged to modernise and sustain the infrastructure underpinning DTEN, transforming it into a modular, automated environment that can be rapidly deployed and continuously improved. The remit spans rebuilding simulation software into modular packages, establishing CI/CD automation, virtualising legacy platforms, standing up integrated monitoring, and developing a locally hosted, AI-driven orchestration capability, all delivered within disciplined engineering and sustainment governance aligned to Defence operating constraints.

The challenge

Modernising a live, high-tempo training environment places several demands on delivery:

  • Complex, evolved estate: a bespoke mix of applications and infrastructure built up over time, with system knowledge often held informally rather than centrally documented
  • Manual builds: each simulation instance required repeated setup from scratch and specialist knowledge, which limited scalability and consistency
  • Limited integrated visibility: no single monitoring view across environments to support proactive management
  • Requirements-to-build gap: no automated way to translate high-level operational requirements into executable technical builds

Our approach

Profectus applies a systems engineering approach to modernise the environment across four coordinated workstreams.

Modular packaging and CI/CD

The team is rebuilding key simulation software into modular Red Hat RPM packages, has established CI/CD automation with Ansible Tower and Git, and maintains tailored standard operating environments for RHEL 7, 8 and 9. Custom pipelines automate environment creation from scenario inputs, reducing manual effort and improving consistency and repeatability.

Virtualisation and monitoring

Infrastructure is being migrated from physical to virtualised platforms using VMware ESXi, VSCA and VSAN across multi-node, multi-domain deployments. Integrated monitoring and alerting stacks using Grafana and Prometheus provide real-time insight and historic trend data, giving full-stack visibility across every environment.

AI-driven orchestration

Profectus is developing a locally hosted LLM-driven orchestration tool that converts user-submitted exercise descriptions into build proposals by referencing internal documentation via retrieval augmented generation (RAG). Once approved, the system triggers the infrastructure pipeline, effectively enabling natural language driven environment builds. This capability is being tested and integrated incrementally.

Federation engineering and modelling

Across DTEN, Profectus aligns federation and environment engineering to the Distributed Simulation Engineering and Execution Process (DSEEP) and supports Joint Live Virtual Constructive (JLVC) style environments. The team maintains a structured, maintainable model of the environment, maps dependencies and configuration states, and aligns modelling to the Joint Event Life Cycle (JELC) so environments can be reliably prepared, executed, reset and sustained.

What we have delivered so far

  • Rapid deployment: automated build processes have reduced environment deployment time from days to under an hour
  • Sustained exercise support: a range of simulation exercises, from small-scale rehearsals to full-spectrum operational scenarios, supported on the new infrastructure over a 12-month period across classified and unclassified domains
  • Full-stack visibility: dashboards providing complete visibility across all environments, improving proactive management and reducing unplanned outages
  • Legacy modernisation: established applications now operating reliably in virtualised environments with full integration
  • Operational AI prototype: a locally hosted AI orchestration tool operating in prototype form, accurately generating infrastructure configurations from user input and reducing planning effort

Systems and methods

Red Hat RPM · Ansible Tower · Git · VMware ESXi, VSCA and VSAN · Grafana and Prometheus · Locally hosted LLM with RAG · DSEEP · JLVC · JELC

The result

This platform is positioning Defence for future readiness, enabling greater exercise frequency, improved technical reliability and faster turnaround between scenarios. By automating infrastructure and enabling AI-assisted orchestration, Profectus is reducing technical overhead while empowering non-technical users to drive outcomes, and is combining disciplined, DSEEP-aligned federation engineering with repeatable automated deployment to sustain reliable, repeatable training on DTEN. The work is ongoing, with the environment continuing to scale to future needs and adapt to new technologies.