Microsoft Copilot (OpenAI)
Microsoft 365 Copilot, Copilot Studio, and Azure OpenAI (GPT-4, GPT-4o, GPT-5 class models) for tenant-grounded productivity and custom agents.
We help organisations choose the right AI tools for the right jobs, with governance and sovereignty designed in from day one.
Multi-model, vendor-neutral expertise
We work across the commercial and open-source AI ecosystem so the recommendation is based on your workload — not a vendor relationship.
Anthropic Claude
Claude Sonnet and Opus for long-context reasoning, document analysis, agentic workflows, and safety-sensitive use cases.
OpenAI ChatGPT
ChatGPT Enterprise and the OpenAI API for general productivity, custom GPTs, and broader knowledge-worker assistance outside the Microsoft tenant.
Google Gemini
Gemini Pro and Gemini for Workspace where Google services are in use, plus Vertex AI for multi-modal and enterprise workloads.
Open-source models
Meta Llama, Mistral, Qwen, and DeepSeek deployed via Azure AI Foundry, Ollama, or self-hosted GPU infrastructure for data-sovereign and cost-sensitive workloads.
Specialist and local models
Whisper for speech, embedding models for search, and small language models (Phi, Gemma) for on-device and edge scenarios.
What we advise on
From strategy through governance to platform selection — the practical decisions behind a credible AI program.
AI strategy and model selection
Vendor-neutral guidance on which AI models, platforms, and licensing fit your data, workloads, sovereignty, and budget — not a single-vendor pitch.
Vendor and platform comparison
Side-by-side review of Copilot, ChatGPT Enterprise, Claude, Gemini, and open-source options against your use cases, integration needs, and risk profile.
AI governance and risk
Acceptable use, data handling, identity, DLP, residency, and review controls aligned to the Privacy Act 1988, Essential Eight, and ISO 27001.
Data and integration readiness
Assess Microsoft 365, SharePoint, Azure, and line-of-business data so AI tools surface the right information with the right permissions.
Use case prioritisation
Identify, score, and sequence AI use cases by value, effort, risk, and reusability — across productivity, automation, and customer-facing workloads.
Build vs buy vs host
Decide when to use a commercial AI service, when to deploy open-source on Azure or your own infrastructure, and when not to use AI at all.
What you receive
An independent AI strategy, not a sales pitch
Every engagement is designed to give you something useful you can act on — even if you choose not to proceed with implementation through us.
- A vendor-neutral view of the AI landscape mapped to your business
- A model and platform shortlist with trade-offs across cost, capability, and risk
- Prioritised AI use cases with effort, value, and dependency ratings
- Data, identity, and integration readiness findings with remediation steps
- An AI governance framework covering acceptable use, data handling, and review
- A staged implementation pathway and decision points for build vs buy vs host
Who this is for
Organisations that want an honest, multi-vendor view of AI before committing to a platform or program.
Leaders evaluating multiple AI tools
You are being pitched Copilot, ChatGPT, Claude, and Gemini and need an independent view of which fits your business.
Organisations with data sovereignty needs
You need to keep sensitive data inside Australia or your own tenant and want to weigh open-source and Azure-hosted options.
Teams beyond the Microsoft stack
You use a mix of Microsoft, Google, and other platforms and need an AI strategy that respects that reality.
Boards setting AI policy
You need a defensible, risk-aware AI position before approving spend, vendor contracts, or production deployments.
How the process works
A clear, staged approach — from discovery, through landscape and readiness, to recommendations and delivery.
01
Discovery call
We discuss your business, current AI activity, data landscape, and the questions leadership is being asked.
02
Landscape and readiness review
We assess data, identity, security, and existing tooling, and map them against the Copilot, OpenAI, Claude, Gemini, and open-source ecosystems.
03
Recommendations and roadmap
You receive a vendor-neutral shortlist, prioritised use cases, governance framework, and an implementation pathway.
04
Enablement and delivery
We help pilot, govern, and deploy the chosen models and platforms — or hand the roadmap to your team or another partner.
Business outcomes
What organisations typically gain from a structured, multi-vendor AI advisory engagement.
Right tool for the job
AI investment is matched to the workload — Copilot for tenant productivity, Claude for long-form reasoning, open-source where sovereignty or cost matters.
Defensible AI governance
A documented framework for acceptable use, data handling, and model selection that leadership and auditors can stand behind.
No vendor lock-in by accident
Conscious choices about where you commit to a single vendor and where you keep optionality through open standards or open-source.
People supported through change
Teams understand which AI tools are approved, what they can and cannot do, and how their work will change.
Related services
AI strategy and advisory sits alongside our agentic AI, managed AI operations, and Microsoft 365 advisory services — including emerging autonomous agents such as Microsoft Scout.
Advisory overviewAgentic AIManaged AI and automationAI readiness assessmentMicrosoft 365 advisoryGovernance and compliance readiness
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Frequently Asked Questions
AI strategy delivers the most value when it is embedded in a technology roadmap that aligns AI investments with your wider IT and business plan.