Built for Scale, Security, and Performance
AI Platforms & Enablement
AI only succeeds when it can scale. We design secure AI platforms on Azure that support experimentation, deployment, and ongoing optimisation without creating technical or governance debt.
Azure AI platforms, built on Microsoft reference architecture
Data & AI on Azure 
Infrastructure (Azure) 
Digital App Innovation (Azure) 
Security Key Capabilities
AI Platform Architecture on Azure & Secure Model Deployment
Design and implement secure, scalable AI platform architectures on Azure that support the full model lifecycle from experimentation through to production deployment.
Integration with Enterprise Systems and Monitoring Tools
Connect your AI platform seamlessly with existing enterprise systems, data sources, and monitoring tools to ensure AI solutions work within your established technology ecosystem.
Cost Control, Performance Optimisation & MLOps Solutions
Implement cost management strategies and performance optimisation practices alongside MLOps solutions that keep your AI platform efficient, responsive, and budget-aligned.
AI DevOps for Continuous Integration and ML Workflow Management
Establish AI DevOps practices that bring continuous integration and automated workflow management to your machine learning operations, ensuring reliable and repeatable AI delivery.
From assessment to a production-grade AI platform plan
Built on Azure AI Foundry and Microsoft Responsible AI
Typical saving on AI compute through right-sizing
Unified platform for experimentation and production
Where AI Platforms & Enablement drives value
Azure AI Foundry platform build
Deploy a secure, landing-zone aligned Azure AI Foundry environment that data science, app development and business teams can share safely.
AI readiness assessment
Evaluate data, identity, networking, compliance and FinOps readiness for AI, with a prioritised roadmap to close the gaps.
Scaling from PoC to production
Replace ad-hoc notebooks and sandbox OpenAI resources with a governed platform that can host dozens of models and agents safely.
AI FinOps and cost control
Bring token, GPU and capacity spend under control with tagging, quotas, model routing and continuous optimisation.
Private networking and sovereignty
Implement private endpoints, customer-managed keys and regional deployments for clients with strict data residency requirements.
Integration with enterprise systems
Connect the AI platform cleanly to Fabric, Databricks, Dataverse, Dynamics 365, line-of-business APIs and your observability stack.
A proven delivery approach
- 01 Step
Assess
Review current AI workloads, Azure landing zone, security and FinOps maturity to benchmark readiness for enterprise AI.
- 02 Step
Design
Architect Azure AI Foundry, networking, identity, secrets, observability and DevOps patterns aligned to your standards.
- 03 Step
Build
Deploy via infrastructure-as-code, onboard the first workloads and establish guardrails, quotas and cost controls.
- 04 Step
Operate
Run the platform with ongoing model, cost and security management — optionally as a fully managed service.
Frequently asked questions
What does an AI readiness assessment cover?
Data, identity, networking, security, governance, FinOps, skills and operating model. The output is a scored assessment against a Microsoft-aligned readiness framework, a prioritised remediation plan and an order-of-magnitude cost estimate for the AI platform build.
How long does a readiness assessment take?
Typically 3–5 weeks, including stakeholder workshops, technical deep-dives and an executive read-out. We keep the business time commitment light — most analysis happens behind the scenes.
How long to build the platform itself?
A production-ready Azure AI Foundry platform with guardrails, CI/CD and first workloads typically takes 8–14 weeks to build. Subsequent workloads land in days rather than weeks once the foundation is in place.
Can the platform host Copilot Studio agents and custom models together?
Yes. We design for a mix of Copilot Studio, Azure OpenAI, open-source models and bespoke ML so teams can pick the right tool for each use case within a single governed environment.
How do you control AI cost?
Through workload tagging, capacity quotas, model routing (e.g., to cheaper models where appropriate), token budgeting and continuous FinOps monitoring. Clients moving from ad-hoc OpenAI usage to a managed platform typically see 30–50% cost reduction.
Is the platform suitable for regulated data?
Yes. We design AI platforms with private endpoints, customer-managed keys, Purview integration, Entra identity and audit logging so they meet financial services, healthcare and public sector requirements.
Ready to Get Started?
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