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Built for Scale, Security, and Performance

AI Platforms & Enablement

Every team has its own OpenAI key, security cannot say what AI is allowed to reach, and the one pilot that worked has nowhere safe to grow. We assess your readiness honestly, then build the Azure AI platform layer that fixes it: Foundry done properly, governed from day one.

Readiness · Platform · Guardrails

What Makes an Organisation Ready for AI?

Most AI programmes fail before the first prompt is written, on questions nobody thought to ask: can our data be trusted enough to ground on, can our security posture contain what AI is allowed to reach, and do we have anywhere safe for teams to build? Readiness is those three answers, and licences come a distant fourth.

Our readiness assessment scores your estate honestly across data grounding, identity and networking, governance, FinOps and skills, then hands you a prioritised gap list with costs attached. No hundred-page maturity theatre; a working document your platform team can execute.

The platform build that usually follows is Azure AI Foundry done properly: landing-zone aligned, private endpoints and customer-managed keys where residency demands it, Entra identity throughout, quotas and cost controls per team, and evaluation and tracing wired in before the first workload ships. Azure OpenAI models are our default engine, with the platform designed so model choice stays flexible as the market moves.

The result is a place where experimentation is safe and production is boring: dozens of models and agents hosted on one governed platform, instead of sandbox OpenAI resources multiplying on corporate credit cards. That platform is also what our multi-agent and Copilot extensibility work stands on, so readiness investment pays across every AI route you choose.

Azure AI platforms, built on Microsoft reference architecture

Data & AI on Azure
Data & AI on Azure
Infrastructure (Azure)
Infrastructure (Azure)
Digital App Innovation (Azure)
Digital App Innovation (Azure)
Security
Security
What We Deliver

Key capabilities

AI Platform Architecture on Azure & Secure Model Deployment

Azure AI Foundry architecture that fits your landing zone and your security posture, supporting every workload from first experiment to regulated production.

  • Azure AI Foundry hubs and projects with RBAC per team
  • Private endpoints, customer-managed keys and regional model choice
  • Evaluation and tracing wired in before the first workload
  • Deployed as infrastructure-as-code from day one

Integration with Enterprise Systems and Monitoring Tools

Connect the platform to what your business already runs: Fabric, Databricks, Dynamics 365, line-of-business APIs and the observability stack your operations team actually watches.

Cost Control, Performance Optimisation & MLOps Solutions

Quotas, tagging and model routing that keep token and GPU spend visible per team, so the finance conversation happens before the invoice arrives, not after.

AI DevOps for Continuous Integration and ML Workflow Management

Ship AI workloads the way you ship software: infrastructure-as-code, promotion gates between environments and deployments that do not depend on one person's laptop.

6 wks

From assessment to a production-grade AI platform plan

100%

Built on Azure AI Foundry and Microsoft Responsible AI

3

Readiness pillars scored: data, security, skills

1

Unified platform for experimentation and production

Common Use Cases

Where AI Platforms & Enablement earns its keep

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.

How We Work

A proven delivery approach

  1. 01 Step

    Assess

    Review current AI workloads, Azure landing zone, security and FinOps maturity to benchmark readiness for enterprise AI.

  2. 02 Step

    Design

    Architect Azure AI Foundry, networking, identity, secrets, observability and DevOps patterns aligned to your standards.

  3. 03 Step

    Build

    Deploy via infrastructure-as-code, onboard the first workloads and establish guardrails, quotas and cost controls.

  4. 04 Step

    Operate

    Run the platform with ongoing model, cost and security management, optionally through Synapx-as-a-Service.

FAQ

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.

Our Clients

Trusted by

Glassmoon
Lanware
Micheldever Tyre Services
Midwich
Mount Anvil
Nuevo Partners
Pro Global
Seras Energy
Skanska
Ocean Conservation Trust
WA Comms
Glassmoon
Lanware
Micheldever Tyre Services
Midwich
Mount Anvil
Nuevo Partners
Pro Global
Seras Energy
Skanska
Ocean Conservation Trust
WA Comms
Client Voices

Hear from our clients

Video Stories

Skanska testimonial video
Skanska

David, Skanska

Project/Programme Manager

Mount Anvil testimonial video
Mount Anvil

Mike, Mount Anvil

Head of Technology Applications

Testimonials

Book a platform readiness assessment

We will score your estate across data grounding, identity, governance, cost control and skills, then hand you a prioritised, costed gap list your platform team can start on immediately.

Book a platform assessment