Environment build time cut to two days.
Secure ML on anonymised data, by design.

Every new environment was a manual, one-off build.

Previous cloud projects relied on manual deployment. That produced inconsistent architectures, configuration drift and slow, error-prone environment builds, inhibiting governance and slowing time to insight in a sector where both matter.

The engineering organisation also lacked experience in infrastructure-as-code and DevSecOps toolchains, and in the specific regulatory and data-handling constraints of healthcare and insurance. Product teams needed a platform that enforced policy automatically and let them experiment with anonymised healthcare data without exposing sensitive information.

Three constraints were non-negotiable:

• Manual deployments produced inconsistent, drifting architectures
• No in-house experience with infrastructure-as-code or DevSecOps
• Sensitive health and insurance data ruled out unsecured experimentation

An Azure platform that deploys itself, securely.

A strategic Azure cloud platform, designed against the Microsoft Well-Architected Framework, with accelerators that wrap native Azure services, including AI and machine learning, so product teams consume them rather than build from scratch. Terraform delivered the infrastructure as code, letting teams provision into secure spokes with network segmentation, role-based access and separation of duties built in.

Automated CI/CD pipelines ran sandbox through to production, with compliance-as-code gates checking policy and security controls before any deployment could progress. Anonymisation patterns and data handling controls let machine learning run against de-identified datasets while preserving auditability and traceability for regulators.

To close the skills gap, a customer success function delivered a white-glove onboarding service: reference templates, workshops and hands-on coaching in Terraform modules and secure deployment practices. Our Fractional Head of AI led the technical alignment between the platform’s accelerators and the data science workflows, so ML tooling was consumable and repeatable from day one.

Two days, not months.

The strategic Azure platform turned a slow, manual, skills-constrained process into a repeatable, two-day deployment model, embedding governance and security into everyday workflows while giving the organisation the confidence to run machine learning on real healthcare and insurance data without exposing it.