Cloud and AI Services Built for Life Sciences

We offer three core specialisations: Generative AI & ML (custom model development, predictive analytics for drug discovery), Cloud Cost Optimisation (reducing AWS spend by typically 30–45% without compromising compliance), and Cloud Security & Compliance (GxP, HIPAA, SOC 2, 21 CFR Part 11). We also provide cloud migration, DevOps, data engineering, and cloud strategy consulting.

Yes — if your systems are used in GxP-regulated processes, migrating to AWS constitutes a significant change requiring computer system validation (CSV) activities. This includes producing updated IQ, OQ, and PQ documentation. However, skyPurple Cloud’s migration methodology is designed to minimise validation scope by preserving validated configurations where possible and generating change control documentation throughout.

A typical life sciences cloud migration takes 3–9 months depending on scope, complexity, and validation requirements. A single-system migration with validation can take 8–12 weeks. A full data centre migration with multiple GxP systems may take 6–12 months. skyPurple Cloud provides a detailed project plan during the discovery phase with realistic timescales tailored to your specific environment.

Yes — skyPurple Cloud is an AWS Select Tier Consulting Partner, having demonstrated technical competency, customer references, and ongoing investment in AWS certifications. Our team holds AWS Solutions Architect Professional and AWS Security Specialty certifications, alongside GxP and compliance expertise specific to the life sciences sector.

Yes. While AWS is our primary platform and where we hold AWS Select Tier Consulting Partner status, we are also a certified GCP partner. We regularly deliver multi-cloud architectures for clients who need specific GCP capabilities alongside their primary AWS environment. We provide cloud-agnostic advice and always recommend the platform that best fits your technical and regulatory requirements.

Our FinOps engagements typically begin with a 2-week cloud cost audit covering Reserved Instance utilisation, rightsizing opportunities, idle resource identification, and storage class optimisation. We then present a prioritised savings roadmap with estimated savings per recommendation. Most clients achieve 30–45% cost reduction within 90 days of implementation.

The AI Delivery Lifecycle (ADLC) is how we use AI to scope, generate, test and run cloud infrastructure and code in regulated environments. It has four stages: Scope, Orchestrate, Stop-Before-Deploy, and Run. The defining feature is the mandatory Stop-Before-Deploy checkpoint, where a human reviews AI-generated infrastructure and code, runs drift and conformance checks, and captures validation evidence before anything reaches production.

Yes, provided the output is governed and reviewed. AI-generated infrastructure and code can enter a validated environment when it is produced under controlled prompts, passes a human stop-before-deploy review, is version-controlled as infrastructure as code, and carries the change-control and validation evidence your auditors expect. Our ADLC is designed so AI accelerates delivery while the validated state is preserved.