Service
AI Platforms & Delivery for Regulated Industries
In short: we design and run GxP-ready AI platforms on AWS for biotech and pharma teams — Bedrock, SageMaker, RAG and agents — delivered through our AI Delivery Lifecycle. It is for regulated organisations that need AI to pass validation and explain what it did.
Two things stop AI projects in life sciences: platforms that can't pass validation, and delivery methods that can't explain what the AI did. We solve both — GxP-ready AI platforms on AWS, delivered through our AI Delivery Lifecycle.
Discuss Your AI ProjectAI Solutions for Life Sciences
In the rapidly evolving life sciences sector, AI and machine learning are no longer optional—they're essential for staying competitive. We help biotech and pharmaceutical companies use advanced AI to accelerate discovery, reduce costs, and improve patient outcomes.
From custom model development to production deployment, our team brings deep expertise in both AI/ML technologies and the unique requirements of regulated industries.
What We Deliver
AI Platforms
- Bedrock and SageMaker architecture
- RAG over regulated document sets
- Agentic workflows with guardrails and action logging
- Model registry, versioning and validation packs
- Private-VPC inference
- Cost controls per model
AI Delivery Lifecycle (ADLC)
- Governed prompting and IaC generation
- Stop-before-deploy human checkpoint
- Drift & conformance function
- Assurance & telemetry aggregation
- Evidence pack for CSV / IQ-OQ-PQ
Real-World Applications
See how AI is transforming life sciences research and development
Drug Discovery Acceleration
Use ML models to predict molecular interactions and identify promising drug candidates faster.
Clinical Trial Optimisation
Analyze patient data to optimize trial design, improve recruitment, and predict outcomes.
Regulatory Document Automation
RAG-assisted drafting of CSV, SOPs and submission content with full source traceability.
Why Choose skyPurple for AI & ML
Domain Expertise
We understand the unique challenges of life sciences—from regulatory compliance to data privacy requirements in clinical settings.
End-to-End Support
From proof-of-concept to production deployment, we guide you through every phase of your AI journey.
Cloud-Native Architecture
Build scalable, cost-effective AI solutions on AWS, Azure, or Google Cloud with best-in-class MLOps practices.
Security & Compliance
Every model and pipeline is built with security and regulatory compliance at its core—no compromises.
Frequently Asked Questions
Expert answers to common questions about AI and machine learning in life sciences.
What is generative AI for life sciences?
Generative AI for life sciences refers to AI models — including large language models (LLMs) and diffusion models — applied to biotech and pharmaceutical challenges. Common applications include drug candidate generation, protein structure prediction, clinical document summarisation, and automated literature review. skyPurple Cloud deploys these solutions on AWS using services including Bedrock, SageMaker, and Comprehend Medical.
How does AWS support AI and machine learning in regulated industries?
AWS provides a suite of managed ML services designed for regulated workloads, including Amazon SageMaker for model training and deployment, AWS Bedrock for foundation models, and Amazon Comprehend Medical for clinical NLP. These services include built-in access controls, audit logging, and encryption at rest and in transit — critical for GxP and HIPAA-compliant environments.
What is GxP-compliant AI and why does it matter?
GxP (Good Practice) compliance in AI means that AI systems used in drug development or manufacturing are validated, auditable, and operate according to documented procedures. Regulatory bodies including the FDA and EMA require that any software influencing regulated decisions follows validation principles. skyPurple builds AI systems with this requirement from day one — including audit trails, model versioning, and change control documentation.
How long does it take to build and deploy an AI solution in life sciences?
A proof-of-concept typically takes 2–4 weeks; a validated production deployment typically 6–12 weeks depending on data readiness and validation scope.
Is AI in life sciences subject to regulatory scrutiny?
Yes. The FDA and EMA have both published guidance on AI/ML-based software as a medical device (SaMD) and algorithmic decision-making in clinical settings. skyPurple designs AI systems with regulatory readiness as a core requirement — including audit trails, model versioning, change control, and validation documentation.
What is the AI Delivery Lifecycle (ADLC)?
The AI Delivery Lifecycle is our four-stage method for using AI safely in regulated environments: Scope, Orchestrate, Stop-Before-Deploy, and Run. It governs how prompts are written, how infrastructure and code are generated as IaC, and how a mandatory human checkpoint reviews output, checks for drift and conformance, and captures validation evidence before anything is deployed.
How do you stop AI-generated code reaching production unreviewed?
Every change passes a Stop-Before-Deploy checkpoint. AI-generated infrastructure and code are produced under governed prompts, opened as a reviewable change, checked for drift and conformance against the validated baseline, and only released once a named human has reviewed and approved it. The review record and evidence pack are retained for audit.
Does the EU AI Act apply to our internal AI tools?
It can. The EU AI Act applies to AI systems placed on the EU market or used in the EU, and internal tools are in scope where their outputs affect people or regulated activities. Most life-sciences back-office tools fall in the limited-risk tier with transparency duties, while some clinical or pharmacovigilance uses reach high-risk. We map your tools to their likely tier as part of ADLC.
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