A leading higher education institution supports researchers working with large, often sensitive datasets across finance, economics, healthcare, marketing, public policy, and organizational behavior. As AI coding assistants such as Claude Code and Codex transformed software development and data science, the institution sought to improve researcher productivity without compromising data security.
Institutional policy required sensitive research data to remain within the organization's AWS environment, preventing the use of externally hosted AI services. The institution also needed centralized governance over AI model access and detailed visibility into Amazon Bedrock usage costs to accurately allocate AI spending by researcher, project, and workspace.
Relevance Lab deployed a secure AI-enabled research platform by integrating Amazon Bedrock with Research Gateway, enabling researchers to use enterprise AI coding assistants while maintaining institutional security, governance, and financial accountability.
CASE STUDY • PDF
AI-Enabled Research Workspace Blueprint
A deep dive into how one university gave researchers self-service access to Claude Code, Codex, and open-source AI models, while keeping every workload inside its own AWS environment.
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Business Challenges
The institution wanted to make modern AI coding assistants available to researchers while ensuring institutional control over research data and cloud costs.
01
Secure AI-Assisted Development
Researchers wanted to use AI coding assistants such as Claude Code and Codex to accelerate software development, statistical analysis, and data engineering. However, confidential research data could not be exposed to AI services operating outside the university's AWS environment.
02
Governed AI Model Access
Research IT needed centralized control over which Amazon Bedrock foundation models—including Claude models—were available to researchers, ensuring consistent governance and institutional policy compliance.
03
Self-Service AI Infrastructure
Researchers needed the flexibility to launch AI-ready compute on demand, without waiting on manual provisioning from Research IT. The platform had to support GPU-enabled workstations while staying simple enough for researchers to self-provision.
04
Cost Attribution
Unlike traditional infrastructure costs, generative AI usage introduces variable API charges based on token consumption. The institution required detailed visibility into Amazon Bedrock usage costs so that AI expenses could be attributed to individual researchers, research workspaces, and projects alongside traditional infrastructure costs.
05
Standardized Research Environments
Every AI workspace needed a preconfigured software stack so researchers could begin work immediately while ensuring research data remained within the organization's AWS environment and AI usage complied with institutional governance.
Solution
RelevanceLab deployed a customized Research Gateway platform accessible exclusively to authenticated university users.
Each research project was provisioned in its own dedicated AWS account, providing strong isolation, centralized governance, and project-level cost management.
Research Gateway was extended with secure AI workspace offerings including:
- Claude Code with Amazon Bedrock
- Codex with Amazon Bedrock
Researchers provisioned AI workspaces through a self-service portal without requiring administrator support.
Each workspace delivered a fully configured AI development environment, ready for immediate use.
- Browser-based remote desktop using Amazon DCV
- CPU and GPU-enabled Amazon EC2 instances
- Pre-installed NVIDIA GPU drivers
- Claude Code pre-installed
- Python, R, Conda, Git, and research tools
- Persistent Research Gateway Study folders
- Secure institutional authentication
Researchers simply signed in and began AI-assisted development within a standardized, governed environment.
Every workspace securely connected to Amazon Bedrock through IAM-based access controls.
- Approved Claude models
- Approved foundation models
- Model availability
- IAM access policies
All inference requests remained within the university's AWS environment, ensuring secure and governed access to foundation models.
Research Gateway unified infrastructure and AI usage reporting in a single dashboard.
- Bedrock API costs by researcher
- Bedrock API costs by workspace
- Bedrock API costs by project
- GPU and infrastructure costs
- Total workspace operating costs
Administrators gained complete visibility into the cost of AI-assisted research across projects.
Architecture Highlights
Researchers authenticate to the private Research Gateway portal and provision AI-enabled workspaces through approved catalog offerings. Each workspace runs within its own project AWS account, providing strong isolation between research programs.
Private institutional portal
Claude Code & Codex integration
Study folder data management
Dedicated AWS account per research project
Amazon Bedrock foundation model access
Integrated Bedrock API cost attribution
Self-service AI workstation provisioning
Centralized AI model governance
Project-level cost reporting
Browser-based Amazon DCV remote desktop
IAM-based least-privilege permissions
GPU-enabled compute options
Business Outcomes
Researcher Productivity
- Rapid provisioning of AI-ready workspaces
- Accelerated code development and data analysis
- Consistent environments across research teams
- Reduced environment setup time
- Immediate access to GPU infrastructure
Secure Enterprise AI Adoption
- All AI inference remained within the AWS environment
- Centralized governance of approved AI models
- Secure IAM-based access controls
- Project-level isolation
- Institutional control over AI adoption
Operational Efficiency
- Self-service workspace deployment
- Standardized AI workstation images
- Centralized software maintenance
- Simplified GPU infrastructure management
- Reduced manual provisioning effort
Financial Visibility
- Allocate AI costs to individual researchers
- Monitor AI usage across projects
- Track workspace operating costs
- Improve budget forecasting
- Optimize cloud and AI spending
Customer Perspective
“
Research Gateway enabled the Business School to adopt AI-assisted software development in a secure and governed manner. By combining Claude Code and Codex with Amazon Bedrock, our researchers can leverage state-of-the-art coding assistants while keeping sensitive research data within the organization's AWS environment. The platform has simplified onboarding, improved visibility into AI usage costs, and significantly reduced operational overhead for Research IT.
— Research IT Leadership
Research Gateway: Built for Modern AI Research
Research Gateway is Relevance Lab's cloud-native platform for secure research computing, AI-enabled environments, and scientific collaboration. It helps universities, healthcare organizations, government agencies, and research institutions deploy governed cloud workspaces, integrate enterprise AI services like Amazon Bedrock, and give researchers self-service access to compute and AI tools, all while keeping costs visible and meeting institutional security and compliance requirements.
We combine deep AWS platform expertise with hands-on experience in research computing, helping institutions standardize AI-ready workspaces, reduce day-to-day dependency on Research IT, and accelerate discovery without compromising governance.
AI-ENABLED RESEARCH CASE STUDY
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See how Relevance Lab combined Amazon Bedrock and Research Gateway to give researchers access to Claude Code and Codex, without sensitive data ever leaving the university's AWS environment.
