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 open-source models via Ollama and enterprise foundation models via Amazon Bedrock, all from a single GPU-ready workspace, without any workload leaving its own AWS environment.
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Business Challenges
The university wanted to democratize access to AI technologies while simplifying infrastructure management and maintaining institutional governance.
01
Supporting Multiple AI Model Types
Researchers needed a single platform that supported both open-source models running locally on GPU-enabled infrastructure with Ollama and managed commercial models such as Claude and GPT through Amazon Bedrock.
02
Simplifying AI Infrastructure
Setting up GPU drivers, AI frameworks, model runtimes, and development tools required specialized expertise. Research IT needed AI-ready environments so researchers could start immediately without manual configuration.
03
Flexible GPU Computing
Researchers needed to provision GPU-enabled infrastructure on demand and choose the right GPU configuration for each workload, balancing performance with cloud cost optimization.
04
Secure Enterprise AI Access
Commercial AI services had to be securely accessed through Amazon Bedrock, with centralized control over available foundation models and all API calls remaining within the university's AWS environment.
05
AI Cost Visibility
Research administrators needed visibility into both infrastructure and Amazon Bedrock API costs, with AI expenses attributed to individual research workspaces and included in overall project cost reporting.
Solution
Relevance Lab deployed Research Gateway with a new AI Workspaces catalog offering that enabled researchers to provision fully configured AI development environments through a self-service portal.
Researchers provisioned AI-ready workstations on demand through Research Gateway.
- Browser-based remote desktop
- CPU and GPU-enabled Amazon EC2 instances
- Pre-installed NVIDIA GPU drivers
- Standardized software images
- Persistent research storage
- Self-service lifecycle management
Every workspace was automatically configured during provisioning, enabling researchers to begin work immediately.
Each AI Workspace deployed Ollama as a Docker container for running open-source LLMs locally.
- Local API endpoint
- Private in-workspace inference
- Support for multiple open-source models
- No external AI dependencies
Researchers selected their preferred model during provisioning, which was automatically installed and configured.
Every workspace included a standardized AI development environment with pre-installed research tools.
- Python
- R
- JupyterLab
- RStudio
- Git
- Ollama
- OpenWebUI
OpenWebUI provided a browser-based interface for interacting with locally hosted AI models.
Research Gateway securely integrated Amazon Bedrock, providing governed access to institution-approved foundation models.
- IAM-based model access
- Institution-approved foundation models
- Support for Claude and other Bedrock models
- API requests remained within the university's AWS environment
Research Gateway automatically mounted approved project datasets into every AI Workspace.
Researchers securely accessed governed research data while leveraging existing permissions, project isolation, and institutional governance.
Research Gateway extended financial reporting to include Amazon Bedrock API consumption alongside infrastructure costs.
- Infrastructure costs
- GPU compute costs
- Amazon Bedrock API costs
- Workspace operating costs
- Project-level AI spending
Administrators gained complete visibility into the total cost of AI-enabled research.
Architecture Highlights: Unified AI Research Platform
Researchers authenticate through Research Gateway and provision AI Workspaces using institution-approved catalog offerings. Each workspace combines GPU infrastructure, local AI model execution, enterprise AI services, and research collaboration tools within a secure cloud environment.
Self-service AI workspace provisioning
Browser-based remote desktop access
GPU-enabled compute options
NVIDIA GPU drivers pre-installed
Ollama runtime for open-source models
OpenWebUI browser interface
JupyterLab and RStudio integration
Python and R development environments
Study folder integration
Amazon Bedrock access through IAM roles
Institution-approved model governance
Workspace-level Bedrock API cost tracking and project-level cost reporting
Business Outcomes
Researcher Productivity
- Rapid provisioning of AI-ready workspaces
- Immediate access to GPU-enabled computing
- Simplified experimentation with multiple foundation models
- Reduced environment configuration time
- Faster onboarding for AI research projects
Flexible AI Innovation
- Run open-source models locally with Ollama
- Browser-based AI interaction through OpenWebUI
- Enterprise AI services through Amazon Bedrock
- Integrated JupyterLab and RStudio environments
- Secure access to institutional research data
Operational Efficiency
- Self-service workspace provisioning
- Standardized AI workstation images
- Automated software installation
- Centralized governance of Amazon Bedrock models
- Reduced manual infrastructure support
Financial Visibility
- Monitor Amazon Bedrock API usage by workspace
- Allocate AI costs to research projects
- Track GPU utilization
- Improve cloud budgeting
- Optimize AI infrastructure spending
Customer Perspective
“
Research Gateway enabled the University to rapidly deliver enterprise-grade AI capabilities to our researchers without introducing unnecessary operational complexity. By combining open-source models, Amazon Bedrock, GPU computing, and self-service provisioning into a single platform, we have accelerated AI adoption while maintaining governance, cost visibility, and institutional security.
— University Research Computing 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 open-source models with Research Gateway, giving researchers GPU-ready AI workspaces on demand—with full governance and cost visibility built in.
