Healthcare•9 months

Medical Library AI Context Protocol Server

Medical research institution struggling with AI model performance and high computational costs for large medical library queries

Client

Medical Research Institution

Duration

9 months

Team Size

11 AI specialists

The Challenge

A leading medical research institution had built an AI system to help researchers query their vast medical library, but the system was inefficient and expensive. AI models were processing too much irrelevant context, leading to slow responses and high computational costs. Researchers were frustrated with query times and accuracy.

Our Solution

We implemented a sophisticated Model Context Protocol server using AWS EKS for orchestration, Amazon Bedrock for AI services, and Amazon OpenSearch for intelligent document indexing. The system intelligently manages context to provide relevant information to AI models while minimizing computational overhead.

Implementation Approach

1Medical document analysis and context mapping
2Model Context Protocol server development on EKS
3Integration with Amazon Bedrock for AI capabilities
4OpenSearch implementation for intelligent indexing
5Context optimization algorithms development
6Scalable container orchestration setup
7Comprehensive monitoring and performance optimization

Technologies Used

Amazon BedrockAWS LambdaAmazon S3Amazon OpenSearchAWS EKSAWS Fargate

Results & Impact

✓75% reduction in AI inference costs
✓3x faster query response times
✓Improved AI accuracy for medical queries
✓Scalable context management for 500K+ medical documents
✓90% improvement in researcher satisfaction

“The MCP server revolutionized how our researchers access medical knowledge. Fast, accurate, and cost-effective AI assistance is now at their fingertips.”

— Director of Research, Medical Institution

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