Complement 1

Complement 1 delivers personalized lifestyle modification programs for cancer patients. Manual clinical decision trees limited real-time personalization, requiring 24–48 hour review cycles and constraining patient scale beyond 200.

Adherence rates were 65%, with $180K/month in medical oversight costs. The platform needed to enable adaptive, compliant, and real-time personalized health recommendations at scale.

• Amazon Bedrock (Claude 3.5 Sonnet) for real-time generative inference.
• Amazon SageMaker for continuous learning pipelines and reinforcement learning.
• Amazon Textract for OCR extraction from medical PDFs.
• AWS Lambda & Step Functions for orchestration with sub-2-minute inference.
• DynamoDB for high-speed storage of patient recommendations.

Multi-AZ VPC with public subnets for API Gateway, private subnets for Lambda & SageMaker, and isolated Bedrock inference. Real-time data flow: S3 → Textract → Bedrock RAG → SageMaker prediction → DynamoDB. Lambda + Step Functions orchestrate AI workloads with <2 minute latency.

• Personalization latency reduced from 24–48 hours to 1.8 minutes (96% improvement).
• Clinical oversight reduced 75% — from 8 hours per 200 patients to 2 hours.
• Patient adherence increased from 65% to over 90%.
• 5x patient volume enabled without additional manual intervention.
• TCO reduction of 65%.