AI-Ready Data Platform Accelerating Surgical Innovation

Overview

Carlsmed, a newly public health tech company, needed a unified AI-ready data platform for surgical planning and clinical decision support. ImagineX built a Medallion Architecture on AWS, using Claude Code to accelerate the custom labeling UI, data pipelines, and IaC — with AWS Bedrock powering case summarization via Claude Sonnet and Opus models.

Problem

• Disconnected data sources limited AI model development and clinical insights

• Lacked secure, compliant de-identification and PHI removal workflows

• Manual labeling slowed dataset readiness for ML and LLM use cases

• No platform for centralized enrichment, analytics, or clinical review

• Needed a fast path to MVP to support executive timelines

Solution

ImagineX built a secure AWS Medallion Architecture with automated ingestion from databases and image repositories. Claude Code accelerated the Next.js labeling UI, Terraform IaC, and Python ETL pipelines — cleaning, deduplicating, and de-identifying data. AWS Bedrock with Claude Sonnet and Opus models powers the Case Summarization product for clinical pattern recognition.

Outcome

• MVP delivered on executive speed-to-market timelines, bringing Carlsmed's production-grade AI data platform to market on schedule

• Scalable, clean datasets established for AI, ML, and LLM development across surgical planning and clinical decision support use cases

• Claude Code-built Next.js UI streamlined human-in-the-loop pathology labeling, enrichment, and clinical data exploration

• AWS Bedrock Case Summarization product enables clinical pattern recognition using Anthropic Claude Sonnet and Opus models via a request-based architecture

Services

• Data & AI Innovation

• Enterprise Cloud-Native Engineering

• Agile Delivery & Transformation

Technologies Used

• Claude Code

• AWS Bedrock (Anthropic Claude Sonnet & Opus)

• AWS Glue

• AWS S3 (Delta Lake)

• AWS Step Functions

• Next.js

• Terraform

• Python