Case Studies | Healthcare & Life Sciences

The platform for the care sector, built on AWS

A Healthcare & Life Sciences client

About

A Healthcare & Life Sciences business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The production engagement had three focus areas.

Reasoning beyond statistical matching

Traditional score matching compares numbers and stops there. The advisor had to identify risk interactions that statistical matching misses, consider peer dynamics across a home's residents, and surface those factors, all while giving a clear, auditable explanation behind every single match with no unexplained recommendations.

Production-grade security for sensitive data

The system handles information about young people in care, so it demanded encryption at rest and in transit, least-privilege IAM,

Reliability and scale under real use

Moving from a demo to daily operation meant handling concurrent placement requests without degradation, controlling the cost and rate of the underlying model calls, and recovering cleanly from failures rather than dropping a request.

Solution

Cloud Combinator retained the validated core from the proof of concept and wrapped it in the network, security, resilience and observability layers a production system needs, all deployed repeatably through AWS CloudFormation across development, staging and production.

85%

Target agreement with care professionals, across at least 50 scenarios (target)

120s

Target response for up to 20 homes of 10 residents (target)

99.5%

Target WebSocket availability in business hours (target)

By the numbers:

  • 85% - Target agreement with care professionals, across at least 50 scenarios (target)
  • 120s - Target response for up to 20 homes of 10 residents (target)
  • 99.5% - Target WebSocket availability in business hours (target)
Changes

The proof of concept was completed and accepted, demonstrating the AI reasoning approach in practice. The production phase is scoped against a clear set of acceptance thresholds, shown below as targets, that define what the hardened system must achieve before sign-off.

  • Explainable by designEvery recommendation carries clear reasoning, an alternative and the individual home analyses as an audit trail, with the professional making the final call.
  • Risk-awareThe advisor is built to catch risk interactions and peer dynamics that statistical matching alone would miss, the specific value the concept proved.
  • Secure and UK-residentEncryption at rest and in transit, least-privilege IAM, Cognito authentication, WAF, KMS and CloudTrail, with data kept in the eu-west-1 and eu-west-2 regions and none retained between invocations.
  • Resilient under loadSQS with a dead-letter queue, retries with exponential backoff and a circuit breaker on Bedrock, targeting 50 concurrent requests at under an one percent error rate.
  • HandoverRepeatable CloudFormation deployments, dashboards and alarms, plus runbooks and knowledge-transfer sessions so the client can operate the system independently.

With a validated concept now being hardened for daily use across the estate, the client has an AI advisor that is not just a demonstration but a production system, ready to support care professionals in one of their most consequential decisions.

AWS Stack

Amazon Bedrock

For the Claude model that analyses each home and synthesises the final placement recommendation.

AWS Lambda

For the orchestration engine that scores, preprocesses and streams results inside the VPC.

Amazon API Gateway

For the authenticated WebSocket API that streams analysis to the user in real time.

Amazon SQS

With a dead-letter queue for reliable request handling and failure capture.

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