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NHS Foundation Trust

Build of a AI-Powered CI/CD Triage for an NHS Foundation Trust

Summary:

Our client is one of the United Kingdom’s largest and most respected NHS Foundation Trusts, running two major teaching hospitals in London plus community health services across South London. gravity9 has been building TAP, its Technology Adoption Platform, with the Trust across multiple phases over 3.5 years.

The most recent phase carried the same delivery pressure as any long running NHS engagement: a small team, a wide platform surface to keep tested, and pull requests and CI failures that needed fast, reliable triage without slowing the sprint rhythm down. Rather than treating AI as a productivity add-on for individual developers, gravity9 built AI tooling directly into the delivery pipeline itself, so it could act as a full participant in the loop between a failing build and a resolved, tracked issue.

Technology Stack

  • Cloud Platform: Microsoft Azure (App Services, Azure B2C, Blob Storage)
  • Frontend: React (TypeScript), Material UI (MUI)
  • Backend: Java (Spring)
  • Database: MongoDB
  • Authentication: Azure B2C (ROPC flow, MSAL token injection)
  • CI/CD & Infrastructure: Azure DevOps Pipelines, Terraform (Infrastructure- as-Code)
  • Test Automation: Playwright end-to- end test suite, plus API coverage
  • AI Test Generation & Repair: Claude Code
  • AI Code Review: CodeRabbit, integrated with Azure DevOps
  • Agent-to-Tool Integration: Model Context Protocol (MCP), connecting Claude Code to Azure DevOps, Jira, and Playwright

Our client, one of the UK’s largest and most respected NHS Foundation Trusts, has partnered with gravity9 over 3.5 years to build TAP, its Technology Adoption Platform. In the most recent phase, a small team faced mounting pressure across product, development, and QA, particularly around CI triage, where every failed build meant someone manually scanning logs, reproducing failures, checking Jira for duplicates, and writing tickets by hand. Rather than treating AI as a productivity add-on for individual developers, gravity9 embedded AI tooling directly into the delivery pipeline itself, connecting Claude and Claude Code to Jira, Azure DevOps, and Playwright via MCP integrations so AI could act as a full participant from a failing build through to a resolved, tracked issue.

The most mature result was a self-triggering triage workflow, invoked with a single command, that automatically finds the latest build, parses logs for failed and flaky tests, checks Jira for duplicates, runs parallel AI subagents to investigate each failure, and writes up confirmed bugs as tickets, all without manual intervention. Combined with AI-driven code review and accelerated requirements refinement, the approach is estimated to save around 10 minutes of manual effort per failing test, multiplied across every test investigated in parallel. The client praised gravity9 as “a development partner who goes beyond a transactional relationship,” citing the team’s curiosity, diligence, and ease of collaboration.

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