Case Studies

These examples show how focusing on the right workflows and trusted signals reduced release risk, shortened feedback cycles, and restored delivery confidence for software teams.

Investment Management Platform (London)

  • Delivery risk: Lengthy manual regression meant releases were slow, expensive, and often delayed.
  • What changed: The most critical investment workflows were identified and protected with fast, reliable API-level signals.
  • Outcome: Regression time dropped from five days to fifteen minutes, for the release gating regression slice, giving the team a predictable release decision backed by automation they trusted.

Implementation details: API automation integrated into Azure DevOps pipelines, designed for stability and maintainability.

Automated API regression summary showing a stable and repeatable test run

WealthTech SaaS Vendor (United States)

  • Delivery risk: Weekly releases relied on slow feedback and growing manual verification of key customer journeys.
  • What changed: Core user journeys were identified and given fast, end-to-end signal that ran on every change.
  • Outcome: Execution time dropped to under one minute, and the team gained consistent confidence in production releases without expanding low-value coverage.

Implementation details: Browser-based automation integrated with CI to provide rapid, repeatable feedback on critical workflows.

Traffic Control Platform (Global Vendor)

  • Delivery risk: Multiple teams were working in parallel with limited visibility into which changes affected safety-critical behaviour.
  • What changed: Automated checks were tied directly to real requirements and failure modes, making impact visible across teams.
  • Outcome: Release confidence improved as teams could clearly see which behaviours were protected and what a failure meant.

Implementation details: End-to-end automation linked to requirements and defects, providing traceable signals across delivery.

Public Sector Housing Platform (Proof of Concept)

  • Delivery risk: A complex legacy platform made browser automation brittle and expensive to maintain.
  • What changed: Critical user journeys were clarified first, exposing accessibility and testability issues early.
  • Outcome: The client could make informed refactoring and automation decisions instead of compounding technical risk.

Implementation details: Proof of concept automation combined with accessibility checks to assess economic testability before scaling.

Who this is for: founders, CTOs, and delivery leaders accountable for release decisions where confidence has become slow, noisy, or assumed.