Meridian Health partnered with GitPlumbers to stabilise a compliance-critical Angular platform. By addressing state management, testing, and rollout discipline in parallel, the team eliminated emergency freezes and hit every audit milestone.
Challenge
An AI-generated Angular application powered compliance workflows but shipped regressions with every release, forcing quarterly code freezes.
Approach
- Mapped the dependency graph and prioritised brittle modules blocking regulatory launches.
- Replaced zone-heavy state management with Signals and component stores to reduce change detection thrash.
- Built a CI/CD guardrail suite with smoke packs, contract tests, and accessibility snapshots.
- Implemented staged rollouts with feature flags tied to observability dashboards for instant rollback.
Outcomes
- Release cadence moved from quarterly to bi-weekly without incident spikes.
- P1 defect volume dropped by 72% across clinical intake workflows.
- Compliance backlog cleared four weeks ahead of the audit deadline.
- 72%fewer production incidents after guardrails were deployed.
- 4xincrease in deploy frequency without expanding the team.
- 0regulatory violations during the next audit cycle.
Northstar Systems engaged GitPlumbers to stabilise a federated React platform. The team combined telemetry, module federation, and progressive delivery to unlock tenant growth without compromising reliability.
Challenge
Enterprise tenants overloaded the AI scaffold that powered a React control plane, causing cold starts, memory leaks, and fractured delivery cadences.
Approach
- Instrumented tenant-specific performance telemetry and created persona heatmaps for load testing.
- Split the monolith into federated modules with a shared design system and versioned contracts.
- Introduced progressive delivery with automated rollbacks, synthetic checks, and guardrail alerts.
Outcomes
- Cut cold start latency by 68% and steadied p95 response times during peak usage.
- Enabled parallel squad delivery with isolated pipelines and contract testing.
- Enterprise NPS climbed 18 points within two release cycles.
- 68%reduction in cold start latency for premium tenants.
- 18ptincrease in enterprise NPS after the rollout.
- 100%of releases shipped with automated rollback coverage.
Copperline Logistics needed to modernise a mission-critical Vue and Laravel platform without disrupting daily fulfilment. GitPlumbers established cohesive UX patterns, automated regression barriers, and executed staged migrations that kept operations live.
Challenge
Operational tooling built with AI pair programming accumulated conflicting UX patterns and brittle workflows that slowed fulfilment.
Approach
- Audited UX debt, accessibility gaps, and duplicated APIs across the suite.
- Implemented a design system with story-driven development and snapshot coverage.
- Staged the migration of critical workflows to new services backed by progressive data migrations.
Outcomes
- Maintained 99.98% uptime during the entire migration programme.
- Support tickets tied to workflow confusion dropped by 61%.
- New engineers onboarded twice as fast thanks to consistent patterns.
- 99.98%uptime maintained during phased releases.
- 61%reduction in support requests tied to workflow errors.
- 2xfaster onboarding for new engineers into the platform.
TechCorp Industries engaged GitPlumbers for a comprehensive diagnostic review of their AI-scaffolded platform. Our deep codebase analysis uncovered critical technical debt patterns, security vulnerabilities, and architectural risks—providing a clear roadmap to transform their fragile MVP into a production-hardened application ready for enterprise scale.
Challenge
A rapidly growing startup's AI-generated MVP had evolved into a complex codebase with mounting technical debt, architectural inconsistencies, and concerning dependency patterns that threatened scalability and maintainability.
Approach
- Conducted comprehensive static analysis across 450K lines of code to identify technical debt hotspots and code smells.
- Generated dependency heatmaps revealing critical coupling issues and over-reliance on deprecated packages.
- Applied AI-artifact detection algorithms to surface untested, fragile code patterns likely generated by AI assistants.
- Performed security audit on 187 dependencies, flagging 23 critical vulnerabilities and 15 deprecated packages.
- Analyzed code complexity metrics, identifying 34 modules with cyclomatic complexity above safe thresholds.
- Created risk scoring matrix combining test coverage, dependency age, complexity, and change frequency.
Outcomes
- Identified 156 instances of duplicate logic across components, reducing bundle size opportunity by 18%.
- Surfaced 23 security vulnerabilities requiring immediate attention, with clear remediation paths.
- Discovered 12 critical paths where changes could cascade failures across 40+ modules.
- Found 67 AI-generated functions lacking error handling, input validation, or edge case coverage.
- Mapped 8 architectural bottlenecks preventing horizontal scaling under load.
- Delivered prioritized remediation roadmap with effort estimates and business impact analysis.
- 23critical security vulnerabilities identified in dependency chain.
- 67AI-generated code artifacts flagged for review and hardening.
- 156code duplication instances mapped for consolidation.
- 18%potential bundle size reduction through deduplication.
