AI SDLC, CI/CD & Quality Gates
AI speeds up how fast code gets written. Without a delivery pipeline to match, that just means defects reach production faster. We build the CI/CD backbone, quality gates, and release workflows that turn AI-generated velocity into reliable shipping, so speed and confidence move together.
Formatters, linters, analysers, tests, and AI review run automatically, so nothing reaches main unchecked.
Typical window to stand up branch protection, quality gates, and automated review on an existing codebase.
Quality gates catch regressions in CI instead of in production, where they are far more expensive to fix.
Pipeline foundations
- Branch protection and PR-cycle policy
- CI/CD pipeline design or overhaul (GitHub Actions, GitLab CI, Azure DevOps)
- Build, test, and deploy stage orchestration
- Environment promotion and release workflows
- Rollback and incident-recovery patterns
Quality gates
- Formatter, linter, and static-analyser integration (ESLint, dotnet format, etc.)
- Coverage thresholds and test-result gating
- Security scanning (SAST, dependency, secret detection)
- Performance and backward-compatibility checks
- Merge policies that enforce the gates
AI in the loop
- Automated AI code review on every pull request
- Human escalation paths for AI-flagged changes
- AI-assisted test generation wired into CI
- Repository-specific review heuristics and prompts
Operations
- Delivery metrics dashboard (cycle time, change-failure rate, MTTR)
- Flaky-test detection and quarantine workflow
- Pipeline documentation and runbooks
- Onboarding so the team owns the pipeline after handover
Diagnostic
Review your current pipeline and delivery flow. We return the highest-leverage gaps in your CI/CD and quality gates, plus a sequencing plan to close them.
Hands-on Demo
Walk through a fully gated pipeline in a real repo: branch protection, automated checks, AI review, and release flow. Shows what trustworthy delivery looks like before you commit.
Sprint
Hands-on build of your CI/CD pipeline and quality gates, with AI review and test automation wired in, validated against your real codebase.
Embedded Retainer
A dedicated senior consultant maintains and evolves the pipeline alongside your team: new gates, tooling upgrades, and delivery-metric reviews. Monthly time-and-materials.
Best fit
- Teams adopting AI coding tools without a pipeline to validate the output
- Engineering teams with slow, manual, or inconsistent release processes
- Organisations where defects routinely surface in production, not CI
- Teams that want quality gates owned in-house, not outsourced indefinitely
Not a fit
- Teams unwilling to enforce branch protection or merge policies
- Projects with no tests and no appetite to build coverage
- Organisations looking for a one-off audit with no implementation
Engineering AI Adoption
B2B SaaS · Series B · 15-person engineering team · APAC
A software company wanted to adopt AI across engineering in a practical way, but needed the right workflows, training, governance, and rollout model to make it useful and compliant.
Read case studyEnterprise AI architectureEnterprise AI Platform
Global professional services firm · 5,000+ employees · multi-jurisdiction
A large enterprise needed a secure, governed way for employees to use LLMs internally without exposing sensitive information or relying on uncontrolled public tools.
Read case study01We already have CI. Why do we need this?
Most teams have a build-and-test pipeline but not a set of enforced quality gates calibrated for AI-assisted development. The gap shows up as inconsistent review, no coverage thresholds, flaky tests nobody trusts, and AI-generated code merging without scrutiny. We harden what you have rather than replace it.
02Which CI/CD platforms do you work with?
We are platform-agnostic and work with GitHub Actions, GitLab CI, Azure DevOps, CircleCI, and others. We adapt to your existing stack rather than pushing a migration, unless a migration is clearly warranted.
03How does AI code review fit into the pipeline?
AI review runs automatically on every pull request as a first-pass reviewer: it flags likely defects, missing tests, and policy violations before a human looks. Humans stay in the loop for judgement calls and final approval. The goal is to cut review time, not remove human accountability.
04Will the team be able to maintain it after you leave?
Yes. Every engagement includes documentation, runbooks, and hands-on onboarding so your engineers own the pipeline. We deliberately avoid building anything that requires us to stay.