Product lifecycle,rewired for AI.

Before changing anything, we measure what you have: cycle time, review latency, test coverage, defect escape rate, and where engineers actually lose their hours. You get a clear read on which stages of your lifecycle AI can move, which it cannot, and what the realistic gain is.

• —DORA and delivery metrics baseline
• —Developer experience and friction audit
• —AI opportunity mapping across the SDLC
• —Costed adoption roadmap with targets

Coding assistants and agents produce very different results depending on how they are set up. We configure the tooling around your codebase and your product context, conventions, house rules, and pair with your engineers until AI-DLC becomes your daily habit.

• —Assistant and coding-agent rollout
• —Repo context, rules and prompt libraries
• —Hands-on enablement and pairing
• —Internal playbooks and champions

Faster code generation is worthless if review becomes the bottleneck. We put AI to work on the parts of quality that scale badly: test generation, coverage gaps, automated review passes, documentation, and release notes.

• —AI-generated test suites and coverage lifts
• —Automated code review in CI
• —Documentation and release note generation
• —Legacy refactoring and migration at scale

The guardrails that let leadership say yes: which tools are approved, what code and data may leave your perimeter, how AI-authored changes are reviewed and attributed, and how licensing risk is handled. Paired with dashboards that show whether adoption is actually moving delivery, not just tool seat counts.

• —AI usage policy and approved tooling
• —Code, IP and data protection controls
• —Secure review paths for AI-authored changes
• —Adoption and impact measurement