1. Baseline and planning
We assess codebase health, architecture constraints, and delivery bottlenecks to define measurable goals.
Approach
AI speed with engineering guardrails built in.
Our methodology is designed to prevent the common failure mode of AI-first development: shipping quickly but accumulating fragile systems. We move fast, but with expert supervision at every meaningful checkpoint.
2. AI-assisted execution
Implementation teams use AI to accelerate routine coding while staying aligned to design and domain context.
3. Senior oversight and quality gates
Senior engineers drive reviews, test strategy, and release decisions to prevent short-term speed from creating long-term risk.
4. Release and knowledge transfer
We finalize documentation, monitor release behavior, and transfer ownership without disruption.
Quality controls that keep delivery sustainable
- ✓Architecture decisions are reviewed and documented.
- ✓AI-generated changes pass the same quality bar as manually authored code.
- ✓Security, reliability, and performance checks are part of CI.
- ✓Team knowledge growth is treated as an explicit project deliverable.