Our approach
Understand. Prove. Scale.
Our approach is deliberately simple — and repeatable. It lowers risk and creates visible value early.
Understand
Understand before intervening.
AI agents analyze your codebases, tickets, git history, CI/CD pipelines, and delivery process. Code is truth; documentation is opinion. We map the architecture, surface risk, measure flow, and build a fact base your team can act on.
Prove
Show it on real work, not demos.
We take the highest-reward items first: process bottlenecks, system constraints, delivery friction. Measurable change your team can build on.
Scale
Expand what works.
Your internal teams take over delivery on proven rails. Our presence decreases as your team’s capability grows. The AI systems stay — embedded in your process.
The blueprint in detail
The same framework — applied in engagements, taught in the Academy.
Every phase assigns responsibility clearly: what the AI agents deliver, and where humans decide.
| Phase · Agent | AI delivers | Human decides | Productivity |
|---|---|---|---|
| DefineProduct Agent | Research & specs, program plan, epics & stories | Scope, priority | ~60% |
| Design (architecture)Architect Agent | System design, tech specs, patterns | Trade-offs, standards | ~60% |
| Design (UI/UX)Designer Agent | UI/UX specs, prototypes, assets | Experience, usability | ~30% |
| Build (develop)Developer Agent | Implementation plan, code, tests | Logic, quality | ~50% |
| Build (test)QA Agent | Test plan, cases, bug fixes | Risk, readiness | ~70% |
| Ship (deploy)DevOps Agent | IaC, pipelines, monitoring | Rollout, approval | ~40% |
| RunSupport Agent | Knowledge management, customer service, triage | Escalation, resolution | ~50% |