AI-SDLC · Flagship discipline
We build software the way software will be built.
AI writes code everywhere now. That's not the differentiator — engineering discipline around it is. Our AI software development lifecycle wraps AI-assisted development in the gates that make it enterprise-grade: automated review, automated testing, DevSecOps, GitOps delivery, and observability from the first commit.
The pipeline, stage by stage.
- 01
Plan
Architecture stays humanRequirements, architecture reviews, and threat modeling — with AI accelerating research and drafts, and senior engineers owning every decision that will be expensive to reverse.
- 02
Generate
AI-assisted developmentAI pair-engineering across the codebase: generation, refactoring, test scaffolding. Velocity goes up; the definition of done does not come down.
- 03
Review
Two reviewers, one humanEvery change passes AI code review for defects, security patterns, and consistency — then a human review for judgment, altitude, and taste. Both gates block the merge.
- 04
Test
Coverage that means somethingAI-powered QA generates the test cases humans forget; CI runs them on every commit. Automated testing is the contract that lets everything else move fast.
- 05
Secure
DevSecOps, not DevOopsDependency scanning, secret detection, SAST, and policy-as-code gates inside the pipeline. Security findings are build failures, not backlog items.
- 06
Ship
GitOps release automationDeclarative deployments from version control: what runs in production is what the repository says, every environment reproducible, every release reversible.
- 07
Observe
Reliability engineeringObservability from commit one — metrics, traces, structured logs, SLOs. Production teaches; the pipeline listens and the next iteration starts informed.
What stays human.
Accountability. Architecture. Taste. AI raises the floor of what a team can produce; humans still set the ceiling — and sign their names to what ships. Every gate in the pipeline has a person accountable for it, because "the model did it" is not an acceptable root cause.
Platform engineering
Internal developer platforms that make the golden path the easy path.
Infrastructure automation
The same discipline applied below the app: everything as code, everything reviewed, everything reproducible.
Reliability engineering
SLOs, error budgets, and monitoring that pages people for things that matter — and only those.
Your team can work this way too.
We run SDLC modernization engagements: assess your current lifecycle, design the target, and implement it alongside your engineers — tooling, gates, and culture included.