AI SDLC - AI across the whole software development lifecycle
Your team ships software, but you use AI hit and miss - one tool here, another there? We bring AI into the whole lifecycle: planning, code, review, testing and deployment - as one standard of work, not a bag of tricks.
The problem we solve
Most teams use AI in software development by accident: someone uses an assistant for code, someone else for tests, and the rest of the cycle stays as it was. The result - point speed-ups, but no repeatability, no quality control, and no real jump in delivery pace.
AI SDLC (AI Software Development Lifecycle) means bringing AI into the whole cycle - coherently, measurably, and with quality preserved.
What you get
- AI at every stage of the cycle - from planning and specification, through code generation and review, to testing and deployment.
- Spec-driven as the standard - the specification is the source of truth for people and for agents (we work this way ourselves, including on OpenSpec).
- A human in the loop and quality control - AI takes the volume, an engineer approves and guards the boundaries; no black box.
- A measurable jump in pace - shorter time from idea to deployment, with quality kept or higher.
How it works
- Planning and specification. We set up a spec-driven flow - AI helps lay out and sharpen the scope before any code exists.
- Code with AI. Agents generate and refactor code within the specification, not "by feel".
- Review and tests. AI supports code review and test coverage; the engineer approves, quality stays under control.
- Deployment and maintenance. We close the loop - deployment and maintenance use AI too, and the specification means maintenance is not guesswork.
Who it is for
For teams and companies that ship software and want to genuinely speed up delivery with AI - but in a repeatable way, with quality control, not through accidental tools. We start with one cycle or project and scale on the results.
The fastest return from AI SDLC shows where a team ships a lot and repeatably - then a coherent standard of working with AI multiplies across every project.
FAQ
How is AI SDLC different from using a code assistant?
An assistant speeds up one stage. AI SDLC brings AI into the whole cycle - planning, code, review, testing and deployment - as one repeatable standard, with quality control.
What does "spec-driven" mean and why does it matter?
The specification is the source of truth for people and for agents. Code is created within the specification, not "by feel", so maintenance is not guesswork. We work this way ourselves, including on OpenSpec.
Does AI in the cycle lower code quality?
No. AI takes the volume, and an engineer approves and guards the boundaries; review and tests stay under human control. No black box.
Where do we start?
With one cycle or project; we bring in a coherent standard of working with AI and scale on the results.