AI-driven SDLC on your existing pipeline
Delivery accelerates without the defect rate or the audit trail paying for it, through executable specs, quality gates and traceable pipelines. We work in your repositories, from requirements to operation.
AI amplifies the delivery system it lands on
Adoption happens developer by developer, and tools arrive faster than the workflows around them, so prompts, reviews and releases stay manual and unrecorded. Quality gates and governance were designed for human-paced output, and generated code passes through them at a volume they were never sized for.
AI-driven SDLC turns scattered tooling into an operating model that spans requirements, code, testing and operation. The question is not which assistant your teams use, but whether your delivery system can measure what it changes. Once specs, gates and telemetry are in place, speed, quality and traceability move together rather than trading against each other.
Every stage of the lifecycle, amplified
The same DevOps cycle your teams already run, with AI execution and quality gates built into each stage and a human in the loop wherever the decision carries risk.
Plan
Ambiguity detected in requirements, change impact analysed before commitment
Code
Contextual generation, AI-assisted reviews and continuous refactoring
Test
Tests generated from changes, quality gates enforced in the pipeline
Release
Traceability from requirement to deployment, ready for audit
What the engagement includes
Four workstreams delivered with your teams, inside your repositories and your pipeline, not in a parallel lab. Not for teams without version control, CI and a shared backlog in place; we improve a delivery system, we do not create one.
AI-enabled SDLC design
AI integration defined stage by stage, workflows that combine human and agent execution, and the high-impact use cases identified before anyone touches a repository.
Spec-driven development
Structured specs as the single source of truth, so agents generate code and tests with business context and full traceability from requirement to commit.
Automation and quality by design
Quality gates and automation inside the pipeline, with AI-supported testing and feedback loops aligned with your Quality Engineering practice.
What working together looks like
- Duration. Three phases: SDLC maturity assessment, pilot with one product team, then scale across squads
- Team. SDLC architects and engineers working inside your squads, alongside your leads
- What we need from you. Access to your repositories and CI/CD pipeline, one product team for the pilot, and the owners of quality and governance
- What you take away. A maturity baseline, automated workflows running in your pipeline, quality gates for generated code and an audit trail per change
- Not included. No licence procurement, no model training or fine-tuning, no migration of your source control or ticketing, no parallel lab environment
Measure your SDLC before changing it
Two working sessions with your engineering leads. You leave with a maturity baseline, the workflows worth automating first and a sequenced path.
Book the SDLC assessment