Submit a requirement and get enriched stories. Open VS Code and generate codebase-aware code. Describe an app and watch it appear. Every step governed, tracked, and policy-compliant.
AI in the SDLC means embedding AI across the entire software delivery lifecycle — beyond autocomplete. It spans AI-assisted development, AI code review, AI for software testing, and AI release management — all governed and policy-compliant. We don't just build this for clients; we run it on our own delivery, which is how we know where it accelerates teams and where it needs guardrails.
It builds on our agentic AI and AI engineering practice, and ties into the reliability and observability work of our SRE team.
Turns enriched requirements into codebase-aware code, with full repository context.
Auto-generates unit tests and test data, and runs AI code review on every push.
Handles release notes, change intelligence, and policy-compliant, auditable deploys.
Watches production and correlates incidents via our Resilient Operations Center, feeding learnings back.
Describe what you want to build. The platform generates the UI, data model, and logic. Iterate in plain language until it works exactly how you need it — then the generated code moves straight into the engineering workflow for production hardening.
No. Copilot-style tools autocomplete inside the editor. AI in the SDLC embeds AI across the whole lifecycle — requirement enrichment, codebase-aware generation, automated testing, AI code review, release management, and production feedback — with governance, audit trails, and policy checks on every step. Autocomplete is one small part of a much larger, governed system.
Requirement-to-user-story enrichment, codebase-aware code generation (full repo context via a VectorDB), auto-generated unit tests and test data, AI code review on every push, release notes and change intelligence, and AIOps-driven production feedback — all governed and policy-compliant. It complements our agentic AI and AI engineering work.
It varies by team and codebase, but we typically see raw requirements turned into dev-ready stories in under two minutes, 60–80% of unit tests auto-generated, and materially faster review and release cycles. The gain comes from compounding small accelerations across the lifecycle — not a single silver bullet — which is why we run the platform on our own delivery too.
We'll map the platform to your codebase, engineering workflow, and compliance requirements — and define what a deployment looks like for your organisation in a single discovery call.