AI at every stage of the lifecycle. Not just in the editor.
Most teams add AI to one step — usually coding — and leave the rest of the lifecycle unchanged. We bring AI into every stage: requirements review, architecture mapping, code generation, test creation, security scanning and ongoing maintenance. SpurTree engineers run each stage in ADIS, so every stage writes to the same traceability grid and every handover has an approval behind it.
You're in the right place if…
- Engineering organisations whose AI adoption has stalled at coding assistants
- Teams where requirements, tests and security findings live in disconnected tools
- Regulated or audit-conscious businesses that need to show why each change was made
- Delivery leaders who want consistent quality gates across several teams or products
What gets in the way.
Ambiguous requirements reach the build
Specs move into development before they are clear, and the gaps surface weeks later as rework.
Architecture is reviewed after the fact
Design decisions are discovered in pull requests rather than agreed up front, so the build starts from a guess.
Documentation drifts from the code
Nobody knows when the requirements docs were last accurate, yet people keep making decisions on them.
Security and maintenance are bolted on
Scanning and debt work happen at the end, in batch reports, instead of alongside every change.
AI-Enabled SDLC, end to end.
Requirements
Raw notes and tickets become signed-off, traceable specs through review-and-refine loops and multi-stage approval by BA, product and architect.
Architecture and design
Each requirement is decomposed into the capabilities, components and data it touches, then approved by a technical architect before build.
Coding
Discovery agents index the repository; coder agents generate interfaces, DTOs, tests and wiring as a reviewable diff.
Testing
Test cases are generated in bulk from acceptance criteria, scaffolded into automation scripts and tracked on a quality dashboard.
Security
OpenGrep, SonarQube and CodeQL run automatically, with incremental fingerprinting so only new findings resurface.
Maintenance and observability
Technical debt is surfaced and prioritised across repositories, and runtime traces link live behaviour back to the originating requirement.
A delivery approach you can plan around.
Map your current lifecycle
We look at how work moves today — tools, handovers, approvals — and where it breaks down.
Connect and index
We connect ADIS to your repositories and, where you use them, Jira or Azure DevOps, so nothing has to be replaced.
Switch on stages with gates
We introduce AI stage by stage, configuring who approves what at each step and per environment.
Run and refine
Our team runs the lifecycle with you, adjusts standards and gates as we learn, and keeps the traceability grid current.
Ways to work with us.
Lifecycle pilot
Start with one project and the stages that hurt most, through your own approval gates.
spurtree POD
A dedicated team runs all seven stages for your roadmap inside ADIS.
Maintenance-led engagement
Scan-driven work on an existing codebase — security, debt and fixes — typically priced lower than new development.
Technologies we work with
- ADIS
- Jira
- Azure DevOps
- OpenGrep
- SonarQube
- CodeQL
AI-Enabled SDLC: common questions.
Which stages of the SDLC do you cover?
Seven: requirements, design, build, testing, maintenance, observability and security scanning. Each one writes into the same traceability grid.
Do we need to adopt every stage at once?
No. We usually start with the stages causing the most friction and add others once the gates are working for your team.
Can our developers keep their coding assistants?
Yes. Assistants help individuals with exploratory work; the AI-enabled SDLC handles the recurring delivery pipeline that has to be traceable afterwards. Most teams run both.
How is our code and data kept separate?
Access is scoped to a single project, each project has its own knowledge index, and each run executes in an isolated worktree with its own credentials. The security architecture is available for review.
Let's talk about your ai-enabled sdlc needs.
We'll reply within one business day with demo slots, and ask a couple of questions so the demo uses a scenario close to yours.