SDLC ^ AI
Your engineers already write code with AI. It’s time the rest of your software delivery lifecycle caught up.
SDLC ^ AI moves you beyond pilots with four accelerators that run inside the tools your team already has. Built for engineering teams moving beyond AI experiments, it keeps agents within your architecture, review process and security standards from the first sprint.
Work with a focused 4-6 week sprint to prove AI in your engineering environment.
ChatGPT made AI look easy.
Type a prompt, get a working prototype in an hour. That first impression was misleading.
Most of those prototypes never make it to production. They stay on someone’s machine, outside the normal review process and security controls, until they’re forgotten. Or until they find their way into production without the oversight they needed.
Speed without a system is chaos with a head start.
Four accelerators
for every stage of software delivery
Most engineering leaders are working through the same four problems. SDLC ^ AI gives you something you can deploy for each one.
AGENT KIT
How do we ship faster without losing quality
Production-ready agents for QA, security, documentation and release notes, without building custom agents from scratch.
SDLC AI HARNESS
How do we let engineers use AI without losing control of our standards
The governed overlay that keeps agents inside your architecture rules and security standards. Works inside Cursor, Copilot and Codex, so governance fits naturally into existing workflows. |
AI SPEND CONTROL
How do we control what AI actually costs us
An AI gateway that routes, logs and governs every model call, giving engineering leaders visibility into usage and costs before invoices become surprises.
AI ADVISORY
How do we help our teams adopt AI in the way they actually work
Wizeline engineers embed with your team, working against your real backlog so new practices stick beyond the engagement.
It runs on your stack, not ours.
SDLC ^ AI is configured within your existing environment, so most teams are up and running in days.
Most AI delivery platforms ask you to adopt their ecosystem before you can get started. That usually means procurement, security reviews and onboarding before a single AI agent delivers value.
- Works within your existing engineering environment, with most teams live in days
- Runs inside Cursor, Copilot and Codex, with no tool migration required
- Model-agnostic, with no proprietary platform lock-in
- Your proprietary code never trains our models, as a contractual commitment
- Every agent we build in your environment stays yours
It runs on your stack, not ours.
SDLC ^ AI is configured within your existing environment, so most teams are up and running in days.
Most AI delivery platforms ask you to adopt their ecosystem before you can get started. That usually means procurement, security reviews and onboarding before a single AI agent delivers value.
- Works within your existing engineering environment, with most teams live in days
- Runs inside Cursor, Copilot and Codex, with no tool migration required
- Model-agnostic, with no proprietary platform lock-in
- Your proprietary code never trains our models, as a contractual commitment
- Every agent we build in your environment stays yours
What customers are seeing
Higher productivity
Productivity gain — Fox Local, on its Braze migration
4 people covering the work of 16
A NewsCorp hackathon team replaced manual Figma prototyping with AI-assisted design based on real product requirements. Discovery time fell from six weeks to two while a four-person team delivered work that previously required sixteen.
Faster velocity
No added headcount — a GitHub Copilot-equipped quality engineering team
Fewer no-shows
MiSalud, applying the same underlying approach to its patient workflow
Tools don't change,
how a team works.
Enablement does.
We test new ways of working in a controlled environment, build the business case with real delivery data and help internal champions scale what works.
- Help engineers adopt new ways of working without disrupting delivery
- Measure adoption and engineering impact from the start
- Drive behaviour change at scale across your engineering team
- Improve adoption with continuous feedback from engineering teams
- Keep teams current as AI tools evolve
Know the cost before you commit to anything.
Map where AI will truly add value. No commitment required.
Every engagement starts with a Free Engineering Diagnostic. We map your SDLC, your toolchain and your security posture, then tell you where AI agents create value and where they don’t. You’ll leave with a clear picture of where AI can help, and where it isn’t worth the investment.
The AI Adoption Sprint
If the diagnostic shows a clear opportunity, the next step is the AI Adoption Sprint: a focused four- to six-week engagement where we work alongside your team on a real backlog item in a sandbox environment. You’ll finish with an adoption roadmap and a practical plan for expanding engineering capacity.
