AI Native SDLC Transformation
Your whole team, building at AI speed. Without the mess.
Agile changed how organizations built software. AI changes it again, and further. This isn’t a tool rollout or a session of tips and tricks. It’s your whole software lifecycle, rebuilt for AI, from requirements through release.
AI bolted on
Review
Backing up
Production
More bugs
Lifecycle rebuilt
Where most teams are
AI made developers faster. The organization didn’t change.
AI bolted onto the old process
No new SDLC behind it. Developers each pick their own harness and skill library, or none, and no one has set a direction.
More code brings new problems
Review can’t keep up, and more bugs reach production, with nothing in place to handle either.
The question is too small
“How does this tool make a developer faster?” instead of “How should we build software now?”
What changes
Crawl, walk, run.
Every engagement starts the same way, with requirements first and nobody writing code by hand. From there, each project moves the organization a step further.
Requirements first. AI writes the code.
Requirements first
Meeting transcripts and voice are the inputs. Open business questions go to the business as written assumptions, and stakeholders review and sign off.
Nobody writes code by hand
No one is in an IDE typing. AI writes all of the code, and every change is tested against real systems.
Standards, and roles that fit the new work.
Standards and reference projects
Development standards, a preferred architecture, and examples to build from. Until a polished seed project exists, the first finished project becomes the reference for the next one.
Roles adapt to the new work
Existing roles shift toward the work that matters now, like technical product ownership. People who know the business and the systems it runs on shape the requirements instead of writing code.
The thing that builds the thing.
A system that does the building
A harness, off the shelf or your own, that takes phased requirements and works through them on its own for hours or days.
Issues get a first AI pass
Incoming issues are investigated and, where possible, fixed by AI before an engineer has to look.
All along · Engineering foundations
If a team can’t ship reliably today, AI won’t fix that. Separate non production and production environments, automated tests, CI/CD, and repository access and branch protection. These are table stakes most organizations don’t fully have, and AI makes them impossible to skip.
The work
The work changes. The titles are still settling.
Some of today’s roles blend together or go away, and it’s too early to pretend every title maps neatly to a new one. The work that matters is clear.
Shaping requirements and guiding design
Technical people who communicate well and know the business and its systems. They write the requirements, steer the bigger decisions and review the designs the AI drafts.
Running the build
Directing the agents, or the harness around them, until the work is done. It isn’t magic. Things come up, and someone has to get it over the line.
Setting the foundations
The tech stack, the reference and seed projects, the standards, and who can change what.
Answering for the business
The owners who know the requirements, confirm the assumptions and sign off.
How an engagement runs
Learned on real work.
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1
See where you are
Tools, practices, foundations, and who holds the business answers.
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2
Put the pieces in place
Training for the team, then standards, seed and reference projects, AI skills tailored to your stack, and the system that builds.
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3
Deliver a real project the new way
Your team does the building, with Enso Dynamics working alongside several times a week.
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4
Hand over
The team carries on, with the guides and examples it built along the way.
What you leave with
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A team working the new way, with a delivered project to show for it
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AI skills tailored to your stack, standards and deployment process
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Seed and reference projects for your most common workloads
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Development standards and a preferred architecture, written down
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Requirements and design examples drawn from your own work
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Practice guides for the new kinds of work
Proof
See the method before you decide.
Let’s talk.
Thinking about how your organization builds software with AI?