Team or org · 4 to 6 weeks
The SDLC AI Foundations Sprint
Your development process and your AI tools are pulling in opposite directions. This sprint fixes that.
The Offer
Most engineering teams in 2026 are using AI somewhere. The problem is nobody decided how. One engineer lives in Copilot, another is running agents, a third hasn't changed how they work in two years. And the actual development process, the way work gets planned, reviewed, tested, and shipped, wasn't built for any of it.
The result is predictable. AI adoption is individual rather than shared. Output is inconsistent. The team's best engineers have built their own workarounds and everyone else is guessing. For teams that don't yet have a defined workflow, the problem is even simpler: you have a real opportunity to build it right from the start, with AI native to every stage rather than bolted on afterward.
Either way, the fix is the same. We map how your team is actually working, identify where the process is fighting the tools, and redesign the workflow around how AI-native engineering works today. You leave with a shared standard your whole team follows and something measurably better than what you had.
What You Get
Outcomes you can point to when this is over.
Current-State Assessment
An honest look at how your team is actually working: where AI is being used, where it isn't, and where the inconsistency is creating real risk. For teams without an existing process, this becomes the foundation everything else is built on.
AI-Native Engineering Workflow
A redesigned development workflow built around how teams actually work in 2026. Agents, copilots, automated testing, AI-assisted review, all of it designed in, not bolted on. Built for your stack, your team size, and your pace.
Shared Standards
Common standards for how every engineer on the team works with AI tools. Not a policy document. A practical playbook your team actually uses.
Two Working Sessions
The workflow gets built with your team, not handed down to them. Two full-team sessions where we pressure-test the workflow together and make sure everyone is working from the same playbook before we leave.
Documentation
Plain-English process documentation a new hire could follow on day one.
30-Day Check-In
A structured follow-up four weeks after handoff to see what's sticking, what isn't, and where you need a small adjustment.
How It Works
A short, clear path from kickoff to handoff.
Step 01
Discovery
Discovery call and team survey to baseline current AI usage and identify where the process is breaking down.
Step 02
Weeks 1 to 2
Current-state assessment, workflow design, and gap analysis.
Step 03
Weeks 3 to 4
Working sessions to build and pressure-test the workflow with the team.
Step 04
Weeks 5 to 6
Documentation, tooling configuration, and handoff. Then a 30-day check-in.
Best Fit
This is for you if…
- Engineering teams of 2 to 30 people, whether you have an existing process or are building one from scratch.
- CTOs, technical founders, and Directors or VPs of Engineering who own the development process.
- Teams where AI adoption is inconsistent and individual rather than shared and systematic.
- Organizations where shipping velocity has plateaued despite investing in AI tooling.
What This Isn't
We'll point you somewhere better if…
- You want a tool procurement engagement. We work with what you have.
- You need an org-wide transformation across every function, not just engineering. Try The Voyant Method →
- You want a one-time training session with slides and a certificate. Try our training programs →
Proof
What this looks like when it works.
"An education company providing licensure products to professionals was struggling with inconsistent AI adoption across their development cycle. After the sprint, throughput increased 20% and bug counts dropped 18%."
Education company, licensure products
FAQ