Inconsistent output
Different engineers give the same assistant different context, constraints, and acceptance expectations.
AIDLC Team Launch Sprint
Bring AI coding into one real repository with controls your team can actually use.
Move from individual experimentation to a shared way of working. Over a focused 10-business-day engagement, your team defines boundaries, configures repository guidance, adds review and evidence gates, and practices the workflow on a real change.
One team
One repository
One real workflow
Model-agnostic
When the sprint helps
Your team is using AI, but the operating rules still live in individual habits.
Use the sprint when code arrives faster but review takes longer, prompts and context vary by person, security boundaries are unclear, or no one can show what evidence supported an AI-assisted release.
Review burden
Generated changes look plausible, but reviewers spend extra time finding scope creep, weak tests, and hidden assumptions.
Unclear accountability
The team has tools, but not a shared policy for data, approvals, verification, release ownership, or learning.
What your team receives
A working AI delivery baseline, fitted to one repository.
The exact scope is confirmed before work begins. The sprint focuses on practical controls your team can maintain after the engagement ends.
Readiness and repository review
Map current tools, change types, risks, instructions, review flow, and the places where rework occurs.
Repository assistant instructions
Define scope, prohibited actions, tool boundaries, verification expectations, and escalation rules.
AI coding policy
Clarify approved uses, data handling, human accountability, dependency review, and production controls.
Workflow and review gates
Route Brownfield, Greenfield, and Config/Infrastructure work through appropriate product and engineering checks.
Evidence and release templates
Capture intent, tests, human review, risks, acceptance, monitoring, release ownership, and rollback decisions.
Team working session
Practice the operating model on a real change and leave with a focused 30-day improvement plan.
How the engagement runs
Move from discovery to a team-owned workflow in four steps.
Align
Choose the repository, team, tools, risks, and one representative change.
Configure
Create the repository instructions, policy, workflow route, and review artifacts.
Practice
Run one real change through framing, generation, verification, acceptance, and evidence.
Hand over
Review what worked, assign owners, and agree on the next 30 days of adoption.
Who it is for
Built for teams that already have AI coding access and need a reliable way to use it.
Engineering leaders
Create one operating model across tools and teams without forcing a vendor-specific process.
Product and delivery owners
Keep generated work connected to measurable intent, acceptance, ownership, and release decisions.
QA, security, and operations
Make verification, data boundaries, risk review, monitoring, and rollback visible before release.
A practical fit for growing teams
Train your team without funding a large transformation program.
If you are a small or midsize company, start with one team, one repository, and one real change. Your team leaves with practical controls it can continue using independently.
Common questions
Know what the first conversation covers.
Which tools can you support?
Claude, Codex, GitHub Copilot, Kiro, Windsurf, internal assistants, or a mixed tool environment. The controls remain model-agnostic.
Do you need production access?
No. Scope and data-handling boundaries are agreed before work begins. Never send credentials or sensitive production data through the site form.
What happens after the sprint?
Your team owns the instructions, workflow, review artifacts, and improvement plan. Ongoing advisory support can be discussed separately when useful.