Without system
Individual AI usage
Teams use AI individually. They gain occasional speed on specific tasks, but the team doesn't change the way it works.
Back to missions
Transform
Your teams so they define and orchestrate cutting-edge systems with governance.
Competitive advantage
What makes this mission possible
There is a measurable difference between teams that use AI individually and those that orchestrate it as a system.
With system
Orchestration as a system
AI is installed as the team's system. Governed and continuously improving, the team learns, shares patterns, and improves the way it works.
Methodology
How we solve it
We install in your team the methodology, tools, and governance system needed for AI to stop being an individual resource and become a collective asset.
We do it by working on your real project, with your architecture and your conventions.
01
We design the agent ecosystem
Specific to your stack: skills per layer, quality hooks, and sub-agents for planning, architecture, and learning.
02
We train the team on the real project
We train the entire team in a workshop on real project cases, not on generic examples.
03
We install Basic Governance
Everyone knows what AI generates, what requires human review, and how the system is continuously improved.
04
Internal AI Champion
We train and certify an internal AI Champion per team who maintains and evolves the system without depending on us.
What remains when we finish
Operating system
An AI system installed in your team, versioned and governed.
Internal AI Champion
A team member trained to maintain and evolve the system.
Active continuous improvement
The agent learns from each task and proposes skill improvements that the AI Champion validates.
Real independence
The team never goes back to working the old way and only turns to Diverger when it wants to make another leap.
Impact
Reference metrics
Results extrapolated from our own teams.
80%
Active team adoption
80% of the team uses the system regularly within the first 3 months.
−45%
Fewer rejected PRs
Reduction in the rejection rate of AI-generated pull requests.
−60%
Fewer lint and type errors
With active hooks, generated code arrives with much higher quality.
>40%
Reflection Rate
By month 3, the system plans using the accumulated task memory from the project.
What's your challenge?
Tell us how your team works today and we'll propose the right entry point.
