Security
AI operates within the same security controls as the rest of your architecture.
Back to missions
Scale
AI with reliable governance.
Competitive advantage
What makes this mission possible
When AI starts generating real results in one team, the next question is inevitable: how to ensure it works across the entire organization with the same standards of security, quality, and regulatory compliance.
Growing without that system isn't scaling: it's multiplying risk.
Quality
Code and AI-generated decisions follow consistent technical standards across all teams.
Regulatory compliance
Every AI use is traceable, reviewable, and aligned with the organization's regulatory requirements.
Methodology
How we solve it
We design and deploy the governance model that allows agentic AI to operate at scale in your organization, with full traceability, cost control, and human checkpoints defined by risk level.
The model scales from one team to hundreds of developers without losing coherence or control.
01
Corporate Control Plane
All AI traffic centralized, audited, and budgeted per team and sprint. Spending becomes predictable and anomalies detectable.
02
Versioned Plugin Registry
Each project's AI knowledge is managed like any other software asset: with versions, a review process, and rollback available.
03
Human-in-the-loop by risk level
We define what can be automated and what requires mandatory human review based on the potential impact of each action.
04
Knowledge Governance
The project's institutional memory as a protected asset, with an owner, validation process, and retention policy.
What remains when we finish
Governance architecture
An AI governance operating system: control plane, plugin registry, and measured maturity model.
Full visibility
The organization knows what AI does, who uses it, and at what point in the process.
Cost and risk control
Every action has traceability, associated cost, and a defined review level.
AI Champions network
An internal network of AI Champions with defined roles and governance cadences.
Impact
Reference metrics
Results extrapolated from applying the model in our own teams.
100%
AI traffic coverage
All AI traffic goes through the corporate proxy with full traceability.
±15%
Token spend control
Maximum deviation from the token budget per team or sprint.
90%
Changes reviewed before release
Most skill changes go through a review process before being published.
6–12 months
Governance maturity
Estimated time to reach an advanced maturity level with the deployed model.
Specialization
Platforms that facilitate adoption
105 min/week Average savings per user
+40% Time savings on everyday office tasks
We deploy Gemini Enterprise on your real processes: agents connected to your data, governance from day one.
For organizations that want to scale AI with real control. Without depending on a specific suite.
What's your challenge?
Tell us where your organization stands and we'll evaluate together the governance model you need.
