Make AI useful, visible, and accountable.

AI adoption creates new questions: what can employees use, what can agents access, what requires approval, how do you detect errors, and who responds when something goes wrong? Valentino AI turns those questions into practical operating controls.

Assess your AI controls Build a training plan

Governance should help people work safely

Governance is not only a policy document. It is the combination of rules, permissions, review points, evaluation, monitoring, training, and incident response that keeps AI use aligned with the business.

Acceptable-use guidance
Use-case registration and ownership
Risk-tiering and approval requirements
Vendor and model review
Data classification and access controls
Human-in-the-loop design
Agent evaluation and testing
Incident handling and recurring review

A practical risk-tier model

Different uses need different controls.

Low risk: drafting, brainstorming, summarization, and internal assistance with approved information.
Moderate risk: classification, routing, recommendations, and draft communications that receive human review.
Higher risk: external communication, system writes, customer-impacting decisions, or actions with financial, legal, operational, or reputational consequences.
Prohibited or restricted: uses involving sensitive data, unauthorized surveillance, unsafe advice, or actions outside the organization’s authority.

Observability makes performance visible

A system cannot be governed if the business cannot see what it is doing.

Inputs, outputs, tool calls, and workflow status
Human approvals, transfers, and overrides
Quality scores and evaluation results
Error, escalation, and exception patterns
Usage, latency, and cost signals
Unanswered questions and knowledge gaps
Changes to prompts, instructions, permissions, and data

Training turns controls into behavior

Employees need clear guidance on what to use, when to verify, how to protect information, and how to report a problem.

Positioning

Training should be role-specific and connected to the tools and workflows people actually use—not a one-time generic AI presentation.

What we deliver

The engagement can be a standalone governance workstream or a standard part of another AI implementation.

AI use-case inventory and ownership model
Policy and acceptable-use guidance
Risk-tier and human-approval matrix
Vendor and tool review checklist
Agent evaluation and testing approach
Observability and incident-response design
Role-based employee training
Recurring governance review cadence

Our process

Controls should evolve with the systems they govern.

Inventory current and planned AI uses.
Classify risk, data, users, and actions.
Define approvals, permissions, monitoring, and escalation.
Create evaluation cases and review standards.
Train the people responsible for using and supervising the system.
Review incidents, metrics, and new use cases on a recurring cadence.

AI Governance & Training

Make AI useful, visible, and accountable.

Assess your AI controls See all services

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Controls & enablement

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Make AI useful, visible, and accountable.

Assess your AI controls Build a training plan

Governance should help people work safely

Acceptable-use guidance
Use-case registration and ownership
Risk-tiering and approval requirements
Vendor and model review
Data classification and access controls
Human-in-the-loop design
Agent evaluation and testing
Incident handling and recurring review

A practical risk-tier model

Different uses need different controls.

Low risk: drafting, brainstorming, summarization, and internal assistance with approved information.
Moderate risk: classification, routing, recommendations, and draft communications that receive human review.
Higher risk: external communication, system writes, customer-impacting decisions, or actions with financial, legal, operational, or reputational consequences.
Prohibited or restricted: uses involving sensitive data, unauthorized surveillance, unsafe advice, or actions outside the organization’s authority.

Observability makes performance visible

A system cannot be governed if the business cannot see what it is doing.

Inputs, outputs, tool calls, and workflow status
Human approvals, transfers, and overrides
Quality scores and evaluation results
Error, escalation, and exception patterns
Usage, latency, and cost signals
Unanswered questions and knowledge gaps
Changes to prompts, instructions, permissions, and data

Training turns controls into behavior

Employees need clear guidance on what to use, when to verify, how to protect information, and how to report a problem.

Positioning

Training should be role-specific and connected to the tools and workflows people actually use—not a one-time generic AI presentation.

What we deliver

The engagement can be a standalone governance workstream or a standard part of another AI implementation.

AI use-case inventory and ownership model
Policy and acceptable-use guidance
Risk-tier and human-approval matrix
Vendor and tool review checklist
Agent evaluation and testing approach
Observability and incident-response design
Role-based employee training
Recurring governance review cadence

Our process

Controls should evolve with the systems they govern.

Inventory current and planned AI uses.
Classify risk, data, users, and actions.
Define approvals, permissions, monitoring, and escalation.
Create evaluation cases and review standards.
Train the people responsible for using and supervising the system.
Review incidents, metrics, and new use cases on a recurring cadence.

AI Governance & Training

Make AI useful, visible, and accountable.

Assess your AI controls See all services