AI Governance & Evaluation
Make AI systems measurable and governable through explicit ownership, risk classification, evaluation evidence, human review, monitoring, and change controls.
When this is the right conversation
An AI demonstration is not an operating model
Teams can move from experiment to use without defining acceptable performance, harmful failure, accountable ownership, review, monitoring, or retirement. Governance must connect to the real task and system rather than remain a policy document beside it.
- AI use cases have no accountable operational owner.
- Quality is described subjectively without representative evaluation.
- Human review and escalation rules are inconsistent or invisible.
- Model, prompt, data, or provider changes are not governed in production.
Who this is for
Organisations piloting or operating AI without consistent evaluation, ownership, monitoring, or human-review rules.
Scope and next step
AI governance and evaluation focus on controls, evaluation and human review. Readiness precedes implementation; these services do not provide legal certification.
- Engagement model
- A fixed governance and evaluation assessment followed by implementation support.
- Indicative timeline
- Typically 4–8 weeks for the initial assessment and operating framework.
Delivery approach
How Algoza operationalises AI governance
Inventory
Map use cases, systems, owners, data, providers, users, consequences, and current controls.
Classify
Define risk, unacceptable outcomes, human authority, evidence, and governance proportional to the use case.
Evaluate
Create representative tests, thresholds, review procedures, and decision evidence.
Operate
Implement monitoring, incidents, change review, feedback, reassessment, and retirement rules.
Technology considerations
Governance capabilities
- Inventory
- Use cases, models, providers, prompts, data sources, integrations, owners, and users.
- Evaluation
- Representative datasets, quality measures, thresholds, adversarial tests, and review evidence.
- Controls
- Access, privacy, human review, escalation, audit events, vendor and change management.
- Operations
- Monitoring, feedback, incidents, drift, cost, reassessment, and retirement.
Responsible outcomes
What operational AI governance provides
Accountable use
Each use case has an owner, purpose, boundaries, human authority, and review path.
Measurable quality
Performance claims are tested against representative tasks and defined thresholds.
Controlled change
Production monitoring and change governance make evolving AI dependencies more visible.
Commercial engagement
What the engagement includes
- Pricing approach
- Fixed assessment; phased control and evaluation implementation.
Typical deliverables
- AI use-case and system inventory
- Risk classification and control model
- Evaluation suite and acceptance thresholds
- Monitoring, incident, change, and retirement process
Success measures to baseline
- Use cases with owners and evaluation evidence
- Unacceptable-error and escalation rates
- Monitoring and review coverage
- Control exceptions closed
Timelines and pricing methods are indicative. Algoza confirms scope, dependencies, procurement requirements, responsibilities, and commercial terms before delivery begins. Success targets are agreed against a client-specific baseline; they are not guaranteed outcomes.
Related pathways
Related pathways
Frequently asked questions
AI governance and evaluation FAQs
Is this legal or regulatory advice?
Can Algoza evaluate a third-party AI product?
What should an AI evaluation measure?
Does governance slow down experimentation?
Make AI quality, ownership, and change inspectable
Share the use cases, systems, users, data, current controls, and decisions affected. Algoza will define a proportionate governance and evaluation scope.