Fragmented model access
Teams select and connect models independently, without a shared enterprise catalog or evaluation standard.
Enterprise AI resource and usage governance
Make enterprise AI investment controlled, measurable, and optimizable
Govern approved model catalogs, human and digital-employee allowances, usage insight, and cost allocation so every AI investment has clear boundaries, measurable outcomes, and room to improve.
AI RESOURCE GOVERNANCECONTROL / VALUE
01
Plan investment
02
Control access
03
Explain cost
04
Improve strategy
Cost control is not the destination. Governance should help AI investment create sustained business value.
Common challenges
AI use is growing too quickly for manual governance
As models enter more teams, applications, and digital employees, fragmented access, unclear authorization, and invisible cost become barriers to scale.
Unpredictable budgets
Usage growth is often discovered after billing, with limited proactive allowances or alerts.
Unallocated cost
Spending cannot be explained clearly by organization, project, application, or digital employee.
Unclear business return
Token consumption is visible, but completed work, time saved, and business outcomes are not connected.
Customer value
Turn AI from a technical expense into a manageable enterprise resource
01
Plan investment
Set periodic budgets and resource boundaries by organization, project, role, and digital employee.
02
Control access
Let each role use only enterprise-approved models suited to its scenario.
03
Explain cost
Connect usage to accountable owners, business scenarios, and task outcomes.
04
Improve strategy
Continuously refine model use based on quality, speed, cost, and business results.
Governance capabilities
Build a complete resource-governance chain above a unified access layer
Models and prices may change while enterprise authorization, budgets, data boundaries, and audit requirements remain stable.
Approved model catalog
Manage evaluated models and assign access by organization, role, project, application, and digital employee.
Budgets and thresholds
Set periodic budgets, call or token reference limits, and reserve resources for critical work.
Usage and anomaly insight
Identify overage trends, unusual frequency, and calls that diverge from policy.
Cost allocation and analysis
Attribute resource investment to organizations, projects, applications, roles, and digital employees.
Model policy and fit
Choose among approved models by task, quality, latency, cost, and data classification.
Audit and improvement
Record identity, scenario, model, usage, and policy outcomes to improve governance continuously.
For decision makers
Give every accountable role a clear management view
CIO / CTO
Establish shared model access and governance standards without creating another technology silo.
AI leaders
Understand model coverage, usage trends, and anomalies while scaling adoption.
Finance
Explain cost by organization, project, and scenario and improve budget forecasting.
Business leaders
Relate resource consumption to work completed, efficiency gains, and business measures.
Digital employee governance
Give every digital employee explicit model and resource boundaries
Alongside identity, permissions, and tools, a digital employee can receive approved models, periodic allowances, and task cost boundaries. The enterprise can assess work completed, resources consumed, and business outcomes produced.
Assign approved models by role and task
Alert, limit, or request adjustment at thresholds
Connect usage to tasks and business outcomes
Keep identity and permission boundaries stable as models change
Compliance and responsibility
Use model services within enterprise authorization and applicable requirements
Purefect provides technical controls for access, authorization, usage, cost, and audit. It does not bypass provider account, authorization, safety, contractual, or regional requirements.
Include only services lawfully selected and authorized by the enterprise
Define models and data scope according to industry, classification, location, and applicable requirements
Configure log content, visibility, and retention according to data minimization
Use technical governance to support—not replace—the enterprise’s own compliance assessment and accountability
Start with an AI resource-governance review
Map current models, users, cost structure, and priority scenarios to establish a practical first governance baseline.