Use cases / Governing enterprise AI
Governing enterprise AI
Argy becomes the governance layer between teams and models: one entry point, shared rules, and predictable costs.
AIGOVERNANCESECURITY
Context
POCs multiply, API keys sprawl, and costs and risks are hard to control.
Argy solution
A single LLM Gateway with quotas, audit, filters, and tenant-aware RAG to keep usage under control.
Key challenges
- • Scattered API keys and loss of control
- • Unpredictable AI spend
- • Lack of auditability and limits
Argy approach
- • Multi-provider LLM Gateway with routing
- • Token quotas and alert thresholds per tenant
- • PII/secret filtering and full audit
Building blocks
- • OpenAI-compatible LLM Gateway
- • Per-request audit trail
- • Tenant-aware RAG on internal knowledge
Governance & sovereignty
- • Input/output filtering policies
- • RBAC and tenant isolation
- • SaaS, hybrid, or on-premises options
KPIs to track
- • Cost / 1K tokens
- • % requests audited
- • Blocked requests
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