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.
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
- • Quotas, credits, and alerting 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 scopes
- • SaaS, hybrid, or on-prem options
KPIs to track
- • Cost per 1M tokens
- • % requests audited
- • Policy violations
Related automations
Example workflows you can assemble for this use case.
Governed AI analysis via MCP
Steps
- • Collect logs & metrics
- • Call AI agent via MCP server
- • Root-cause summary
Outcomes
- • Faster diagnosis
- • Standardized routines
Governed HR assistant via MCP + Argy Chat
Steps
- • Action 1: create an MCP server connected to the internal HR tool via Argy Code
- • Action 2: run a deployment and validation pipeline
- • Action 3: publish the tool in Argy Chat for employees
Outcomes
- • Self-service HR access
- • Protected data
Explore more in automatable actions.
Related solutions
How leaders frame this use case across teams.
CTO / VP Engineering
Scale enterprise AI without losing control.
Security / GRC
Govern AI and DevSecOps with evidence and sovereignty.
FinOps
Control AI and cloud costs without slowing teams down.
Next step: request a demo or explore solutions.