AI Governance Platform
AI Governance for secure, cost-aware AI adoption
AI Governance is for AI usage: gateway routing, prompt guardrails, model and agent controls, tracing, token efficiency, and AI spend visibility.
Prompt and data guardrails
Protect AI usage with content filtering, PII detection, policy enforcement, and audit-ready controls.
Agent and model governance
Register agents, control tool access, trace behavior, detect loops or drift, and govern local, private, and public LLM usage.
AI gateway and integrations
Route AI requests through a single control plane connected to the tools teams already use.
Bluebill AI Governance
Four pillars of AI governance
These capabilities belong under AI Governance and are focused on controlling prompts, models, agents, routing, tracing, and auditability.
Intelligent AI Gateway
Route every query between local models, public LLMs, and MCP servers while optimizing for cost and latency.
Smart routing • Prompt optimization • Model output arbitrage • Role-based access • Department billing • Data sovereignty
AI Security & Guardrails
Protect every AI interaction with filtering, PII detection, data loss prevention, and gateway-level policy enforcement.
Content filtering • PII detection • DLP • Policy enforcement • Compliance-ready audit trails
Team & Agent AI Performance
See how each team and agent uses AI, with efficiency scores, leaderboards, model benchmarking, and full prompt visibility.
Per-team usage • Per-agent usage • Efficiency scores • Model benchmarks • Full prompt visibility
AI Agent Control
Register the agents you already built and govern them with cost limits, anomaly detection, recursion limits, and audit trails.
Monitor agents • Cost and time limits • Loop detection • Block unauthorized MCP and vector stores • Flag insecure operations
From setup to governed AI in days
01
Connect your infrastructure — point Bluebill at existing models, LLM providers, corporate services, and MCP servers so available tools and data sources can be discovered.
02
Define policies and access — set guardrails, routing rules, role-based permissions, model access, and data-flow controls.
03
Register your agents — govern existing LangChain, CrewAI, or custom agents with cost limits, time limits, recursion limits, and approved tools.
04
Monitor, optimize, and scale — trace every interaction, catch anomalies, reduce waste, and expand governance as AI adoption grows.
AI spend signal
AI spend optimization inside the governance gateway
Token compression, model routing, and usage controls belong here because they manage how teams use LLMs, agents, prompts, and AI providers.
up to 60%
AI usage reduction target
up to 97%
fewer tokens billed
System-prompt trimming
Cuts fixed instruction and schema overhead re-sent on every call.
Compression engine
Shrinks retrieved context, tool outputs, and history inline while preserving meaning.
Cache alignment
Keeps provider caches hitting so repeated context can be billed at a fraction of the usual cost.
Smart model selection
Routes each request to the best-value model that still meets the required quality bar.
Govern the tools your AI teams already use
The governance platform connects to LLM providers, MCP servers, agents, identity systems, collaboration tools, and operational apps so every AI interaction can be routed, secured, traced, optimized, and audited.
60%
Governed AI routing
99.9%
Gateway uptime SLA
<10ms
Routing overhead
Real-time
Agent anomaly detection
SaaS cloud deployment
Start with seats, pooled requests, smart routing, guardrails, cost dashboards, tracing, role-based access, department billing, and enterprise SSO options.
On-premises deployment
Run in your own cluster with local model hosting, agent governance, SSO/SAML/LDAP, air-gapped options, GPU support, custom connectors, and white-glove onboarding.