Every enterprise workload has a control plane. AI doesn't — until now. The MTA AI Control Plane is the enterprise governance layer for routing, cost, compliance, and explainability across cloud and on-prem infrastructure. Build a policy profile for each agent that balances quality, cost, and risk. The control plane evaluates available models, selects the best fit, enforces data residency, and records every decision — automatically, on every request, across every hub.
Token costs are unified across environments — cloud APIs at published rates, on-prem GPUs at observed utilization — into a normalized cost-per-token metric by model. The CFO question answered before it's asked.
Policy-driven routing, unified cost intelligence, PII enforcement, audit-ready decision records, and project-level budget governance — across every cloud and on-prem hub.
Each AI agent gets its own policy profile — quality, cost, and risk — so enterprises can balance model selection, governance, and operating standards by workload instead of relying on static global settings. Consistent control across every use case.
The control plane evaluates available cloud and on-prem models for each request, then selects the best fit based on policy, performance, cost, and risk. No manual model selection. No config files someone wrote six months ago. A live policy evaluated on every request.
Unified cost visibility across cloud and on-prem inference. The control plane converts marginal and fully loaded cost, observed GPU utilization, and breakeven routing logic into a normalized cost-per-token metric by model. Defensible, cost-based routing across environments — not estimates, not assumptions.
PII detection occurs before sensitive data leaves the enterprise boundary, with automatic routing to approved on-prem or compliant environments when required. Compliance policy becomes an enforceable control at the point of inference — not a document someone hopes developers read.
Each routing decision is recorded in a tamper-evident, integrity-hashed record — models evaluated, scoring factors, rejected options, and final selection. Mapped to SOC 2 controls and GDPR Article 22 explainability requirements. Compliance, security, and risk teams get a defensible basis for audits and regulatory review.
A single routing layer across major cloud providers, private GPU infrastructure, and on-prem inference stacks. Add or remove hubs with zero downtime. No vendor lock-in — the control plane is the abstraction layer that preserves operational flexibility.
Budgets are managed at the project level with forecasting, threshold alerts, and policy-based routing to control spend before overruns erode ROI. Finance, platform, and product teams share a single governance model for AI consumption.
Click "Explain" on any routing decision. See the candidates evaluated, the scores assigned, the alternatives rejected, and the policy constraints applied. The system shows its work — to the platform team in real time and to the auditor after the fact.
Preview the cost and routing impact of policy changes before committing them. Adjust an agent's quality slider from 5 to 9 and see which model it moves to, what the cost delta is, and which hub serves it — before a single token is generated.
“You don't buy an LLM. You buy a routing policy.”
The AI Control Plane is the foundation. Its differentiation is strongest where AI decisions must be policy-driven, auditable, cost-aware, and independent of a single vendor ecosystem. Everything else — agents, tools, knowledge, workflows — plugs into it.
Privacy isn't a feature we bolt on. It's the foundation everything else is built on top of. Your data stays yours — by architecture, not by policy document.
Vector embeddings, conversation history, and semantic memory stay on your premises. When a cloud model is selected, only the retrieved context for that specific request is sent — your full data never leaves.
PGP encryption on all stored content. TLS on all connections. WAL streaming replication to hot standby. Encrypted daily backups with 30-day retention.
SOC 2, ISO 27001, CCPA, GDPR — not checklists we satisfy, but constraints we designed around. The audit trail, data residency, and access controls exist because the architecture requires them.
The AI Control Plane + privacy is the foundation. On top of it, choose the capabilities your organization needs. Each module plugs into the same governance, the same audit trail, the same routing engine.
Enterprise document ingestion. Connect SharePoint, Confluence, wikis, file shares. Employees get answers from your own knowledge base — embeddings stay on-prem.
Domain-specific agents trained on your terminology and processes. Healthcare, legal, finance, manufacturing. Specialized AI that speaks your language — routed through control plane policy.
Ticket triage, response drafting, escalation routing, satisfaction tracking. AI handles volume, your people handle relationships. Every interaction audited.
Departments, divisions, subsidiaries — each gets isolated context, separate permissions, dedicated agents. One platform, many boundaries. The control plane enforces routing policy per tenant.
Multi-agent pipelines that compose from agents, tools, and routing policies. AML investigation, contract review, underwriting — each workflow is a composition of policy-governed agents.
CRM, ERP, HRIS, ticketing systems. AI that works inside your workflows, not alongside them. Every external call routed through the same policy engine.
Start with the AI Control Plane and the modules you need today. Add more as your organization evolves. Every module inherits the same privacy foundation, the same compliance posture, the same routing governance.
The backplane and LLM hubs are separate. Keep your data on-premises while routing inference to the best model at the best price — anywhere.
Vector embeddings, user facts, and semantic search stay on your network. When a cloud LLM is selected, only the retrieved context for that request is sent — your full vector store never leaves.
Inference runs on any combination of on-prem GPUs, private cloud, or commercial APIs — chosen dynamically per request by the AI Control Plane — inference routing engine.
Set routing rules at the project and agent level — deterministic, not probabilistic. PII-flagged requests always route on-prem. Regulated workloads never leave your network. Policies are declared in config, enforced automatically, and auditable.
Policy-driven inference routing makes risk, quality, and cost-based decisions on where inference runs. Route sensitive data to on-prem, complex reasoning to the best model, routine queries to the cheapest. Add or remove hubs in real time — no restarts, no downtime.
Route by model capability: reasoning tasks to large models, quick answers to small ones, embeddings to local GPUs. Minimize cost per token while maximizing quality.
The AI Control Plane runs on your Kubernetes or OpenShift cluster. Your data, your network, your compliance boundary. Cloud hubs connect outbound — you control the egress.
Start with on-prem GPUs. Add cloud hubs with a config change — no restarts, no migration. The control plane routes the overflow automatically.
Deploy under your brand. Custom domain, your logo, your colors. Your employees experience AI governance as a native part of your organization.
Live demos built on the platform. Each one composes from the same capabilities — agents, tools, routing, audit — configured for a specific industry.
6-agent investigation pipeline. Adverse media screening, sanctions checks, case narrative generation. 22 seconds vs. 2-4 hours manual. HITL gates at every decision point.
Coming soon. Clinical document review, PHI detection, compliance monitoring.
Coming soon. Clause extraction, risk analysis, precedent search.
Coming soon. Risk scoring, adverse history, policy recommendation.
Tell us about your infrastructure, your compliance requirements, and the models you're running. We'll show you what the AI Control Plane delivers — live, on your data, in 30 minutes.
Request a Demo