Secure Enterprise AI Operations

One control plane for every prompt, model, tool call, and dollar.

MCP Agents gives IT and finance teams a governed AI gateway: sensitive data is blocked before it leaves the company, model spend is optimized in real time, and every request is traceable.

Gateway Status
Enforcing policy
AI Apps Observed
ChatGPT, Claude, Gemini, internal MCP tools
Deployment
SOC2-ready audit mode
Updated
Live
Security Blocks Today
DG
47
PII, API keys, payroll data, and customer records stopped before model egress.
+18 high-risk prompts
Monthly Savings
CM
$18.4k
Saved through semantic cache hits, model routing, and duplicate-request suppression.
60% below unmanaged baseline
Trace Coverage
TR
100%
Prompt, response, model, user, department, token cost, and policy outcome captured.
12,884 spans indexed
Cache Hit Rate
SC
71%
Repeated research, HR, and engineering prompts answered without extra model spend.
287 ms average latency

Live AI Traffic

Policy decisions across departments and models
User Department Model Policy Cost

Department Budget Control

Month-to-date spend against approved limits

Data Guard Simulator

Test what happens before prompts reach external LLMs
Prompt Inspection
Policy Result

Active DLP Policies

Rules mapped to enterprise data classes
Projected Month-End Spend
$30.7k
Down from $76.8k unmanaged forecast.
Routing Savings
$11.2k
Complex prompts still reach premium models; routine tasks use efficient models.
Cache Savings
$7.2k
Semantic reuse cuts duplicate calls without changing employee workflow.

Cost Monitor

Actual spend, prevented spend, and unmanaged baseline

Trace Explorer

Every request is explainable after the fact

Routing Policy

Quality, cost, latency, and data sensitivity decide the model

Connector Governance

Tool access is scoped, logged, and revocable
Demo data is static and designed for product validation conversations. Connect live gateway logs to turn this into an operational console.