Policy Simulation Engine

Your AI agents'guardrails

Everyone builds the gas pedal. This is the brakes and steering.
Test your AI pricing policies before they cost you millions.

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Simulate Orders

Generate thousands of synthetic orders with LLM buyer personas that try to game your pricing.

Define Policies

Create deterministic rules with json-rules-engine. Margin floors, discount caps, volume tiers.

Monitor Results

Visualize margin impact, discount distribution, and policy violations in real-time.

MCP Integration

Expose policies via Model Context Protocol. Your AI agents query, the engine decides.

How It Works

1

Define Policy

Set your margin floors, discount caps, and volume tier rules using our visual editor or JSON.

2

Simulate Attacks

LLM buyer personas attempt to extract maximum discounts. See what breaks before production.

3

Deploy Confidently

Fix policy gaps, then integrate via MCP. Your AI agent queries, the engine enforces.

MCP Tool: evaluate_policy
// AI agent calls the MCP tool
const result = await mcp.callTool({
  name: 'evaluate_policy',
  arguments: {
    order: {
      order_value: 5000,
      quantity: 100,
      product_margin: 0.4  // 40% margin
    },
    proposed_discount: 0.12  // 12% discount request
  }
});

// Deterministic response
{
  "approved": true,
  "violations": [],
  "applied_rules": ["margin_floor", "max_discount", "volume_tier"],
  "calculated_margin": 0.28  // 28% after discount
}