AI-Powered Discount Approval Enforcement

The problem

When a sales rep or AI agent proposes a discount, Corules evaluates it against customer tier limits, margin floors, and product category rules before the CRM record is written. Non-compliant discounts are blocked immediately. Edge cases escalate to the deal desk with full context.

Without deterministic enforcement, AI agents either block every edge case (adding manual overhead) or silently approve decisions that violate policy — with no audit trail to show auditors or regulators.

How Corules solves it

Corules sits between your AI agent and the action it wants to take. When the agent proposes a decision, Corules evaluates the full context against your compiled policy set in a single deterministic pass — no LLM, no ambiguity.

The result is a structured outcome: BLOCK — Discount of 35% exceeds max_discount_by_tier['standard'] = 25%.

Policy example

Corules policies are written in CEL (Common Expression Language). They are compiled once at publish time and evaluated deterministically at request time — no LLM, no variability.

// Discount approval policy (CEL)
discount_pct <= params.max_discount_by_tier[context.customer_tier]
  && (context.deal_value * (1 - discount_pct)) >= params.margin_floor
  && context.product_category in params.discountable_categories

This expression is evaluated against the structured context your agent sends in the /v1/validate request.

Integration options

Corules integrates with the tools your teams already use. All integrations call the same REST API or MCP server — your policy logic stays in one place.

Frequently Asked Questions