Reduce Compliance Costs with Automated Policy Enforcement

The answer

The cost model for manual compliance review is: (volume × review time per decision × fully loaded cost per hour). As AI volume increases, manual review scales linearly — a direct cost problem. Automated policy enforcement with Corules eliminates review cost for policy-compliant decisions. Only genuinely ambiguous or high-risk decisions reach human reviewers. At scale, this reduces the marginal cost of an additional AI-assisted decision from 'full human review cost' to near zero for compliant cases.

How it works

Corules's policy runtime sits in the enforcement path between your AI agent and the action it wants to take. The agent sends a structured context payload to /v1/validate. Corules evaluates the context against a compiled CEL policy set and returns a structured decision — ALLOW, BLOCK, or ESCALATE — with a reason and audit ID.

Every decision is recorded in an immutable audit ledger. You can replay any past decision by providing the policy_set_version and the normalized input hash — the result will be identical.

Policy example

Policies are written in CEL (Common Expression Language). They are compiled once at publish time and evaluated in microseconds at request time.

// Cost model example:
// Before: 10,000 decisions/month × 15 min review × $80/hr = $200,000/month
// After: 10,000 decisions/month, 8% escalation rate
//   = 800 decisions × 15 min × $80/hr = $16,000/month
// Savings: $184,000/month = $2.2M/year

Frequently Asked Questions