Reduce Compliance Costs with Automated Policy Enforcement
Finance and compliance teams quantifying ROI from automated policy enforcement and reduced manual review overhead.
Das Problem
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.
So löst Corules es
Corules's policy runtime evaluates structured context against compiled CEL expressions — returning ALLOW, BLOCK, or ESCALATE with a reason and audit ID.
Richtlinienbeispiel
// 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/yearFrequently Asked Questions
What is a realistic escalation rate?
Escalation rates depend on policy strictness and use case. Typical range: 5–15%. Even at 15% escalation, you eliminate 85% of manual review costs for that decision category.