After every cluster of decisions, ZenoPay analyzes its own outcomes and proposes specific evidence-backed refinements. A/B tests run live experiments on real decisions. Human approval is always required. The system becomes calibrated to your specific customer base — not through manual tuning, but through contact with its own outcomes.
After every 50 decisions of the same type — retention interventions, upgrade offers, rail selections, fraud blocks — the system runs a micro-cycle analysis. It looks at outcomes, identifies patterns, and generates specific proposals with evidence.
A proposal might be: "The price_lock_30d offer converted at 34% for customers with churn score 0.60–0.70 but only 11% for scores above 0.80. Suggest raising the BNPL offer threshold from 0.65 to 0.72 and reserving price_lock_30d for the lower-risk band."
The analysis threshold is reached for a decision type. The micro-cycle runs automatically.
The brain reviews its own audit trail, identifies patterns in what worked and what didn't, and compares against expected performance.
Specific refinements proposed with evidence, expected improvement, and current vs. proposed values. Stored as pending proposals.
Pending proposals appear in the dashboard guardrails panel. Nothing changes until a human explicitly approves each one.
The A/B testing engine runs controlled experiments on live decision traffic. Two retention strategies, two offer amounts, two rail-selection weights — variant A receives a defined percentage of traffic, variant B the rest. The engine tracks outcomes until one variant reaches statistical significance at 95% confidence.
When a test concludes, the winning configuration is submitted as a guardrail proposal. Human approval promotes the winner to the active default. Losing configurations are documented with their evidence for future reference.
The self-improvement engine and the guardrail validation engine are architecturally separate systems. The improvement engine generates proposals. The guardrail engine enforces limits. The LLM cannot instruct the guardrail engine. It cannot reason around it, prompt-inject it, or propose a guardrail change and simultaneously apply it.
This is by design. An AI system that can change its own limits — even with good intentions — introduces a category of risk that ZenoPay's architecture eliminates by construction.
Continuous self-improvement is one of six integrated systems. Every other system's outcomes feed into the improvement analysis.
Most software stays static until you configure it. ZenoPay is designed to improve itself from contact with its own outcomes. Reach out for early access.