Anatomy of a Verdict: How SimplyCodes Adjudicates Truth in a Broken Economy

This paper details the verification architecture of SimplyCodes, which adjudicates promotional truth across over 400,000 merchants in an environment where 40–60% of codes on the public internet are dead, restricted, or misleading. While the companion paper Axiomatic Intelligence (doi:10.5281/zenodo.18190481) defines the theoretical paradigm of deterministic adjudication, this paper demonstrates that paradigm operating in production. The system is designed around Byzantine Fault Tolerance (BFT) — the assumption that any single source may be lying, malfunctioning, or mistaken — and operates through a three-layer verification stack: Layer 1: Automated Code Testing (ACT). Headless browsers simulating real checkout sessions using tiered intelligence: platform adapters, heuristic pattern matching, and AI-powered navigation. Layer 2: Human Consensus Network. Tens of thousands of trained verifiers operating under blind voting, producing millions of monthly verification actions within a reputation-weighted trust economy. Layer 3: Fleet Signal. Real-time telemetry from browser extension users during actual checkouts — ground truth, not simulation. Core Contributions: The Health Score: A confidence measure derived from an event stream rather than stored as a static value, subject to variable decay rates depending on code type (evergreen, regular, flash), and traceable through six forensic dimensions from verdict back to raw evidence. The Confident No: A verified absence of codes that delivers cognitive closure at checkout. Not a failure state — a product. The Proof Packet: A structured evidence bundle documenting the full adjudication chain, designed for auditability and machine consumption by AI agents in the agentic economy. We examine three structural moats that make this system irreplicable: fifteen years of time-accumulated merchant signal, a reputation economy that cannot be purchased, and an edge case corpus that cannot be replicated without experiencing the failures firsthand. We conclude with the shift toward agent-native endpoints and an Applicability Engine that moves from verifying code existence to predicting whether a specific code will work for a specific cart in real time.