Assisted Pricing and Foundational Pricing Capabilities: A Scalable, Explainable, and Fair AI-Driven Pricing Architecture

Static price schedules ignore how much service requests vary. The result is estimation error, revenue leakage, and prices that customers cannot interpret. This study tests whether an assisted pricing architecture can improve accuracy without giving up fairness or transparency. The system pairs gradient-boosted trees with a regularized linear model, applies a rule engine for margin, regional, and risk limits, attaches SHAP attributions, and adjusts prices with Q-learning under a fairness-penalized reward. It was run on a simulated dataset of 500,000 transactions from four service industries, repeated over five random seeds. Against a static rate card, the share of estimates within ?5% of the realized price rose from 11.2% to 34.0%, mean absolute error fell by 67%, and the standard deviation of relative error dropped from 39% to 12%. Simulated price noise caps attainable accuracy at 63.7%, so the system is still well short of a perfect estimator. Simulated CSI rose by 30 points and revenue per session by 35%, but both follow from assumed customer-response models. With an 8% age-based markup injected into the training data, an ungoverned boosted-tree model gave parity ratios of 0.98?1.06. The governed system stayed within 0.99?1.01. A burst of 10,000 concurrent requests on one 4-vCPU node had a median latency of 1.55 s (p95 2.89 s, p99 3.02 s). Reinforcement learning did not reduce six-month pricing drift. The data are simulated, so all results are preliminary.