Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework

A production-grade LLM-powered pricing system that keeps all numeric computations deterministic while using LLMs for structured extraction and policy selection reduced order handling time from 10 minutes to under 2 minutes at a municipal state-owned tourism enterprise, processing 3,960 orders in six months and cutting the order management team from 15-20 to 3. The system, described in an arXiv paper (2608.26124v1), compiles policies into interpretable condition trees and supports 1,000+ active policies across 7 scenic sites and 12 business categories.

arXiv:2608.26124v1 Announce Type: new Abstract: Pricing automation in large-scale tourism is challenging because travel orders are highly unstructured, while pricing policies are complex, rapidly evolving, and inherently open-ended. Traditional rule engines are brittle and costly to maintain, whereas unconstrained LLM agents lack the reliability and auditability required for financial decisions. We present a production-grade LLM-powered pricing system with a strict decision boundary: LLMs perform structured extraction and bounded policy/path selection, while all numeric pricing, including total-price computation, is executed deterministically. Policies are compiled into interpretable condition trees, enabling open-ended support for new clauses and evolving rules without code changes, while exposing auditable artifacts for human-in-the-loop control. Periodic fine-tuning on logged traces further improves tree induction and path matching. Deployed at a municipal state-owned tourism enterprise across 7 scenic sites and 12 business categories with 1,500+ operators and 1,000+ active policies, the system processed 3,960 orders in six months, reduced the order management team from 15-20 to 3, and cut per-order handling time from 10 minutes to <2 minutes.