hn · Show HN · 09-22 16:15 50 分 ← 返回列表

Show HN: AI·rete·RAG – a Rete rule engine decides, RAG explains why

原文链接 ↗ ·ZaharaHussain

Hi HN, I built ai·rete·rag because I kept seeing teams put an LLM in charge of decisions that need to be auditable (lending, fraud, clinical triage), then bolt on "guardrails" after the fact. It runs the two in series instead: 1. A pure-Python Rete engine evaluates YAML rules against your facts. The verdict comes only from here. Same facts, same verdict, every time, with salience-based conflict resolution. 2. RAG retrieves passages from your own policy documents, and an LLM writes a plain-Englis

五维评分

付费意愿

需求真实度

个人可落地

竞争空间

新鲜度

AI 分析

痛点：受监管行业用LLM做贷款、欺诈、临床分诊等决策时缺乏可审计性，事后加护栏无法解释和复现决策。

目标用户：金融、保险、医疗、法律、运营等受监管企业的合规、风控与工程团队。

证据：I kept seeing teams put an LLM in charge of decisions that need to be auditable (lending, fraud, clinical triage), then bolt on "guardrails" after the fact. ... The verdict comes only from here. Same facts, same verdict, every time.

变现：按企业席位或规则执行量订阅，提供私有化部署、合规审计模块、可视化规则编辑器与政策文档解析增值服务。

MVP：2周MVP：实现YAML规则引擎+RAG引用政策文档生成解释，提供审计日志/回放，只选贷款或欺诈一个场景做demo。

竞品：Drools、CLIPS、IBM ODM、FICO Blaze Advisor、Camunda、GoRules、Guardrails AI、NeMo Guardrails

摘要：Show HN 发布 ai·rete·rag，用纯Python Rete规则引擎做可复现决策，再用RAG+LLM只做带引用的解释，面向贷款、欺诈、临床分诊等需审计场景。帖子只有6分0评论，无独立付费证据，但受监管行业对可审计AI决策有真实预算。独立开发者可做轻量MVP，但需面对Drools/FICO等成熟规则引擎竞争。