PathReader converts existing policy documents into a structured, versioned rule set and evaluates AI agent actions against these rules in real time.
Funding
Funding not disclosed
Founders
Product
Problem
Regulated organizations struggle to ensure that AI agents consistently follow complex, often undocumented policy requirements, leading to compliance risk and audit challenges. Existing probabilistic guardrails lack reproducibility and clear traceability, making it difficult to prove adherence to regulations.
Solution
PathReader automates the conversion of existing policy documents—such as PDFs, handbooks, and compliance guidelines—into a structured, versioned rule set. Human reviewers validate each extracted rule with its source citation before publishing. The platform then evaluates every AI agent action in real time, returning deterministic allow, block, or escalation decisions accompanied by a full audit trail. Because decisions are rule‑referenced and reproducible, they can be directly presented to auditors. Version control enables organizations to roll back or retire policies as regulations evolve, ensuring continuous compliance without custom engineering.
Target Audience
Primary customers are compliance, legal, and risk teams in regulated sectors—such as insurance, finance, healthcare, and legal services—that deploy AI agents to perform policy‑critical actions.
Features
- AI‑driven extraction of policy clauses from PDFs, internal handbooks, and compliance guidelines
- Human‑in‑the‑loop review interface showing source quotes for each candidate rule
- Versioned, auditable policy repository with rollback and retirement capabilities
- Real‑time decision engine that returns allow, block, or escalation outcomes with full traceability
- Deterministic, rule‑referenced enforcement contrasting with probabilistic LLM guardrails
- Integration points for AI agents to query compliance decisions before messaging, form submission, or transaction execution