Idea
Platform refining expert decision rules into self-improving, high-accuracy agents for compliance and audit workflows.
Research Paper
Core Innovation
This paper introduces Trace2Policy with EISR, an error-driven iterative skill refinement process that clusters errors by root cause and applies targeted patches to human-readable rules. It demonstrates that rule quality, not model capability, drives performance gains, achieving higher accuracy by compiling refined rules into deterministic Python code for zero-cost inference and improved deployment.
Why It Matters
Many enterprises rely on tacit expert rules for auditing and compliance, which are hard to document and improve systematically. Trace2Policy automates rule refinement to boost decision accuracy and reduce costly expert hours, enabling scalable, transparent, and reliable compliance processes. This transforms manual, error-prone workflows into efficient, self-evolving systems.
Market Size (TAM)
$10–20B TAM for compliance automation and decision support; $2–5B SAM from logistics, legal, and regulated enterprises. Driven by increasing regulatory complexity and demand for scalable audit solutions.
Potential Customers & Pain Points
- Logistics companies – Need accurate scalable audit decision-making
- Legal firms – Require consistent compliance rule application
- Enterprises in regulated industries – Struggle with costly manual compliance reviews
- Software vendors – Seek to integrate explainable decision automation.
Business Model
Subscription-based SaaS platform offering rule refinement and deployment tools with tiered pricing by volume of audit cases and support levels; optional professional services for expert rule onboarding and customization.
Competitive Landscape
- IBM OpenPages
- SAP GRC
- LogicGate
- Ayasdi
- OpenAI Codex-based compliance tools
Implementation Challenges
- Integration complexity with existing enterprise systems
- Dependence on expert input for initial rule creation
- Resistance to replacing human judgment in sensitive compliance tasks
- Ensuring robustness across diverse regulatory environments
Validation Strategy
- Pilot deployment with major logistics carrier to measure accuracy improvements and operational impact
- Benchmarking on public legal and process-mining datasets to demonstrate transferability
- Cost-benefit analysis comparing expert hours saved versus Auto-EISR cycles
- Customer feedback loops to refine user experience and integration
Research Paper Overview
Trace2Policy: From Expert Behavior Traces to Self-Evolving Decision Agents
Summary
Trace2Policy recovers and iteratively refines expert decision rules in compliance-sensitive tasks using error-driven analysis, improving accuracy beyond LLM baselines. It compiles refined rules into deterministic Python for zero-cost inference, achieving higher performance in real-world audits and legal reasoning benchmarks with reduced expert effort.