Automated Enterprise Document Rule Generation System
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On this page 4 sections
Related concepts 2 concepts
Key Takeaways
- GUIDE reduces document processing time from a 2 to 3 day manual baseline to 40 to 125 minutes.
- The system achieved a 96% success rate across 120 real-world enterprise documents.
- The framework successfully extracted 3,896 rules, with 71.4% passing automated semantic evaluation.
- A hybrid approach combining deterministic parsing and VLM-driven visual extraction ensures high-fidelity data capture.
- The system utilizes a versioned rule store to maintain schema-validated contracts between agents.
Summary & Methodology Analysis
The GUIDE architecture processes heterogeneous enterprise files by utilizing a multi-stage pipeline. A deterministic parsing agent extracts text from standard formats like PDF, DOCX, and PPTX, while a VLM-driven agent, specifically utilizing Qwen2.5-VL, manages visual content. Extracted data flows into a rule extraction agent that normalizes input into a 26-field schema, supported by a consistency module that uses embedding-based similarity (representing text as vectors to capture semantic meaning) and NLI classification (a technique for determining if a premise implies a hypothesis) to handle deduplication and version alignment. A shared, versioned rule store serves as the system backbone, ensuring all agents adhere to strict schema-validated contracts and provenance tracking.
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the primary purpose of the GUIDE system?
GUIDE automates the manual, error-prone, and time-consuming process of converting enterprise guidelines into governed, deployment-ready artifacts.
Q2. How does GUIDE improve efficiency compared to manual workflows?
It reduces the processing time for a single document to between 40 and 125 minutes, down from the standard 2 to 3 day manual baseline.
Q3. How reliable is the automated output?
The system achieved a 96% success rate across 120 real-world enterprise documents, with 71.4% of the 3,896 extracted rules approved automatically through semantic scoring.
Q4. Which models power the visual extraction capabilities?
The system primarily employs Qwen2.5-VL for visual processing, alongside other models including Qwen3-32B, LLaVA-13B, GoLLIE, UIE, Docling, and Donut.
Q5. How does the system ensure the generated artifacts are accurate?
It uses a two-stage evaluation framework consisting of L1 structural validation via Pydantic and L2 semantic scoring using an LLM-as-judge, followed by a dependency-aware human-in-the-loop controller for review.
Q6. What are the limitations of the current VLM extraction?
Performance degrades significantly when the system encounters low-quality scans or complex tables within documents.
Q7. Does the system support languages other than English?
No, the current framework is restricted to English-language enterprise guidelines.
Q8. How are non-expert audiences handled in artifact generation?
The system faces difficulties in maintaining persona appropriateness and ensuring full rule coverage for non-expert users.
Q9. What computational resources are required to run this framework?
The paper does not specify the exact computational hardware requirements.