LLM Document Validation: Zero False Approvals for Telecom Compliance

Executive Summary

Built an LLM-powered compliance validation system for an Italian telecom firm to streamline their manual document review process. The system pre-screens compliance documents and flags potential issues before clients submit to regulatory authorities, where false approvals cause costly project rejections. Human reviewers make final decisions based on the system's analysis. Achieved 100% precision (zero false approvals), 90% rejection accuracy, and reduced review time from days to minutes.

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Challenge

An Italian telecom firm needed to validate compliance documents for tower projects before their clients submitted them to authorities. Their manual review process was slow, time-consuming, and inconsistent, such that given the same requirements, different reviewers would approve or reject similar application documents differently.

False approvals meant clients' projects would be rejected by authorities later in the process, wasting time, money, and delaying tower deployments. The firm needed a way to pre-screen documents consistently and flag potential issues at scale, while maintaining human oversight for final decisions.

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Approach

The core requirement: 100% precision with human oversight. False approvals would cause regulatory rejections, wasting time and money. This meant building a system that reliably flags issues for reviewers rather than attempting full automation (precision over speed), with humans making final decisions.

Key considerations:

• Multiple document categories requiring category-specific validation logic
• Zero tolerance for false approvals (costly regulatory rejections)
• Human reviewers must make final decisions (system flags, doesn't auto-approve)
• Full audit trail for compliance verification

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Solution

I designed and built an LLM-powered document validation system with the following components:

• Containerized backend: FastAPI, Celery, PostgreSQL, and Redis for scalable, high-throughput processing
• Multi-model integration: GPT-4 Vision and Claude 3.7 Sonnet, selected based on document type and validation requirements
• Category-specific prompts: Custom prompt logic for each document category with explicit pass/fail criteria
• Strict validation rules: Versioned prompt templates without ambiguity
• Human-in-the-loop design: System pre-screens documents and flags potential issues with confidence scores and reasoning, enabling reviewers to make faster, more informed decisions

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Outcome

• 100% precision: Zero false approvals, no bad documents slipped through
• 90% rejection accuracy: System correctly identified 90.16% of invalid documents
• Review time: Reduced from days to minutes per document
• Production-ready: Delivered as a deployable prototype for human-in-the-loop compliance workflow

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Tech Stack

FastAPI • Celery • PostgreSQL • Redis • Docker • GPT-4 Vision • Claude 3.7 Sonnet • PyMuPDF • Pydantic