From dense legal text to 89% precision in government AI document review

The lab set out to build a document understanding model capable of extracting entities, relationships, and compliance indicators from dense legal and regulatory text. The goal was to achieve 90% accuracy in identifying citations, stakeholder relationships, and compliance status, while meeting strict government data security standards.

• Automate review of regulatory filings and administrative rulings
• Reduce manual review workload and improve turnaround times
• Ensure accuracy and auditability for compliance-critical use cases
• Maintain security clearance compliance throughout the process

Complex legal language, nested references, and clearance restrictions created unique obstacles.

• Generic NLP models achieved only 48% accuracy on government-specific entities
• Regulatory language complexity caused 57% errors in relationship extraction
• Prior annotations missed 44% of nested citations and cross-references
• Security clearance requirements reduced annotator pool by 85%
• Timeline required 25k documents in 6 weeks vs. standard 12 weeks
• Cross-jurisdictional differences caused 41% inconsistency in labeling

CleverX assembled a domain-trained team of legal and government document experts, building a custom secure workflow.

Expert recruitment:

• 6 former federal agency document reviewers (8+ years' experience)
• 4 paralegals in regulatory compliance and administrative law
• 5 records managers familiar with metadata and filing standards
• 3 cleared professionals experienced in sensitive data handling

Technical framework:

• Developed annotation schema for 85 entity types in government docs
• Mapped 120 relationship types spanning legal and administrative contexts
• Built a secure annotation environment meeting federal compliance rules
• Applied multi-level review process to ensure citation accuracy

Quality protocols:

• Triple-blind review for sensitive document sections
• Citation checks verified against official government databases
• Daily audits with pre-validated reference sets
• Full chain-of-custody documentation for all processed files

The initiative followed a structured rollout, balancing speed with quality assurance.

Week 1: Onboarding & clearance

• Verified security clearances and trained experts on document structures

Weeks 2–3: Initial processing

• Processed 8,500 documents with 45,000 entities and 22,000 relationships extracted

Weeks 4–5: Refinement phase

• Incorporated expert feedback to improve extraction accuracy to 81%

Week 6: Completion

• Delivered full dataset of 25,000 documents with comprehensive validation

Technical methodology:

• Applied hierarchical entity recognition with agency-specific taxonomies
• Developed contextual extraction aligned with legal precedents
• Achieved 85% consensus on complex regulatory interpretations
• Generated 3,200 training examples for ambiguous legal phrasing

Efficiency gains:

The project dramatically accelerated document review timelines and throughput.

• Reduced processing time 15 → 6 minutes per document
• Accelerated review cycles by 42% with automation
• Decreased manual verification needs by 38%
• Improved compliance check throughput by 2.8x

Quality improvements:

Accuracy in entity and citation extraction rose sharply with expert guidance.

• Boosted entity extraction accuracy 48% → 89%
• Increased citation linking precision by 47%
• Improved compliance detection by 43%
• Reduced misclassification of document types by 56%

Business impact:

The improvements translated into stronger performance and measurable savings.

• Secured $2.4M contract extension from improved reliability
• Processed 40% more documents within existing budget
• Reduced review costs by $1.8M annually
• Cleared backlog by 65% during pilot deployment

Strategic advantages:

The lab established lasting capabilities and a competitive edge in gov-tech AI.

• Built specialized gov-doc NLP capability for future contracts
• Created reusable extraction patterns for regulatory filings
• Established cleared expert network for sensitive engagements
• Gained differentiation in the government AI vendor landscape

The lab's system earned recognition from a federal technology modernization board for innovation in regulatory automation.