99%
Document Extraction Accuracy
50,000
2.5 lakh
Documents Processed Successfully
Overview
CRIF is a leading global provider of credit information, business intelligence, and risk management solutions. As part of its GIS engagement in the Philippines, CRIF processes large volumes of business documents that must be accurately extracted, validated, and mapped into client-defined JSON schemas.
Business Challenge
Despite an established extraction and quality control process, CRIF faced challenges in achieving higher accuracy and reducing manual review efforts, including:
01
Managing high-volume document extraction at scale
02
Improving document-level accuracy beyond 97%
03
Identifying root causes of extraction errors efficiently
04
Reducing dependency on manual QC processes
05
Additionally, a subset of 2,174 Foreign Stock documents had structural characteristics that differed significantly from the primary document mix. Processing these files within the main extraction pipeline risked impacting the 50,000-document weekly delivery cadence.
elsai Solution
To help improve extraction accuracy and streamline validation workflows, elsai deployed two complementary capabilities: Docloom and LLM-as-Judge.
LLM-as-Judge for Intelligent Validation
The LLM-as-Judge module validated extracted JSON outputs against source OCR data and provided reasoned error detection, enabling:
• Automated validation of extracted data
• Detection of missing fields and schema mismatches
• Identification of semantic and mapping errors
• Categorization of errors for faster remediation
• Support for bulk correction workflows
Docloom for Specialized Document Processing
Docloom provided a dedicated extraction pipeline for Foreign Stock documents, enabling parallel processing without affecting the primary workflow. This enabled:
• Independent end-to-end processing of Foreign Stock documents
• LLM-as-Judge validation against OCR source data before QC checks
• Automated identification and remediation of recurring extraction issues
• Parallel processing that reduced pressure on the primary pipeline
• Unified output merged into the main delivery batch as a single JSON deliverable
Business Impact
By combining Docloom and LLM-as-Judge, CRIF significantly improved extraction quality while reducing manual effort.
99% document-level extraction accuracy, improving from 97% and helping eliminate recurring data quality issues across high-volume document processing workflows.
2.5 lakh documents processed successfully, demonstrating the platform's ability to operate reliably at enterprise scale without compromising accuracy.
50,000 documents delivered every week, enabling CRIF to maintain its delivery commitments and service-level agreements throughout the engagement.
Accuracy improvements achieved within just 3 weeks, accelerating time-to-value while minimizing disruption to ongoing operations.
Significantly reduced manual QC effort through automated error diagnosis, intelligent categorization, and bulk remediation of recurring extraction issues.
Improved operational efficiency and delivery consistency by replacing file-by-file investigations with reasoning-driven validation and scalable correction workflows.
Protected pipeline throughput for specialized document types, with 2,174 Foreign Stock files processed through a dedicated Docloom workflow without impacting mainline delivery capacity.
Collaboration with Unicus
Unicus and elsai worked together to enhance CRIF's document extraction workflow while preserving existing delivery commitments. Unicus leveraged elsai Docloom and LLM-as-Judge capabilities to improve error detection, automate remediation, and streamline the handling of specialized document types. The partnership delivered measurable gains in both accuracy and operational efficiency.
The elsai Advantage
• AI-powered document extraction and validation
• Reasoning-driven quality assurance with LLM-as-Judge
• Faster error detection and remediation
• Scalable processing for specialized document types
• Enterprise-ready accuracy, reliability, and governance
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