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Home > Case study > How Everforth Quinnox Enabled AI-Driven Contract Intelligence with 60–80% Effort Reduction for a Leading US Insurer

How Everforth Quinnox Enabled AI-Driven Contract Intelligence with 60–80% Effort Reduction for a Leading US Insurer

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Client Overview

A leading US-based insurance company managing a vast portfolio of policies, contracts, and regulatory documents sought to modernize its document intelligence capabilities. With thousands of new documents processed daily across underwriting, claims, compliance, and legal functions, the organization faced increasing pressure to improve speed, accuracy, and governance in decision-making.

Business & Technical Challenges

The client’s document ecosystem had grown complex and difficult to manage, leading to several operational inefficiencies: 

    • Extracting meaningful insights from large volumes of unstructured insurance documents was difficult and time-consuming.  
    • Critical data was fragmented across multiple repositories, with no unified search or analysis layer.  
    • Manual review processes introduced delays, inconsistencies, and a higher risk of human error.  
    • There was no scalable mechanism to automatically validate document correctness, consistency, or contextual relevance.  
    • Decision-making cycles were slowed due to the lack of structured, explainable insights derived from documents.  

These challenges collectively affected operational efficiency, compliance assurance, and the organization’s ability to respond quickly to business needs.

The Everforth Quinnox Approach & Solution

To address these challenges, a modern AI-powered Contract Intelligence platform was designed and implemented using a scalable, cloud-native architecture with advanced generative AI capabilities.

1. Automated Document Ingestion & Processing

A unified ingestion pipeline was built to automatically collect, normalize, and process documents from multiple enterprise repositories. This eliminated manual intervention and ensured a consistent data foundation.

2. AI-Powered Extraction & Classification

Using advanced foundation models and AWS Bedrock-based automation, the solution enabled: 

    • Intelligent document classification across insurance domains (policy, claims, legal, compliance)  
    • High-precision information extraction from complex unstructured text  
    • Structuring of key contract attributes into searchable formats 

3. Intelligent Scoring & Grading System

Each document and extracted output was evaluated using an AI-driven scoring mechanism to assess: 

    • Data completeness  
    • Structural consistency  
    • Relevance to business context  

This helped prioritize high-value documents for downstream processing.

4. LLM-Based Evaluation Layer

A Large Language Model (LLM) evaluation framework was introduced to ensure output reliability: 

    • Faithfulness checks ensured extracted data accurately reflected source documents  
    • Correctness validation improved trust in generated insights  
    • Context quality assessment ensured relevance to business queries

5. Human-in-the-Loop Review Mechanism

To maintain governance and accuracy, the system flagged uncertain or low-confidence outputs for human validation. This ensured a balanced approach between automation and expert oversight.

6. Secure Insight Dashboard

A centralized, secure dashboard was delivered to enable: 

    • Natural language querying across contracts and documents  
    • Real-time insights and analytics  
    • Role-based access for compliance and business users  
    • Traceability and explainability of AI-driven outputs 

Benefits We Delivered

The transformation delivered measurable impact across operational efficiency, accuracy, and decision-making: 

    • 60–80% reduction in manual effort in document review and extraction workflows. 
    • 30–40% faster decision-making cycles across underwriting and compliance teams.  
    • Improved accuracy and consistency in extracted contract intelligence. 
    • Automated risk and compliance checks, reducing dependency on manual audits. 
    • A scalable, enterprise-ready AI platform designed for future expansion across additional document-heavy processes. 

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