AI & Automation Case Study

RAG Document Intelligence Bot

Enterprise LLM knowledge assistant querying internal PDF databases safely without data leakage.

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Project Specifications

Client

Apex Legal Advisory

Industry

Legal & Professional Services

Duration

6 Weeks

Completed

May 2024

Tech Stack
Python OpenAI API Pinecone Vector DB LangChain React
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The Challenge

Apex lawyers spent 15+ hours weekly manually searching through thousands of PDF contracts to extract clause compliance details.

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Our Solution

WebZap built a private Retrieval-Augmented Generation (RAG) vector search pipeline that indexes internal legal documents into a secure vector database.

Key Features Implemented

Specific technical features built into this application.

01

Private Vector Indexing

Encrypts and vectorizes PDF contracts locally without sending data to public AI training sets.

02

Citation Source Verification

Every AI answer links directly to the exact page number and paragraph of the source document.

03

Multi-Document Comparison

Cross-references contract variations across multiple vendors in seconds.

04

Role-Based Access Guardrails

Ensures junior staff cannot query confidential partner compensation files.

Key Performance Indicators

Quantifiable results achieved post-launch.

85%
Contract Review Time Saved
< 1.5s
Vector Query Search Speed
100%
Private Data Encryption Standard
“

WebZap RAG assistant cut our contract auditing timeline from days to minutes while maintaining absolute client confidentiality.

Anand Vardhan
Senior Partner, Apex Legal
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