Enterprise AI Document Assistant

Overview

An enterprise organization struggled with information retrieval across thousands of internal documents, policy manuals, and knowledge bases. Employees spent hours searching for answers, leading to decreased productivity and inconsistent information access. The client needed an intelligent search solution that could understand natural language queries and retrieve accurate, contextual information from their vast document repository.

We designed and implemented a custom RAG (Retrieval Augmented Generation) system that combines semantic search with large language models to provide accurate, context-aware answers. The solution indexes all document types, understands complex queries, and delivers precise answers with source citations.

The system now serves as the primary knowledge discovery tool for over 500 employees, dramatically reducing search time and improving information accuracy across the organization.

Objectives

Implement semantic search across 10,000+ enterprise documents
Achieve >90% retrieval accuracy for domain-specific queries
Support natural language questions with contextual answers
Maintain data security and access control compliance
Scale to handle 1,000+ concurrent users

Challenges & Approach

Challenge

Handling diverse document formats (PDF, Word, Excel) with varying structures

Solution

Built custom document parsers and preprocessing pipelines for each format, preserving semantic structure and metadata

Challenge

Achieving high accuracy for domain-specific terminology and acronyms

Solution

Fine-tuned embedding models with company-specific vocabulary and implemented custom entity recognition

Challenge

Ensuring secure access control across departments

Solution

Integrated with existing IAM systems and implemented document-level permissions in the vector database

Challenge

Optimizing response time for complex queries

Solution

Implemented hybrid search combining vector similarity and keyword matching, with intelligent caching strategies

Outcomes & Impact

95% retrieval accuracy on domain-specific queries

80% reduction in time spent searching for information

500+ active users across multiple departments

Sub-2-second response time for 95% of queries

Successfully indexed 10,000+ documents with automatic updates

Key Learnings

Successful RAG implementations require deep domain understanding and custom fine-tuning. Generic models struggle with enterprise-specific terminology and context. We learned that combining semantic search with traditional keyword matching provides better results than either approach alone. Additionally, involving end-users early in the testing process was crucial for identifying edge cases and improving accuracy.

Document preprocessing and chunking strategies have enormous impact on retrieval quality. We experimented with multiple chunking approaches before settling on a semantic-aware strategy that preserves context boundaries. This project reinforced the importance of building robust evaluation frameworks to measure and improve RAG performance over time.

Technology Stack

PythonLangChainOpenAI GPT-4PineconeFastAPIReactPostgreSQLDockerAWS

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