The system, in plain terms.
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.
What needed to be solved.
Built an AI-powered document intelligence system to search and retrieve information from thousands of enterprise documents with high accuracy.
- Handling diverse document formats (PDF, Word, Excel) with varying structures
- Achieving high accuracy for domain-specific terminology and acronyms
- Ensuring secure access control across departments
- Optimizing response time for complex queries
“Successful RAG implementations require deep domain understanding and custom fine-tuning.”
What we set out to do.
- 01Implement semantic search across 10,000+ enterprise documents
- 02Achieve >90% retrieval accuracy for domain-specific queries
- 03Support natural language questions with contextual answers
- 04Maintain data security and access control compliance
- 05Scale to handle 1,000+ concurrent users
How we built it.
Handling diverse document formats (PDF, Word, Excel) with varying structures — Built custom document parsers and preprocessing pipelines for each format, preserving semantic structure and metadata
Achieving high accuracy for domain-specific terminology and acronyms — Fine-tuned embedding models with company-specific vocabulary and implemented custom entity recognition
Ensuring secure access control across departments — Integrated with existing IAM systems and implemented document-level permissions in the vector database
Optimizing response time for complex queries — Implemented hybrid search combining vector similarity and keyword matching, with intelligent caching strategies
Retrieval accuracy
95% retrieval accuracy on domain-specific queries
What we used.
What changed in production.
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
Lessons from shipping it.
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.