Portfolio Info

  • Category Artificial Intelligence
  • Date 25 Dec, 2024
  • Project Requirements Build an AI assistant that can answer questions from 50,000+ internal documents using RAG technology. Must include content guardrails, analytics dashboard, and integration with Confluence and PDF systems.
  • Budget $55,000.00
  • Project Manager Dr. Emily Rodriguez
  • Location New York, USA
  • Project Duration 14 weeks
  • Rating

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Generative AI Knowledge Assistant — RAG over Docs

Generative AI Knowledge Assistant — RAG over Docs

Enterprise assistant that answers from internal PDFs/Confluence using vector search and guardrails. Added analytics on prompts and outcomes.

We developed a cutting-edge AI knowledge assistant that transformed how our enterprise client handles internal documentation. Using advanced RAG (Retrieval-Augmented Generation) technology, the system processes over 50,000 internal documents and provides instant, accurate answers to employee queries. The result was a 28% reduction in support ticket volume and significantly improved employee productivity.

The primary challenge was building a system that could accurately understand and retrieve information from diverse document types (PDFs, Confluence pages, Word docs) while maintaining enterprise security standards. We had to implement sophisticated content filtering to prevent sensitive information leakage and ensure all responses were contextually relevant and factually accurate.

Project Tips

Here are the key features and highlights of this project that showcase our expertise and attention to detail.

  • Reduced support ticket volume by 28% through intelligent automation
  • Implemented RAG system processing 50,000+ internal documents
  • Built vector search with 95% accuracy for document retrieval
  • Created content guardrails preventing sensitive data exposure
  • Developed analytics dashboard tracking 10,000+ monthly queries

Overview & Challenge

We developed a cutting-edge AI knowledge assistant that transformed how our enterprise client handles internal documentation. Using advanced RAG (Retrieval-Augmented Generation) technology, the system processes over 50,000 internal documents and provides instant, accurate answers to employee queries. The result was a 28% reduction in support ticket volume and significantly improved employee productivity.

The primary challenge was building a system that could accurately understand and retrieve information from diverse document types (PDFs, Confluence pages, Word docs) while maintaining enterprise security standards. We had to implement sophisticated content filtering to prevent sensitive information leakage and ensure all responses were contextually relevant and factually accurate.