Case Study • RAG System
Smart Corporate Document Search
How we reduced information search time by 80% with an AI assistant — fast search across 30K+ documents in 3 seconds
80%
Time Reduction
95%
Search Accuracy
30K+
Documents
3 sec
Response Time
Client's Task
- •Employees spend up to 2 hours per day searching for the right documents
- •Documents are scattered across different systems: Confluence, SharePoint, Google Drive
- •Standard search can't find documents by meaning, only by keywords
- •New employees don't know where to look for the right information
Project Goals:
- Consolidate all documents into a single search system
- Find documents by meaning, not just keywords
- Answer questions in natural language
- Reduce information search time by at least 5x
Solution
Corporate AI assistant with semantic search
RAG system that indexes all corporate documents and answers questions with source references
Indexing 30K+ documents from various sources
Semantic search: finds by meaning, not just words
Natural language answers with document citations
Source reference for every answer
Integration with Confluence, SharePoint, Google Drive
Automatic updates when documents change
Results After 1 Month
| Metric | Before | After |
|---|---|---|
| Information search time | 30-60 minutes | 3 seconds |
| Information accuracy | 60% | 95% |
| Document coverage | Scattered | 100% in one place |
| Onboarding time | 1 month | 1 week |
| Support tickets | 100% | -40% |
What the Client Gained
Employees save 2+ hours per day searching for information
All documentation accessible through a single interface
Answers with source references — verifiable
New hires find information faster
Reduced workload on support and HR
Technologies
OpenAI Embeddings • GPT-4 • Python • FastAPI • Qdrant • Confluence API • SharePoint API