Back to ProjectsRAG Service for Corporate Knowledge Base
We developed an AI service based on RAG approach for working with a corporate knowledge base — making interaction with large volumes of internal documents faster and more practical through conversational queries.
Client
NDA
Period
1 month
Format
AI service
About the project
We developed an AI service based on RAG approach for corporate knowledge base interaction. Instead of manual document search, users get a tool for natural language questions answered from relevant knowledge base fragments — reducing manual search, simplifying navigation, and making internal material work more intuitive.
The Challenge
We needed an AI service for extracting relevant corporate knowledge base information using LLM reliably. Answers needed to be based on retrieved documents, not just model generation — increasing accuracy, reducing hallucinations, and making the service suitable for real working scenarios.
Our Solution
- Designed AI service for corporate knowledge base interaction
- Built document indexing and storage pipeline for search
- Implemented RAG approach for relevant fragment extraction before answer generation
- Developed natural language user query processing logic
- Built answer generation mechanism based on retrieved documents
- Delivered service as practical tool for internal information work
- Laid foundation for knowledge assistant scenario development
Results
- AI service for corporate knowledge base interaction delivered
- Information search moved to convenient conversational format
- Answers based on real documents, not only model generation
- Manual effort reduced when navigating internal materials
- Case demonstrates expertise in RAG solutions and knowledge assistants for business
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