RAG System Development
Connect Large Language Models securely to your internal business databases.
Solution Deep Dive
Kibozera designs Retrieval-Augmented Generation (RAG) platforms. We index your company wiki files, NDAs, and customer support databases, allowing staff or users to retrieve answers via semantic search queries without LLM hallucination.
Core Benefits
AI responses derived entirely from verified company documentation
Instant lookup of details inside thousands of PDFs
Enhanced data privacy: records stay within private clouds
Sub-second semantic search responses
Key Deliverables
- Custom document ingestion & chunking microservice
- Configured vector database tables
- Search user interface and web application
- Semantic search API endpoints
Technologies We Use
Target Industries
Implementation Roadmap
How we go from initial audit to production-grade automation.
Ingestion Setup
Scraping wikis, PDFs, and folders.
Text Chunking
Splitting documents and generating vector data.
Vector Indexing
Saving embedding data into Pinecone or pgvector.
LLM Wiring
Connecting search query matches to GPT-4 context.
FAQs
Answers to common service questions
What is RAG?expand_more
Retrieval-Augmented Generation (RAG) is a technique that retrieves relevant passages from your database and feeds them to an LLM to answer prompts accurately based on your data.
Is our database uploaded to OpenAI?expand_more
No. We can set up local open-source embedding models and local LLMs (like Llama 3) so that your data never leaves your secure server.