AI-powered company knowledge base: ask your own documents in plain language and get accurate answers with source references.
Retrieval-Augmented Generation (RAG) lets a large language model (LLM) answer not only from its own "memory" but by first retrieving the relevant passages from your documents – policies, contracts, technical manuals, wiki pages – and then grounding its answer on them.
Your colleagues find what they need in seconds, every answer can be verified because the source document is cited, and your data never becomes training data for public AI models.
What you get
- Ingestion of documents (PDF, Word, Excel, email, Confluence, SharePoint, file servers)
- Semantic and keyword (hybrid) search with a vector database
- Chat interface with source citations and access control
- Automatic re-indexing when documents change
- Runs in the cloud or entirely on your own servers
Benefits
- Less time spent searching for information
- Faster onboarding for new colleagues
- Verifiable, cited answers – fewer "hallucinations"
- Company knowledge stays even when people leave
Typical use cases
- Internal policies and HR questions
- Customer support knowledge base
- Technical documentation and maintenance manuals
- Legal and contract repositories
Technologies
- LLM (OpenAI, Anthropic, Mistral, Llama)
- pgvector / Qdrant
- Python
- Node.js
- Docker
Frequently asked questions
Does our data leave the company?
Not necessarily. A RAG system can run entirely on your own servers with a locally hosted language model, so documents never leave your infrastructure.
How long does it take to build a RAG system?
A working prototype is typically ready in 2–4 weeks; a production system with access control and integrations takes 1–3 months depending on the volume of documents.