Retrieval-Augmented Generation

Connect AI to Your Data. Get Accurate, Sourced Answers.

RAG How it Works

Chunk, Vectorize, and Retrieve
Chat with Your Data

We chunk your organization's knowledge with embedding models, store the vectors in a private vector database, and stand up agents that retrieve the right context for every response — on your CubCloud infrastructure.

  1. 01

    Your Data, Ingested

    Connect documents, knowledge bases, wikis, tickets, and databases. We meet your data where it lives — no exfiltration required.

    • PDFs, Docs, Markdown
    • Confluence, Notion, SharePoint
    • SQL & object storage
  2. 02

    Chunked & Embedded

    Content is split into semantic chunks and run through embedding models that turn meaning into high-dimensional vectors.

    • Smart, overlap-aware chunking
    • Open or proprietary embedding models
    • Re-indexed as your data changes
  3. 03

    Stored in a Vector DB

    Vectors land in a private vector database on CubCloud — fast similarity search across millions of chunks, tenant-isolated and yours alone.

    • pgvector, Qdrant, Weaviate
    • Per-org tenancy & ACLs
    • Hybrid keyword + semantic search
  4. 04

    Agentic Chat Response

    Your agent retrieves the most relevant chunks, reasons over them, and answers in chat — with citations back to the source.

    • Cited, source-grounded answers
    • Tools, memory, and guardrails
    • Streamed into Chat or Voice
Org DataDocs · DBs · KBsEmbeddingsChunk · VectorizeVector DBPrivate · IndexedAgentic ChatCited Answers
IngestEmbedRetrieveRespond
Retrieval-Augmented Generation
RAG connects your AI to your actual data — documents, databases, knowledge bases — so it retrieves and reasons over real information before responding. We build private RAG systems on your CubCloud infrastructure.
Talk to Us About RAG