AI Solutions → RAG & Enterprise Knowledge
RAG & Enterprise Knowledge
We build retrieval-augmented generation and enterprise search on top of your documents, databases, and knowledge bases — reducing hallucination and keeping answers traceable back to a source.
What's included
Core capabilities
Retrieval-Augmented Generation (RAG) pipeline design
Enterprise search across documents, wikis & databases
Vector search on Pinecone & MongoDB Atlas Vector Search
Hybrid search (semantic + keyword) and retrieval tuning
Citation, freshness & access-control handling
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FAQ
Common questions about rag & enterprise knowledge
Does Karsient build RAG (retrieval-augmented generation) solutions?
Yes. Karsient designs and builds enterprise RAG systems end to end — ingestion and chunking of your documents and databases, embeddings and vector search, hybrid (semantic + keyword) retrieval, and grounding LLM answers in your own governed data with citations back to source.
How is enterprise RAG different from a simple chatbot?
A chatbot answers from a general model's training data. Enterprise RAG grounds every answer in your organization's actual documents and systems, keeps track of access control and data freshness, and is built to be monitored and evaluated in production — not just demoed once.
What does Karsient's RAG architecture typically include?
Document ingestion and chunking, embeddings, vector search (we work with Pinecone and MongoDB Atlas Vector Search), retrieval and reranking, guardrails and evaluation, and citation/traceability so every answer can be checked against its source.