Generic AI chatbots hallucinate and can't answer questions about your business. Retrieval-Augmented Generation (RAG) fixes that. Kolte Technologies builds RAG systems that ground large language models in your documents, databases and knowledge — so answers are accurate, current and traceable to a source.
RAG combines a search step with a generation step. When a user asks a question, the system first retrieves the most relevant passages from your knowledge base using vector and semantic search, then the language model composes an answer using only that retrieved context — with citations back to the source. You get the fluency of an LLM without the invented facts.
Answer employee or customer questions from manuals, policies, tickets and wikis — instantly and accurately.
Query contracts, reports and research at scale and get sourced answers.
Deflect tickets with grounded, on-brand answers and smooth human handoff.
Semantic search across everything your business knows, with permissions respected.
We prepare your documents and split them for accurate retrieval.
We index content in a vector database such as pgvector, Pinecone or Weaviate.
We fetch and rank the most relevant context for each question.
The model answers using only retrieved context, with citations.
We measure answer accuracy and faithfulness rigorously.
We add safety controls so the system stays on-topic and honest.
Let's scope a RAG pilot on your real content and prove the accuracy.
Scope a RAG pilot