Retrieval Augmented Generation Service

Combine the power of LLMs with real-time data retrieval to deliver accurate, context-aware, and grounded AI responses.

RAG Services

  • Design end-to-end RAG pipelines combining LLMs with vector databases or document stores for accurate, context-rich responses.

  • Vital for breaking down long documents. Ensures higher retrieval accuracy and coherence.

  • Core RAG functionality. Enables smarter retrieval versus traditional search.

  • Important for structuring how retrieved data is used by the LLM. A differentiator in output quality.

  • Popular use case. Embeds RAG into support and conversational interfaces.