RAG-LLM

RAG-LLM

AI-powered Knowledge Solutions

Solutions Based on Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) make it possible to transform enterprise information into accessible and actionable knowledge.

The combination of document databases with generative AI capabilities delivers precise, relevant answers that are aligned with each organisation’s internal documentation.

Typical applications include:

  • Commercial chatbots that answer customer queries using company knowledge.
  • Internal assistants that streamline workflows and support employees.
  • Smart document manuals offering direct, context-aware answers.
  • Compliance and policy guidance systems ensuring consistent decisions.
  • Knowledge hubs that unify dispersed information for quick retrieval.

👉 Try our RAG Sandbox using ChatGPT-3.5

A demonstration environment is available to explore how a RAG-LLM system responds using sample documentation. The trial version uses ChatGPT 3.5, while production solutions can be deployed with ChatGPT 4.0 or with proprietary models on dedicated GPU servers.

Two Paths to RAG–LLM Integration

There are two main approaches to implementing RAG with large language models:

API-based deployment (e.g. ChatGPT-4.0) Provides fast, reliable, and scalable solutions. Costs are predictable, and no specialized hardware is required.

Local deployment (e.g. Mistral-7B) Runs directly on controlled infrastructure, maximizing privacy and independence from external providers. This option requires GPU-powered servers and involves higher operational costs.

The right choice depends on priorities such as performance, cost structure, and data security. Both approaches lead to robust, production-ready applications capable of adapting to the specific needs of each organization.