Ground your AI responses in your organization's knowledge, documents, and data for accurate, context-aware answers. Let AI help you find relevant documentation and files faster.
The foundation for AI that understands your business
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language models with access to external knowledge.
Unlike standard LLMs that rely solely on their training data, RAG systems dynamically retrieve relevant information from your documents, databases, or knowledge bases to provide more accurate, up-to-date, and contextually-aware responses.
Large language models are trained on general internet data, but they don't know about your specific products, customers, internal processes, or proprietary information.
Without access to your knowledge, AI responses can be generic, inaccurate, or even hallucinated - leading to wrong information being presented with confidence.
Proprietary AI platforms solve this by requiring access to your data. In order to provide high-quality results AI providers often index your information, which can result in the provider storing a large amount of your proprietary information within their ecosystem.
Private Retrieval-Augmented Generation (RAG) keeps your knowledge in your environment. Instead of sending your documents to a proprietary AI platform you host the entire system yourself; indexing, retrieval, and generation all happen within your infrastructure.
The business value of knowledge-grounded AI
Responses are grounded in your actual documents, not just generic knowledge. This means verifiable, accurate information with source references.
Unlike static model training, your RAG system can be updated with new documents and information at any time, keeping your AI current.
Start with a small knowledge base and scale to millions of documents. The system grows with your needs without requiring architectural changes.
Support different user groups with different access levels to different knowledge bases, all within the same system.
How organizations are using RAG in production
Problem: Employees spend hours searching through wikis, SharePoint, and document repositories for information.
Solution: AI assistant that can answer questions based on all your internal documentation, with source citations.
Tools Used: Open WebUI, LiteLLM, Docling, n8n, Vector Database
Problem: Support agents need to reference product docs, FAQs, and past tickets to resolve customer issues quickly.
Solution: AI that can search your knowledge base and suggest answers or find relevant documentation for any customer query.
Tools Used: Open WebUI, LiteLLM, Docling, Vector Database, n8n (for ticket integration)
Problem: New employees need to learn your products, processes, and culture quickly.
Solution: Personalized AI mentor that can answer questions about your company, products, and processes based on your training materials and documentation.
Tools Used: Open WebUI, LiteLLM, Docling, Vector Database
Problem: Creating comprehensive, accurate documentation is time-consuming and requires synthesizing information from multiple sources.
Solution: AI-powered writing assistant that leverages your existing knowledge base, documents, and best practices to help draft, refine, and complete documentation faster while maintaining consistency and accuracy.
Tools Used: Open WebUI, LiteLLM, Docling, Vector Database
Use n8n to collect and process your documents, emails, knowledge base articles, and other content sources
Docling extracts clean text and metadata, splits documents into semantic chunks, and prepares data for AI use
Open WebUI helps coordinate embeddings and store them in a vector database.
Open WebUI processes user queries and finds relevant chunks using LLM capabilities (via LiteLLM / SGLang)
Open WebUI assembles relevant chunks as context for the LLM (via LiteLLM / SGLang) to generate accurate, grounded responses
Our team can help you design and deploy a production-ready RAG solution tailored to your specific documents, use cases, and requirements.