RAG · Knowledge Base · Enterprise AI

AI that knows what you know
Not guessing — citing sources

ChatGPT doesn't know your procedures, your 2024 price list, your supplier contracts. RAG bridges a language model with your organization's internal knowledge — precise answers with citation of the original document.

Let's build a RAG system

A RAG (Retrieval-Augmented Generation) system is an architecture that connects a language model to your business documents, so it answers from your real knowledge — not from a guess. Instead of relying on what the model learned during training, RAG retrieves relevant passages from your knowledge base in real time — procedures, price lists, contracts and FAQs — and feeds them into the answer, including a citation of the original document. It fits any organization whose internal knowledge is scattered across drives, emails and people, and that wants answers to arrive in seconds rather than by asking a colleague. Orel AI builds RAG systems end to end — document indexing, semantic search, Evals and automatic updates — on top of OpenAI, Anthropic, Supabase and pgvector, as a standalone layer or as part of an AI agent. Implementation time: 1–3 weeks for an initial system, depending on the number of sources and the complexity of permissions.

How a RAG system works

Your internal knowledge, accessible in seconds

No more searching Google Drive, no more asking colleagues — AI that finds the right answer from your own documents.

Automatic document processing

PDF, Word, Notion, Confluence, Google Docs — one-time upload and everything auto-indexed from there. Add a new document and within minutes it's searchable.

Semantic search (Vector Search)

Not searching keywords — searching meaning. Ask in natural language and get the most relevant passages, even if you didn't use the exact same words.

Source citation

Every answer comes with the document name, page number, and last update date. Never wonder if the AI made it up.

Continuous updating

Your knowledge updates constantly. We build a pipeline that detects document changes and updates the index automatically — no manual intervention.

Where RAG meets the business

Uses running at our clients

Customer service

An AI rep that knows all product guides, return policies and FAQ answers — responds in 3 seconds instead of 3 minutes of searching.

HR & procedures

Employees ask about vacation days, data security procedures, forms — AI that finds the right procedure and cites from it.

Sales

Sales reps ask 'what's the price for quantity X in category Y' and get a precise answer from the price list — without waiting for a manager.

Testing & support

Engineers and developers searching internal documentation, run-books and incident history — AI that knows where everything is.

FAQ

Everything you wanted to know about RAG systems

A regular chatbot answers from the model's general knowledge — it doesn't know your procedures, price list, or contracts. RAG connects the model to your internal knowledge base and cites sources for every answer — zero hallucinations.

PDF, Word, Excel, PowerPoint, Notion, Confluence, Google Docs, HTML pages, Airtable tables, and more. Any source with an API — can be connected and auto-indexed.

Existing documents (even hundreds of files) — ready for search within hours. After that, sync is automatic: a new document is added to the index within minutes.

Not necessarily. We can deploy the system on your cloud infrastructure (AWS, GCP, Azure) so documents don't leave your organizational environment. Even when using the Claude API — documents are not stored for retraining.