RAG Systems
RAG Development
Retrieval-Augmented Generation systems that give LLMs access to your knowledge: documents, databases and internal data — with accurate, sourced answers.
Who it's for
- Companies with large volumes of documents and knowledge bases
- Businesses that need an AI assistant over internal data
- Teams tired of searching for answers across 1,000 files
Outcome
- An AI assistant answering from your data with sources
- Semantic search across documents and databases
- A system with freshness control and access rights
Problems it solves
An LLM without your company's context gives generic answers. RAG systems connect the model to your data — and answers become accurate, current and verifiable.
Who it's for
Knowledge is scattered across documents, chats and databases
Employees waste time searching for answers manually
The LLM answers in generalities without your context
Hallucinations — the model invents facts without sources
Data changes, but knowledge does not update
When you need it
You have documents that require precise answers
You need an assistant for policies, products or code
Source references are required in every answer
Data changes — you need an up-to-date knowledge base
Approach
We design RAG as a system: index quality, search relevance, answer evaluation — from prototype to production.
01
Discovery
Research, requirements and AI opportunity analysis
02
Architecture
AI system design: agents, RAG, data layer
03
MVP
Working product delivered in weeks, not months
04
Production
Deployment, monitoring, scaling and iteration
Technical expertise
The stack for production-ready RAG systems.
RAG
Retrieval-augmented generation: index, search, generation
OpenAI / Claude / Gemini
Models for answer generation and embeddings
OpenRouter
Model unification and cost control
AI Agents
Agents for document processing and routing
PostgreSQL
Document and metadata storage
React / Next.js
Assistant and admin interfaces
Expert articles
How we approach this in our own products — from the knowledge base.
FAQ
What is RAG and why do we need it?
RAG (Retrieval-Augmented Generation) is an approach where the LLM first searches for the answer in your data and then generates a response based on it. This gives accuracy and sources instead of generic phrases.
Which documents are suitable for a RAG system?
Any text data: policies, documentation, contracts, knowledge bases, articles. We prepare the pipeline for loading, cleaning and indexing your formats.
How do you solve the hallucination problem?
Answers are built only from found documents, and every answer comes with source references. If data is insufficient — the system honestly says so.
Can access to some documents be restricted?
Yes. We design document-level access rights: different roles see different sources, and permissions are checked on every request.
How does data get updated?
We build indexing pipelines: documents can be added manually or automatically from your systems. The system tracks versions and re-indexes changes.
How much does a RAG system cost?
An MVP with a single data source starts from $2,000-4,000. A full system with integrations, permissions and updates is discussed after analyzing your data.