RAG systems and intelligent document search

Introducing LLM into business: corporate knowledge base with AI, document search and AI assistant for employees

Employees spend up to 20% of their working time searching for information in documents, regulations, and instructions. The RAG system finds an accurate answer in seconds - from PDF, knowledge base, and corporate archives — and responds in natural language.

Submit your application

info@softrest.ru

CSS API Laravel MariaDB MySQL LLM Docker Scala Typescript PHP Python Ubuntu Django WEB AI Sockets PostgreSQL CRM HTML FastAI Bash VPS JS Redis REST React ERP Vue.js TensorFlow

The company's knowledge is working, not gathering dust in folders

Document search

Employees find the answer in seconds: the AI searches through regulations, instructions, PDFs, and archives — without manually going through the files.

Knowledge base with AI

Corporate knowledge base with artificial intelligence: automatic indexing, semantic search and relevant answers.

No risk

Free document audit, transparent budget, and step-by-step implementation with checkpoints.

Personalization

The RAG system learns from your data: regulations, knowledge bases, PDFs, instructions, and corporate documents.

Creation of intelligent search and RAG systems

RAG systems

Development of full-cycle RAG systems: document loading, vector storage, LLM response generation and integration with corporate services.

Search for documents with AI

Intelligent search of PDFs, regulations, instructions and company archives — semantic search instead of keywords.

Corporate knowledge base

Knowledge Base with AI Assistant: a single knowledge repository with AI search, auto-update, and role-based access for employees.

Chatbot for documents

An AI chatbot for the company that answers employees' questions about the knowledge base, regulations, and internal documents.

Corporate LLM Assistant

An AI assistant to support employees: training, onboarding, answering questions about processes, and helping the support service.

Implementation of LLM in business

Integration of language models into the company's existing systems: CRM, ERP, databases, document management and internal portals.

Expertise and technology

LLM and RAG

Architecture of RAG systems, selection and fine tuning of models, optimization of the quality of responses to corporate data.

APIs and integrations

Communication with CRM, ERP, document management systems, cloud storage and external services is a single knowledge loop.

Software development

Backend, vector databases, document processing pipelines, REST API, and scalable infrastructure.

AI agents

Intelligent agents for automating document search, classification, and contextual response generation.

How to implement RAG systems and intelligent search

We analyze corporate documents, evaluate the volume of the knowledge base and form the architecture of the RAG system.

LLM selection, pipeline architecture, indexing strategy, integration, and timing estimation.

MVP of document search: uploading data, configuring RAG, testing the quality of responses, and launching.

Team training, knowledge base expansion, quality monitoring and functionality development.

We will find a solution for your needs.

The RAG system

Intelligent search of company documents: PDF download, regulations and instructions, semantic search and generation of answers based on your data.

from 300,000 ₽

Corporate knowledge base with AI

A full-fledged knowledge management system: RAG search, chatbot for documents, role-based access, integration with CRM/ERP and an AI assistant for employees.

from 500,000 ₽

Frequent questions

RAG (Retrieval-Augmented Generation) is a technology in which AI first finds relevant fragments in your documents, and then generates an accurate response based on the information found. Unlike regular search, RAG understands the meaning of the query and responds in natural language.

PDF, Word, Excel, text files, regulations, instructions, knowledge bases, event recordings — any corporate documents. The system indexes the content and makes it available for intelligent search.

A regular search searches by keywords. RAG understands the semantics of the query: it finds relevant fragments even with a different wording and generates a coherent response indicating the source.

MVP with document search — from 3 to 6 weeks. A full-fledged corporate knowledge base with an AI assistant lasts from 2 to 4 months, depending on the amount of data and integrations.

Yes. A RAG system with a knowledge base replaces classic wikis: employees ask a question and get an answer instead of manually searching through articles and sections.

Discuss the implementation of the RAG system

+7 (981) 407-17-17 info@softrest.ru

Why companies are implementing RAG systems and intelligent document search

Corporate knowledge — regulations, instructions, knowledge bases, and document archives—is growing faster than employees' ability to navigate them. The classic keyword search fails: the results are inaccurate, the context is lost, and new employees spend weeks adapting. The RAG system solves this problem: LLM analyzes the request, finds relevant fragments in corporate documents and generates an accurate response indicating the source. It's not just a PDF search, it's an AI knowledge base assistant that understands the meaning of the question and answers in natural language. The introduction of RAG reduces the time spent searching for information, accelerates the onboarding of new employees, reduces the burden on the support service and turns the accumulated knowledge of the company into a working asset. Leave a request and we will find a solution for your tasks.

RAG systems and intelligent document search

Introducing LLM into business: corporate knowledge base with AI, document search and AI assistant for employees