How is RAG different from training your own model?

RAG does not retrain the language model on every document. Before responding, the system finds suitable fragments in the knowledge base and passes them on to the model as context, so it is easier to update and check information against sources.

What sources can be connected to the RAG system?

Documents, Wiki, databases, CRM, helpdesk, cloud drives and internal portals. For each source we configure extraction, updating, metadata and access rights.

Can I use PDF scans and document images?

Yes. We add OCR, recognition quality checking and table processing, if required. During the pilot, we separately test complex scans and documents with a non-standard structure.

How does RAG account for employee access rights?

The user receives only those fragments to which he has access in the source system or in the rules of the RAG loop. Activities and sources used can be logged for auditing purposes.

How often is the knowledge base updated?

Scheduled, event-based, or near real-time, depending on the source. Changed documents are reindexed without completely retraining the language model.

How much does it cost to develop a RAG system?

A pilot internal consultant usually starts from 350,000 rubles. The cost is influenced by the number of sources, quality of documents, access rights, selected LLM, infrastructure and integrations.

How to check the quality and reduce the number of made-up answers?

We collect a control set of questions, measure the completeness of the search and the correctness of the answer, show sources and set up refusal to answer if there is insufficient data. The quality is regularly checked after updating the database.

Is it possible to run RAG inside a company loop?

Yes. We deploy search, storage and LLM on-premise or build a hybrid circuit where sensitive data remains within the company.

Development of a RAG system and AI consultant

We are introducing a corporate AI consultant for support, sales and internal teams: RAG or hybrid search with AI, LLM connection, integration with CRM and portals.

We are preparing the infrastructure for the task: on-premise within the company or rental capacity (RU/EU/US). We work with both foreign models and Russian LLMs.

Discuss an AI consultant

What is included in the service

Turnkey solution: from search architecture and knowledge base to production launch and KPI.

— Design of RAG and hybrid search: vector + keyword/SQL circuit
— Preparation and indexing of corporate data: documents, Wiki, CRM, regulations
— Connection of LLM: foreign and Russian models according to security and budget requirements
— Deployment on your server or in rental infrastructure (cloud/VPS/DC)
— Setting up access roles, logging, quality control and fallback scenarios
— Integration with the portal, CRM, helpdesk, chats and internal services

RAG and hybrid search

Accurate answers based on your knowledge base, not the “fantasies” of the model.

Server for the task

On-premise or rental of capacities in the Russian Federation and abroad.

Flexible LLM selection

We connect foreign and Russian models in a single circuit.

Data Security

Access control, query auditing and storage policies.

Integrations with business systems

CRM, portal, ticket database, mail and documents.

Stable production

Monitoring, response quality metrics and SLA support.

Measurable result

Reduce support burden and time spent searching for information.

Team training

User onboarding and practices of working with an AI consultant.

3-6 weeksLaunching the pilot

-35%Load on 1st line

<3 secAverage assistant response

24/7Consultant Availability

Stages of implementing an AI consultant

We fix business goals, raise the infrastructure, set up the RAG layer and launch it into real work.

Data and script audit

We determine what questions the consultant should solve and what sources are needed for accurate answers.

Search architecture and LLM layer

We design RAG/hybrid search, select models and form a security loop.

Server preparation and deployment

We deploy the solution within the company or in a rental infrastructure, set up CI/CD and monitoring.

Integrations with business systems

We connect CRM, portal, chat platforms and documentation, configure access rights.

Testing the quality of answers

We check accuracy, completeness and stability using a control set of queries.

Launch and optimization

We put the solution into operation, train users and fine-tune it based on real metrics.

Related projects with AI and enterprise data

Examples of AI services, integrations and interfaces. The specific RAG architecture of each new project is designed according to its sources and access rights.

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Development of a RAG system and AI consultant

We are introducing a corporate AI consultant for support, sales and internal teams: RAG or hybrid search with AI, LLM connection, integration with CRM and portals.

What is included in the service

Stages of implementing an AI consultant

Data and script audit

Search architecture and LLM layer

Server preparation and deployment

Integrations with business systems

Testing the quality of answers

Launch and optimization

Related projects with AI and enterprise data

Universal AI platform AI2Media

AI agent for realtor of the agency “Everything for Rublyovka”

AI analyzer of Yandex and 2GIS cards for lead growth

Calculation of the cost of a RAG consultant

Total

Type of RAG solution

Basic business scenario

Placement outline

Infrastructure and deployment stack

Integrations and additional work

Total

Who develops the RAG system

Frequently asked questions about RAG systems

Contact information and details

Saint Petersburg

Tampa, FL, US

nbm-it@mail.ru

Services

Development

Services

Marketing

About us