RAG for Business: How AI works with internal company data

1. What is RAG and why does your company need it?
2. What can RAG do and why does a business choose it?
3. When is a full-fledged system not needed and easier to do?
4. How does RAG work in corporate scenarios?
5. Implementation examples
6. How to make a RAG system?
7. Advantages of implementation
8. RAG using the LighTech product example
9. What difficulties can you face?
10. RAG as a new standard for corporate AI

Entrepreneurs sometimes find themselves in situations where AI cannot answer the simplest questions about their business. «What are our current promotions for corporate clients?» or «Who is currently responsible for sales?» The response is always the same: «I don't have that information.»

Traditional AI models only know what they've been trained on public data, and they don't see your internal documents, databases, and processes.

RAG (Retrieval-Augmented Generation) solves this problem: the technology combines powerful language models with the ability to extract relevant information from internal sources in real time.

It is estimated that by 2030, up to 80% of project management tasks will be automated using AI, from documentation analysis to decision preparation and reporting.

This article will demonstrate how RAG works in practice and explain why companies that use these systems have a significant advantage.

What is RAG, and why is it important for your company?

RAG is a technology that enables AI to work with your organization's internal data and provide accurate, customized answers.

Here's how it works:

1. Your data is uploaded to a special database.

2. When a question is received, the system automatically finds the necessary information.

3. This data is transmitted to the AI along with the question.

4. AI generates a response based on your documents and regulations.

In simple words, an ordinary AI is like an outside consultant: smart, but knows nothing about your company. RAG is the same consultant, but he has been given access to all your documents, knowledge bases, and internal instructions. It can answer questions accurately, quickly, and concisely.

From a technical point of view, RAG combines several key areas:

integration of data from different sources;
predictive analytics for risk assessment and scenarios;
continuous learning based on new data;
automatic decision support.

RAG uses advanced mechanisms, including data splitting into semantic blocks (chunking), vectorization to search for relevant information, query transformation, and final assembly of the response based on the found data.

What can a RAG system do and why does a business choose it?

RAG is used to support decision-making in specialized areas. This system works with industry data, reference books, and internal methodologies and protocols to help specialists quickly obtain relevant information without a manual search.

Another popular scenario is technical support services. With RAG, you can find solutions based on the knowledge base, request history, and technical documentation.

RAG systems are also used to generate analytical reports and summaries. Using data from internal documents, tables, and knowledge repositories, AI can generate summaries and reports on specified queries, saving analysts and managers time.

RAG is used for consulting support in client scenarios, providing up-to-date information about products, services, tariffs, and conditions.

An important area is working with HR data and internal knowledge. RAG helps employees quickly locate information on internal policies, procedures, training materials, and corporate rules.

When is a full-fledged RAG system unnecessary?

Not every organization needs a RAG tool. Sometimes, a simple solution that enables AI to respond to basic queries is sufficient.

1. There is not much information

Data up to about 1000-1500 words:

Office opening hours.
Contact information.
A short price list (5-10 items).
Addresses of points of sale.

2. Data rarely changes

For example, the company's history, basic principles of operation, and standard answers to standard questions.

This approach is ideal for small businesses and simple queries, allowing AI to provide correct answers without implementing a full-fledged RAG system.

How does RAG work in enterprise scenarios?

RAG works like an experienced employee: first it finds the necessary information in the internal data, and only then it forms a response.

1. Data connection

The system gets access to your documents, knowledge bases, catalogs, instructions, and FAQ. The data is cleaned, structured, and prepared for searching by meaning, not by words.

2. Context search

AI uses the context and rules of communication you provide to give an accurate answer based strictly on your data. If necessary, it will indicate the source.

3. Forming a response

AI uses the found context and rules of communication to give an accurate answer strictly based on your data, if necessary — with an indication of the source.

Do you want to know how much it costs to develop a RAG system for a business?

We will analyze your tasks, data sources and usage scenarios and propose the optimal solution for your budget.

Discuss the project

Examples of the implementation of RAG systems

RAG is necessary when the volume and dynamics of the data exceed the usual prompts and instructions.

Scenarios	What the business is facing	Solution with RAG
Online store or large catalog	There are hundreds or thousands of products in the catalog. Each one has its own characteristics, price, availability, and variations. The range is constantly being updated, and customers are asking specific questions: "Are there noise-canceling headphones up to 30 dollars?"	RAG connects to the current catalog, selects products based on specified criteria, and sends only the relevant items to AI. The response is based on real balances and prices.
B2B company with tariffs and conditions	There are multiple tariffs and service packages with different conditions for different customer segments. Prices, promotions, and contracts change regularly. Clients and managers both have questions.	RAG works with current commercial offers and contracts, so AI responds strictly within the framework of the current conditions.
Internal knowledge base for employees	Regulations, instructions, and policies are scattered across different systems. It is difficult for new employees to quickly figure out where to look for the necessary information.	RAG finds the necessary fragment in the documents and generates a response based on the current version of the regulations.
Technical support and service teams	There are many similar questions with different contexts. It is important to consider both the product version and the customer's history.	RAG relies on a ticket database and documentation to ensure that the AI provides accurate and consistent responses.
Legal and consulting services	There is a large volume of documents, judicial practices, and regulations. Each request must be analyzed individually and precisely worded.	RAG searches for relevant precedents and legal norms, summarizes them, and cites sources. This speeds up the work of specialists without replacing their expertise.
Internal Corporate Assistant	Employees spend time searching for information in Drive, Confluence, mail, and chat rooms. It is often unclear where the current version of the document is located.	RAG integrates with corporate systems and, upon request, provides up-to-date instructions with a link to the original document.
Educational platforms and online courses	A large volume of educational materials and repeated questions from students. Teachers spend time on the same type of explanations.	RAG uses lectures and course manuals so that AI explains the material in its own words, based on educational sources and the student's level.

How to make a RAG system?

At LighTech, we develop RAG systems for businesses for specific tasks and integrate them into the company's existing infrastructure.

1

Analysis of tasks and infrastructure

We define automation tasks such as request processing, customer and employee support, document management, and report preparation. Then, analyzing the client's infrastructure — including CRM, ERP, messenger, and BI systems — we choose the best integration methods while taking security and data storage requirements into account.

2

The design of use scenarios

We study the current processes and create scenarios for using the RAG system. Our team considers the architecture, logic, and ways users can interact with the system to ensure fast, accurate responses.

3

Development and integration

We implement the functionality of the RAG system for working with chats, e-mail, API and other channels:

- search and compilation of information from internal and external sources;

- running business processes according to the set rules;

- integration with CRM, task managers and knowledge bases;

- adaptation of the system based on user feedback.

4

Testing and quality control

We verify the accuracy of responses, test stability under load, and handle standard and non-standard requests.

5

Introduction to the corporate environment and support

We are integrating RAG into the corporate ecosystem, including messengers, internal portals, CRM, and ERP. We set up access rights and logging, update data, and provide technical support.

Advantages of implementing RAG

Advantage	How it works
Accuracy and verifiability	AI responds based on the latest corporate data and documents. Each answer can be verified, reducing the risk of errors and increasing trust.
Real-time relevance	The system takes into account any updates to tariffs, rules, or documents without additional training for the model.
Speeding up the work of employees	Automatic information search, preparation of summaries and reports save time for employees and accelerate the adaptation of newcomers.
Improving the quality of customer service	Fast, accurate, and personalized responses, including complex queries, increase customer satisfaction and loyalty.
Lower operating costs	Less manual work on information retrieval, reporting, and employee training.
Scalability and flexibility	The system can be easily expanded to work with large amounts of data, new sources and interfaces, while maintaining the quality of responses.
Sharing knowledge between departments	AI uses data from different departments, which improves understanding of processes and helps to make more informed decisions.
Analytical support and forecasting	RAG can generate summaries, reports, and forecasts based on corporate data, facilitating strategic planning.
Risk reduction and compliance	Automatic data up-to-date verification helps to avoid violations of regulations and errors in documentation.

RAG system example: the LighTech product

A good way to understand the value of RAG is to look at how this technology works not in theory, but in the product. One such example is LighTopic, an AI secretary for communications, which we developed based on the RAG approach.

The goal of the product is to transform meetings, calls, and correspondence from «lost conversations» into structured and accessible corporate memory.

In LighTopic, the data source is not only documents, but also live communications: hangouts in Zoom, Teams, Google Meet, chats, notes, and related files.

Each call is automatically recorded, decrypted and saved in the system. Next, the data is cleaned, normalized, and broken down into semantic blocks — by topics, issues, decisions, and agreements.

When a user asks a question, for example, «what did we agree on with the client last week?» or «what tasks were discussed on Project X?» — the system analyzes the meaning of the request, determines its intention and compares it with the context of the accumulated data.

The RAG engine selects relevant fragments from:

transcripts of meetings;
sammari;
tasks and solutions;
related documents.

As a result, the user does not receive a list of mentions, but an already compiled response with key agreements, responsibilities, and deadlines, even if the wording in the question and in the original discussions differ.

What difficulties can you face when implementing RAG?

The RAG approach provides companies with significant advantages, but its implementation in processes requires preparation. In practice, organizations most often face the following challenges.

Data security and confidentiality

It is important for any company to protect internal documents and customer data. When working with RAG, it is necessary to determine in advance who has access to what information, as well as use encryption and isolated storage. This is especially important if the system is accessing external data sources.

Integration with current systems

RAG should be "integrated" into the existing IT infrastructure: CRM, ERP, knowledge bases, internal portals. This requires proper API configuration, data synchronization, and operation logic. Without regular testing and clear metrics, it is difficult to make sure that the system really gives relevant answers.

Scalability

As the amount of data increases, the load on the system increases. If the architecture is not thought out in advance, responses may become slow or less accurate. In practice, this problem is solved through cloud infrastructure, search optimization, and data management.

Cost of implementation and support

Costs include infrastructure, setup, model training, and maintenance. Companies can start with one or two scenarios (for example, employee or customer support) and scale the solution after receiving the first results.

Adaptation to industry and specifics

Each area has its own language, terms, and context. Without further training, RAG may misinterpret professional concepts or give formal answers. Learning from industry data and gradually improving the model as it is used helps to increase accuracy.

RAG as a new standard for corporate AI

RAG is becoming the next step in the development of analytics and enterprise AI systems. It combines AI logic, internal company data, and up-to-date information in real time, providing accurate and informed answers.

Unlike conventional language models, RAG relies on validated data rather than abstract knowledge. This makes it a reliable tool for decision-making, customer support, and process automation.

In fact, RAG transforms AI from a "smart conversationalist" into a full-fledged digital business assistant.