Reg.oblako reduced reporting time to minutes: how an AI agent automated routine and maintained data confidentiality

Reg.oblako employees used to spend hours on routine operations: responding to clients, transcribing meetings, preparing reports and presentations. After implementing their own AI agent, information search time was significantly reduced, and report and presentation preparation took only minutes, all while maintaining complete data confidentiality. Every company has routine tasks that consume employee time: endless information searches, retyping, drafting standard responses. This not only slows down work but also creates risks of data leaks if public AI services are used. Such work is no longer necessary; this burden can now be lifted without sacrificing security.

In any company, routine tasks, though seemingly minor, devour the budget: thousands of hours are spent on searching for information, retyping, and drafting standard responses. This not only slows down work but also creates risks of data leaks if public AI services are used. Multiply these hours by a specialist's rate, and you'll see the cost of "just manual work." This problem has a solution; the question is who in your niche will solve it first.

Where Reg.oblako's time was leaking and why it was critical

At Reg.oblako, as in many modern IT companies, employees faced an enormous volume of information. Every day, they had to respond to client inquiries, transcribe meeting recordings, and prepare presentations and reports. These tasks, though seemingly simple, required significant time and high concentration from highly qualified specialists. Time that should have been dedicated to strategic tasks and development was spent on routine operations.

Searching for necessary data in corporate repositories, systematizing information from disparate sources, and formulating accurate and complete responses — all this consumed hours of valuable working time. A particularly acute issue was data security. Reg.oblako handles confidential information, making the use of external public AI services for its processing unacceptable due to high risks of leaks and loss of data control. This meant that even with ready-made AI solutions on the market, the company could not use them, remaining trapped in manual processes.

The path to a proprietary AI agent

Standard automation methods, such as scripting or creating knowledge bases, only partially helped. They could handle strictly formalized processes but lacked the flexibility and natural language understanding required for diverse queries and unstructured data. For example, a script could find a document by keyword but couldn't extract its "essence" or rephrase information for a specific client request.

The need for quick access to up-to-date information without the constant need to retrain models for each new scenario became evident. The company sought a solution that could not only handle routine tasks but also provide intelligent support, all while maintaining data control. This led to the idea of implementing its own AI agent, capable of working with corporate data, ensuring security and high-speed information processing, and generating meaningful responses in natural language.

How the RAG-based AI agent was designed

The primary goal in design was to create a system capable of efficiently working with large volumes of internal data, understanding query context, and generating accurate, relevant responses. The solution was found in the RAG (Retrieval-Augmented Generation) architecture, which allows the AI agent not just to generate text, but also to extract information from the corporate knowledge base and then use it to formulate a response. This ensured the relevance and accuracy of information without the need for constant model retraining on new data, which is critical for a dynamically developing IT company.

The AI agent was designed to perform several key functions:

• Automatic Transcription. Converting audio recordings of meetings and calls into text, significantly reducing manual transcription time and allowing for quick textual protocols.
• Meaning Extraction and Summarization. The ability to analyze large text documents, such as technical specifications, reports, or articles, and extract key information, forming concise summaries or answers to specific questions.
• Intelligent Support. Rapidly searching for and providing up-to-date information upon employee request, drafting client responses, reports, and presentations based on internal documentation.
• Confidentiality Control. All data was processed within the company's secure perimeter, eliminating risks of leaks and unauthorized access.

A key requirement was seamless integration with existing internal systems and ensuring that all data remained within the company's protected perimeter, never leaving it. This built employee trust in the new tool.

Implementation into operational processes

The AI agent was implemented in stages, starting with the most labor-intensive and repetitive tasks where the benefits of automation would be immediately apparent and have the greatest impact on daily operations. The first step was automating meeting transcription and document summarization. Employees quickly appreciated the benefits, as it immediately freed up a significant amount of time previously spent on manual processing.

Subsequently, the functionality expanded: the agent began assisting in drafting reports, presentations, and detailed client responses. A crucial aspect was training employees to work with the new tool. Instead of replacing people, the AI agent became a reliable assistant, allowing them to focus on more complex and creative tasks requiring human judgment and creativity. The implementation process took several months, during which the system was continuously refined and optimized based on user feedback, enabling it to be maximally adapted to the company's real needs.

Results

The implementation of the AI agent allowed Reg.oblako to significantly increase operational efficiency, reduce labor costs for routine tasks, and minimize risks associated with data confidentiality. Employees stopped wasting valuable time on monotonous tasks, shifting their focus to higher-priority projects that directly impact business development and client satisfaction. This led to increased overall productivity and an improved work environment.

How to implement this in your company

If your company's employees spend a lot of time on routine tasks involving information processing, and data confidentiality is a critical concern, Reg.oblako's case can serve as an excellent example to follow. Here's how to start:

• Identify the most labor-intensive routine processes. Start with operations that consume the most time and do not require complex human judgment (transcription, information search, drafting standard responses).
• Assess confidentiality risks. If you work with sensitive data, consider implementing your own AI agent that operates within your secure perimeter, as Reg.oblako did.
• Design an RAG-based AI agent. This will allow the system to use up-to-date corporate data without the need for constant retraining, ensuring accuracy and relevance of responses.
• Implement in stages. Start with one or two processes so employees can get used to the new tool and see its benefits. Gradually expand functionality, based on user feedback.

If this case sounds like what's happening in your company, our manager can help: he'll analyze your business and niche for free and point out where an AI agent would bring a real result in your case. Message the manager