DeepResearch: How AI Agents Are Changing Work with Corporate Data

AI agents are one of the most discussed topics in the world of artificial intelligence. Although many companies are developing similar solutions, only a few of them actually deliver tangible value. One successful example is DeepResearch – a deep search tool capable of answering complex questions. Many users are familiar with its use in ChatGPT or Perplexity, but these external solutions do not have access to corporate data. Therefore, Sergey Skorodumov's team developed their own version of DeepResearch, adapted for the company's internal needs, which allowed them to save significant time for employees.

In his article, Sergey Skorodumov, head of the search services department, shares his experience in creating and developing DeepResearch, examining in detail the key aspects of AI agent development, methods for improving their effectiveness, and the main conclusions drawn during the work. This is not simply a story about technological development, but a guide to action for companies seeking to optimize their data work with the help of artificial intelligence.

The main task of DeepResearch is to provide employees with fast and efficient access to the necessary information stored in corporate databases and codebase. Traditional search methods often prove ineffective, requiring significant time spent on filtering and analyzing results. An AI agent, by contrast, is capable of understanding complex queries, taking context into account, and providing relevant answers in a short timeframe. This is achieved through the use of modern natural language processing (NLP) and machine learning methods.

One of the key factors in DeepResearch's success is continuous work on improving the quality of its operation. This includes training the model on large volumes of data, optimizing search algorithms, and regularly evaluating results. An important aspect is also integration with existing corporate systems, which enables seamless access to information. Additionally, developers paid special attention to data security and privacy issues to prevent unauthorized access to sensitive information.

The implementation of DeepResearch had a significant impact on the company's operational efficiency. Employees gained the ability to find answers to complex questions faster, which allowed them to focus on more important tasks. Reducing the time spent on information searches led to increased productivity and decreased operational costs. Additionally, DeepResearch contributed to improved decision-making quality, as employees gained the ability to rely on more complete and up-to-date information.

The experience of developing DeepResearch shows that AI agents can become a powerful tool for optimizing work with corporate data. However, to achieve success, it is necessary to pay attention not only to technological aspects, but also to integration, security, and user training issues. In the future, we can expect further development of this direction, with the emergence of increasingly intelligent and adaptive AI agents capable of solving a wide range of tasks.

In conclusion, DeepResearch is an example of the successful application of AI agents to solve specific business tasks. Developing such a solution requires significant effort and expertise, but the results justify the resources spent. Companies seeking to improve the efficiency of their data work should pay attention to the possibilities offered by AI agents.