DeepResearch: How AI Agents Are Changing the Way Companies Work with Internal Data

AI agents are one of the hottest topics in artificial intelligence. Plenty of companies are building them, but only a few deliver real, measurable value. DeepResearch is one of the success stories: a deep search tool that can answer complex questions. Many users already know it from ChatGPT or Perplexity, but those external products have no access to corporate data. So Sergey Skorodumov's team built its own version of DeepResearch, tailored to the company's internal needs, and it has saved employees a significant amount of time.

In his article, Sergey Skorodumov, head of the search services department, shares how his team built and evolved DeepResearch. He covers the key aspects of developing AI agents, ways to make them more effective, and the main lessons the team learned along the way. The piece goes beyond a story about building a technology: it works as a practical guide for companies that want to use AI to streamline how they work with data.

DeepResearch's main job is to give employees fast, effective access to the information they need from corporate databases and the codebase. Traditional search often falls short and forces people to spend a lot of time filtering and analyzing results. An AI agent, by contrast, can understand complex queries, take context into account, and return relevant answers quickly. It does this with modern natural language processing (NLP) and machine learning techniques.

One key to DeepResearch's success is the team's ongoing work on quality. That includes training the model on large volumes of data, optimizing the search algorithms, and regularly evaluating the results. Integration with existing corporate systems also matters, since it enables seamless access to information. The developers also paid close attention to data security and privacy to prevent unauthorized access to sensitive information.

Rolling out DeepResearch had a significant impact on the company's efficiency. Employees can now find answers to complex questions faster, which frees them to focus on more important work. Less time spent searching for information has raised productivity and cut operating costs. DeepResearch has also improved decision-making, because employees can now rely on more complete and up-to-date information.

The experience of building DeepResearch shows that AI agents can become a powerful tool for streamlining work with corporate data. But success requires attention not only to the technology but also to integration, security, and user training. Expect this area to keep developing, with increasingly intelligent and adaptive AI agents capable of handling a wide range of tasks.

In short, DeepResearch is an example of AI agents successfully applied to specific business problems. Building a tool like this takes significant effort and expertise, but the results justify the resources invested. Companies that want to work with their data more efficiently should look closely at what AI agents can offer.