The client came to us with a general idea: build a “smart chatbot.” But instead of jumping straight into interface development, we focused on deeply understanding the company’s internal workflows. We analyzed which tasks were repeated, how employees spent their time, and where automation could actually save resources. The goal was to identify which actions were truly worth automating.
We discovered that the company had accumulated a significant amount of internal knowledge, but it wasn’t being used effectively. First, when employees received new tasks, they had no way to quickly check whether similar problems had already been solved. As a result, work was being duplicated. Second, departments like legal, analytics, marketing, and sales were spending enormous amounts of time preparing reports and summaries — time that could be saved by automating information retrieval and standardizing report formats with the help of AI.
We built an information retrieval and analysis system tailored to real-life use cases taking into account the specific needs of legal, marketing, and sales teams. The system worked with both internal sources (archives, CRM, documents) and external ones (news, websites, open databases). It allowed employees to generate the right type of document based on the task at hand and their role. We also trained the team using real work scenarios, ensuring the tool became part of their daily routine.
During the project, we:
The client got an intelligent decision-support system, rather than just a chatbot. Internal knowledge became structured and searchable. Time spent on analytics dropped. Team efficiency improved.
Instead of starting from scratch every time, employees can now quickly find relevant templates and reuse proven approaches. This has saved hours of work and significantly reduced the need for manual checks and repeated analysis.