First Dataset

S. Shch.: In order to teach the neural network to solve certain stages of work with requests with the highest possible quality, we used an ensemble of models in our product. To analyze requests and attached files, we used a tool for natural language processing (NLP) and document recognition (OCR), and to identify key points in requests, we used entity extraction (NER). Classic machine learning algorithms (ML) were used to classify and route requests, and, of course, large language models (LLM) together with the augmented turkey telegram database sampling generation method (RAG) were used to generate a response. It is this set of tools that allows us to comprehensively analyze a request and generate relevant responses.

IT Channel News: Why do you think this product is relevant in the market today? What tasks can it help solve for companies?

S. Shch.: Every day, companies with a large client audience generate thousands of requests that need to be resolved as quickly and accurately as possible. These can be companies from different areas - retail, banks, government agencies and many others. Our product can help them improve the quality and speed of customer service, optimize business processes, automate routine operations and free up employees' time to solve more important tasks. In addition, it allows you to quickly analyze large volumes of historically accumulated requests to draw conclusions and support decision-making and business development.

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