"Size matters"...what's the point?

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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 sampling generation method (RAG) were used to generate a response. It is this set of tools that usa telegram database 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 companies solve?

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.

IT Channel News: Does your product function as a separate system or can it be integrated into other ready-made IT solutions?

S. Shch.: Our system can be used in any way. It can work as a standalone solution, but it is also possible to add Gen.AI to your existing system to enhance its functionality and add more intelligence. For example, we can integrate with a request system or ITSM, adding the full range of product capabilities - from deep analysis of requests, including attached files, to classification, generating prompts for operators and automatic responses to users.