The article explores approaches to integrating chatbots into customer interaction management systems, as well as other digital platforms such as educational, financial, and marketing environments. The aim is to study architectural solutions and algorithms that ensure the productive operation of chatbots in terms of performance, scalability, and flexibility in various user interaction scenarios.

The methodology is based on comparing centralized and decentralized integration models. It examines data transfer protocols such as REST API, GraphQL, and WebSocket. Special attention is paid to natural language processing algorithms, including transformers like BERT and GPT, which can interpret queries, maintain context, and quickly adapt to changes in communication scenarios.

The article also discusses hybrid models combining automation with human operators for handling non-standard situations. Approaches focused on active learning are examined, which improve chatbot performance in real-time.

The results demonstrate that the use of chatbots in customer interaction management systems and e-commerce improves query processing, speeds up responses, and enhances personalization. The application of data analytics opens opportunities for predicting customer behavior and generating proposals tailored to user needs. Issues of data security, encryption, authentication, and access control, which are critical for regulatory compliance, are also considered.

The conclusions highlight the necessity of a comprehensive approach to chatbot design, selecting flexible architectural solutions, adapting to business processes, and implementing machine learning algorithms. The proposed methods are expected to benefit software developers, analysts, marketers, and managers engaged in digital transformation.

chatbots, CRM systems, integration, natural language processing, REST API, machine learning

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