Modern retail is faced with the need to simultaneously improve customer service and optimize operating costs, which is driving the active adoption of digital solutions to automate customer interactions. This article examines the use of dialog agents to automate key processes in retail—from initial consultation to order processing and follow-up. The aim of the study is to develop and evaluate an integrated system based on a dialog agent interacting with the 1C: Trade Management corporate system. The paper presents an analysis of the retail network's domain, describes the key business processes subject to automation, and proposes a dialog agent architecture and its integration with 1C. The paper also examines CASE tools for functional modeling and analysis of company operations and presents a rule-based approach to developing conversational agents based on a dictionary-pattern concept with regular expressions and keywords. The results showed that the use of conversational agents not only reduces operating costs but also increases sales conversion. The study confirms that conversational agents are an effective tool for digital retail transformation, combining scalability, personalization, and cost effectiveness

Keywords: Sales automation, retail, conversational agent, integration, online ordering, business processes, chatbot, trade management

In order to provide information support for decision-making on the issuance of bank guarantees for the execution of a contract in the field of public procurement, it is important for banks to obtain historically accumulated information on the execution of government contracts. This is necessary to assess the possibility of the supplier's performance of his future contract. This can be done by collecting and aggregating information about contracts from the Unified Information System in the field of procurement. The paper proposes to use IT technologies and data analysis to predict the performance of the contract and identify bona fide suppliers. In the work, a selection of primary data on contracts was formed for modeling using the parsing of the FTP server of the Unified Information System in the field of procurement, and the parsed data was preprocessed for use in machine learning models.

Keywords: information system, data analysis, government contract, data parsing, machine learning