Background. Small and medium-sized businesses in Ukraine operate under two simultaneous pressures: a prolonged martial law with strikes on energy infrastructure, and the need for digital transformation to remain competitive. Existing ERP and CRM systems assume stable infrastructure and large implementation budgets and are poorly suited to partial-availability operation, fast migration between on-premises and cloud environments, and phased deployment within tight budgets. This creates a need for architectural solutions for the class of small multi-channel retail-service businesses. Objective. The paper aims to develop the architecture and algorithms of a modular digital automation system for the multi-channel retail-service small business class under martial law conditions, and to propose a verification and validation methodology for the microservice implementation of its components. Methods. The architecture follows the principle of one microservice per business function and distributes responsibilities among nine services. Service interactions go through standardised APIs (REST, gRPC) and asynchronous messaging (RabbitMQ). The verification and validation methodology covers unit, contract, integration, end-to-end, and chaos testing. Validation was carried out on four enterprise types: retail with service support, e-commerce, food service with catering, and a full production-and-sales model. Results. The architecture supports partial-availability operation when individual services fail, portability between on-premises and cloud infrastructure, phased deployment within tight budgets, and the absence of vendor lock-in due to the open-source stack. The proposed verification and validation methodology covers five testing levels and ties each architectural concern to a specific check type. Conclusions. The modular microservice architecture is suitable for the class of multi-channel retail-service small businesses operating under unstable infrastructure conditions. Further work covers the collection of quantitative operational metrics and the integration of machine-learning-based forecasting modules.
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