Architectural Challenges in Building Scalable B2B Products Using Multimodal AI Models

The article presents a comprehensive analysis of the architectural challenges that arise when building scalable B2B products based on multimodal models. The study draws on a systematization of modern approaches to integrating textual, visual, voice, and structured data, as well as on an examination of architectures designed for extended context, sparse expert modules, adaptive layers, and composite agent-based systems. The discussion addresses how multimodality affects computational cost, interface stability, data organization, and corporate infrastructure requirements. Particular attention is given to the interplay between accuracy and scalability. It is shown that architectural choices create their own trade-offs, shaping both the depth of data interpretation and the degree to which the model depends on computational resources. The article analyzes key limitations that emerge in B2B environments, including data heterogeneity, the complexity of modality integration, preprocessing requirements, and the multi-layered nature of enterprise orchestration. The findings indicate that the performance of multimodal systems depends not only on model quality but also on the coherence of infrastructural, computational, and organizational components. The study concludes that multimodal AI becomes a system-forming element of corporate platforms, defining requirements for architecture, data, and engineering processes. The article will be useful for AI researchers, corporate product developers, B2B system architects, and specialists implementing multimodal solutions within large digital ecosystems.