A Scalable Microservices-Based Architecture for AI-Driven Salesforce Cloud Applications
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I6P110Keywords:
Microservices Architecture, Salesforce Cloud, Artificial Intelligence, Cloud Computing, Enterprise Systems, Kubernetes, API Gateway, CRM Intelligence, Distributed SystemsAbstract
The fast development of enterprise cloud platforms has led to the shift of the paradigm on intelligent, modular and highly scalable digital ecosystems. Salesforce Cloud is now a leading enterprise customer relationship management (CRM) and cloud platform, being constantly enhanced with artificial intelligence (AI) applications like Salesforce Einstein, predictive analytics, and intelligent automation. Traditional monolithic integration strategies, however, are restrictive to the scalability, flexibility and extensibility needed in modern AI-driven enterprise applications. The proposed paper introduces a microservices based scaled architecture of AI-driven Salesforce Cloud applications that can be utilized in making intelligent decisions, real-time analytics, and to achieve elastic scalability and smooth integration with heterogeneous enterprise systems. The architecture is presented as the breakdown of application functionalities into independent services that are loosely coupled to each other and coordinated with help of Kubernetes and connected by API gateways and event-based messaging platforms. The AI workloads are separated into inference and training services, which are specialized to facilitate efficient deployment of the models and manage lifecycle. The paper proposes a layer architecture that includes presentation, orchestration, business services, AI services, data services, and infrastructure layers. All the layers are fault-tolerant, observable, secure, and compliant. It suggests a hybrid deployment model that will make use of Salesforce Platform Events, REST APIs, MuleSoft, and containerized AI pipelines. Performance assessment shows that performance is better in latency of response, throughput, and fault isolation than in monolithic and conventional SOA architecture. The architecture embraces horizontal scaling, multi-cloud implementation and perpetual delivery pipelines, which is appropriate to enterprise-level digital transformation efforts. The findings suggest that AI architectures built with microservices can significantly contribute to the operational agility and reliability as well as intelligence of business applications built on Salesforce. The framework suggested will act as a guide to companies that want to develop next-generation smart CRM, sales automation, customer analytics and service orchestration solutions.
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