AveriSource TransformDeployment Reference Architectures
The AveriSource Platform reference architectures provide optional deployment scenarios, based on your application re-architecture and implementation goals. We provide reference architectures for the most common cloud service providers: AWS, Azure, Google Cloud, Oracle Cloud, distributed environments, your internal or outsourced data center, as well as application deployment back to the IBM mainframe using Red Hat® OpenShift® on z/Linux or LinuxONE.
AWS Reference Architecture
AveriSource Transform accelerates re-architecture and deployment to multiple target environments and is designed to automate and streamline legacy code transformation and application modernization. These reference architectures leverage scalable cloud service provider infrastructure, multi-environment support, and fault-tolerant designs to ensure high availability and efficient application deployment across distributed, virtual, and cloud platforms. Transform enables implementation of modernization best practices and processes, reducing manual efforts and optimizing application migration to modern cloud environments like AWS.
AWS Gen AI Reference Architecture
AveriSource reference architectures leverage scalable cloud service provider infrastructure, multi-environment support, and fault-tolerant designs to ensure high availability and efficient application deployment across distributed, virtual, and cloud platforms. AveriSource GenAI solutions combine the AveriSource knowledgebase with popular Large Language Models (LLMs) for rapid creation of Business Requirements Documents using Natural Language Processing (NLP). Averi™ for AveriSource Discover utilizes the same approach to accelerate the generation of application modernization assessments. These new GenAI solutions integrate with AWS Bedrock and Anthropic Claude 3 for cloud generation of modernization assessments and business requirements documentation on AWS.
Azure Reference Architecture
AveriSource Transform accelerates re-architecture and deployment to multiple target environments and is designed to automate and streamline legacy code transformation and application modernization. These reference architectures leverage scalable cloud service provider infrastructure, multi-environment support, and fault-tolerant designs to ensure high availability and efficient application deployment across distributed, virtual, and cloud platforms. Transform enables implementation of modernization best practices and processes, reducing manual efforts and optimizing application migration to modern cloud environments like Microsoft Azure.
Google Cloud Reference Architecture
AveriSource Transform accelerates re-architecture and deployment to multiple target environments and is designed to automate and streamline legacy code transformation and application modernization. These reference architectures leverage scalable cloud service provider infrastructure, multi-environment support, and fault-tolerant designs to ensure high availability and efficient application deployment across distributed, virtual, and cloud platforms. Transform enables implementation of modernization best practices and processes, reducing manual efforts and optimizing application migration to modern cloud environments like Google Cloud.
IBM Reference Architecture
AveriSource Transform accelerates re-architecture and deployment to multiple target environments and is designed to automate and streamline legacy code transformation and application modernization. These reference architectures leverage scalable cloud service provider infrastructure, multi-environment support, and fault-tolerant designs to ensure high availability and efficient application deployment across distributed, virtual, and cloud platforms. Transform enables implementation of modernization best practices and processes, reducing manual efforts and optimizing application migration to modern cloud environments like IBM.
Oracle Cloud Reference Architecture
AveriSource Transform accelerates re-architecture and deployment to multiple target environments and is designed to automate and streamline legacy code transformation and application modernization. These reference architectures leverage scalable cloud service provider infrastructure, multi-environment support, and fault-tolerant designs to ensure high availability and efficient application deployment across distributed, virtual, and cloud platforms. Transform enables implementation of modernization best practices and processes, reducing manual efforts and optimizing application migration to modern cloud environments like Oracle Cloud.
Distributed Reference Architecture
AveriSource Transform accelerates re-architecture and deployment to multiple target environments and is designed to automate and streamline legacy code transformation and application modernization. These reference architectures leverage scalable cloud service provider infrastructure, multi-environment support, and fault-tolerant designs to ensure high availability and efficient application deployment across distributed, virtual, and cloud platforms. Transform enables implementation of modernization best practices and processes, reducing manual efforts and optimizing application migration to distributed multi-tier web application environments.
Gen AI Reference Architecture
AveriSource reference architectures leverage scalable cloud service provider infrastructure, multi-environment support, and fault-tolerant designs to ensure high availability and efficient application deployment across distributed, virtual, and cloud platforms. AveriSource GenAI solutions combine the AveriSource knowledgebase with popular Large Language Models (LLMs) for rapid creation of Business Requirements Documents using Natural Language Processing (NLP). Averi™ for AveriSource Discover utilizes the same approach to accelerate the generation of application modernization assessments. These new GenAI solutions integrate with Meta Llama 3.1 for generation of modernization assessments and business requirements documentation on distributed environments.
