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Pega to expand GenAI framework with Google Cloud & AWS, to allow for enterprise GenAI choice

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Pegasystems Inc. announced an expansion of its Pega GenAI capabilities to connect to Amazon Web Services (AWS) and Google Cloud’s Large Language Models (LLMs). The additions are designed to allow Pega clients to effectively leverage the integration of generative AI technologies powered by AWS and Google Cloud into the decisions and workflows within Pega Platform. These services will be on display at PegaWorld iNspire, the annual conference at the MGM Grand in Las Vegas on June 9-11. The new capabilities are expected to be available in the second half of 2024.

With this expansion, Pega’s enterprise platform will provide clients the ability to connect with a vast range of generative AI services and models with Pega GenAI architecture to support generative AI models within Pega solutions. These will include solutions from AWS, including Amazon Bedrock – a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies via a single API – and Amazon Titan; from Google Cloud, including Vertex AI and Google Gemini; and Claude from Anthropic. This provides a broad set of capabilities organizations need to build generative AI applications with security, privacy, and responsible AI.

Enterprises that leverage Pega for generative AI-infused development, engagement, service and back-to-front office operations workflows will benefit from unified governance, auditability, and controls across all applications of generative AI throughout their operations. Pega GenAI is available to all Pega clients through Pega Cloud.

AWS and Google Cloud generative AI models will be available in Pega Connect GenAI, a plug-and-play architecture that allows low-code developers to author prompts and get immediate value from generative AI in any workflow or decision. This enables Pega low-code developers to build custom generative AI-powered capabilities into their workflows to help boost the productivity of employees and agents interacting with them. For example, suppose a process, such as a claims or approvals workflow, includes a range of documents. In that case, Pega GenAI can be used to build a component to summarize documents on the fly and give end-users an at-a-glance overview of critical information when they open their assignments.

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