Monday, September 29, 2025
More than 80% of enterprise data exists in unstructured formats like text, images, and documents and this volume is growing by 50–60% every year. For organizations, tapping into this wealth of information represents both a major challenge and a powerful opportunity for generative AI (GenAI).
However, current GenAI approaches often fall short. They are code-heavy, complex, and inconsistent, making it difficult for enterprises to generate meaningful, reliable outcomes.
To solve this, SAS, a global leader in data and AI, has launched SAS® Retrieval Agent Manager (RAM), a new AI solution that converts fragmented, unstructured information into actionable insights, accelerating better business decisions.
RAM empowers enterprises to apply the power of GenAI and large language models (LLMs) to their knowledge base in a scalable, trustworthy, and no-code way. The solution integrates seamlessly with existing systems, enabling organizations to adopt AI from chatbots to intelligent agents without disrupting operations.
“SAS Retrieval Agent Manager transforms fragmented, unstructured information into actionable enterprise knowledge, to make more informed decisions faster,” said Kathy Lange, Research Director for the AI and Automation practice at IDC. “By leveraging generative and agentic AI, RAM provides a user-friendly interface to build and modernize organizational processes without overhauling existing systems.”
Built on the retrieval augmented generation (RAG) framework, RAM ingests and processes unstructured data, then delivers fast, accurate, and context-aware responses. Organizations can interact with this data through APIs or chatbots. The platform also integrates with GenAI services such as LLMs and vector databases while adding an agentic AI layer to automate complex workflows grounded in enterprise data.
According to Jason Mann, VP of IoT at SAS, “SAS Retrieval Agent Manager can scale to very large data volumes that are updated continually. RAM makes it easier for a company to apply technologies like chatbots and conversational AI to its corporate knowledge base, integrate GenAI-powered knowledge services into existing applications via robust APIs, and support the development of AI agents.”
RAM is designed for cross-industry adoption, offering use cases such as:
Banking: Identifying fraud patterns, accelerating compliance checks, and supporting risk assessments.
Insurance: Helping adjusters retrieve claims data instantly for faster, fairer payouts.
Public sector: Empowering contact centers to deliver accurate, policy-aligned citizen services.
Healthcare: Assisting clinicians with HIPAA-safe, evidence-based insights from patient and research data.
Manufacturing: Enhancing predictive maintenance by retrieving insights from manuals, reports, and vendor records to issue clear work orders.
Unlike many AI systems, RAM ensures trust and transparency. It does not use enterprise data to train LLMs. Instead, it keeps data and models separate, providing relevant answers while citing original sources to maintain accountability.
With its no-code interface, cross-industry flexibility, and focus on secure, transparent AI, SAS RAM empowers organizations to finally unlock the full value of unstructured data, turning it into a competitive advantage.
To join our expert panel discussions, reach out to sudipto@intentamplify.com
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