More than 80% of enterprise data is in unstructured formats such as text and images, and this huge amount of data is growing 50% – 60% annually. Unlocking its value is one of the most significant challenges – and opportunities — for GenAI.
Too often, however, current GenAI approaches are code-heavy, complex and inefficient. They fail to deliver consistent and compelling results.
Enter a new AI offering from SAS, a leader in data and AI, that supercharges business productivity. SAS® Retrieval Agent Manager (RAM) streamlines the process of transforming raw unstructured data within a company’s knowledge base into quick and relevant answers that support better business decisions.
RAM addresses fundamental challenges faced by all industries and business leaders, including how to:
- bring the power of GenAI and large language models (LLMs) to their enterprise data in a quick, scalable and trustworthy way
- seamlessly integrate AI – from chatbots to sophisticated agents – into existing systems with confidence.
“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 analyst firm IDC. “By leveraging generative and agentic AI, RAM provides a user-friendly interface to build and modernize organizational processes without overhauling existing systems.”
How it works
Built on the retrieval augmented generation (RAG) framework, RAM is a no-code solution that delivers fast, accurate and context-aware AI responses from unstructured content.
RAM ingests and processes unstructured documents and evaluates and selects the best configurations for rapid interaction with those documents via an API or chatbot. RAM also supports the plug-and-play use of GenAI services such as LLMs and vector databases. And it adds an agentic AI layer to automate complex workflows grounded with enterprise data.
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