SAS RAM streamlines processing of unstructured data

"The no-code solution aims to bridge the gap between GenAI promises and practical business results. RAM offers concrete solutions for a wide range of industries. Fraud teams can detect suspicious patterns in seconds, speeding up investigations and strengthening compliance. In insurance, claims adjusters can immediately retrieve relevant information for faster payouts. Contact centers benefit from significantly shorter wait times while ensuring consistent responses. In the healthcare sector, RAM extracts insights from patient records and clinical protocols more efficiently, which can lead to improved treatments."

"Unstructured data presents challenges More than 80 percent of all business data consists of unstructured formats such as text and images. This information grows by 50 to 60 percent annually. Extracting value from this data stream is one of the biggest challenges for organizations seeking to utilize GenAI. Existing approaches are often complex, code-intensive, and inefficient. As a result, companies often struggle to achieve consistent results with their GenAI projects. SAS wants to bridge this gap with a new approach."

"No-code approach for complex processes RAM builds on the Retrieval Augmented Generation framework and automatically processes unstructured documents. The tool evaluates various configurations and selects the optimal settings for rapid interaction via APIs or chatbots. The solution supports the plug-and-play use of GenAI services, including Large Language Models and vector databases. For more complex workflows, RAM adds an agentic AI layer that enables automation."

SAS's Retrieval Agent Manager (RAM) is a no-code platform that converts unstructured text and images into actionable insights for multiple industries. RAM automates document processing using a Retrieval Augmented Generation framework, evaluates configurations, and selects optimal settings for rapid API or chatbot interactions. The platform supports plug-and-play GenAI components such as Large Language Models and vector databases, and adds an agentic AI layer for automating complex workflows. Use cases include fraud detection, faster insurance claims, shorter contact-center wait times, improved healthcare record insights, and predictive maintenance in manufacturing. More than 80 percent of business data is unstructured and grows rapidly, making such solutions essential. Reliability is emphasized.

Read at Techzine Global