SAS Introduces RAM: A No-Code AI Tool for Quick Insights from Unstructured Data
SAS has introduced a new AI tool, the Retrieval Agent Manager (RAM), designed to simplify the process of extracting insights from vast amounts of unstructured data. This innovation aims to address the challenge of transforming raw data into quick and relevant answers for better business decisions.
RAM, built on the retrieval augmented generation (RAG) framework, is a no-code solution that can scale to very large data volumes. It supports the seamless integration of GenAI services like large language models (LLMs) and vector databases, adding an agentic AI layer to automate complex workflows. Unlike traditional GenAI approaches, RAM does not use enterprise data to train or fine-tune LLMs, ensuring data privacy and trustworthiness.
More than 80% of enterprise data is in unstructured formats, growing at a rate of 50% to 60% annually. RAM helps organizations tap into this wealth of data, unlocking faster decisions, stronger relationships, and better outcomes across various industries. In manufacturing, for instance, RAM enhances predictive maintenance by retrieving relevant repair and maintenance information and generating clear work orders.
SAS Retrieval Agent Manager (RAM) is a significant step forward in leveraging unstructured data for better business decisions. By streamlining the process of transforming raw data into quick and relevant insights, RAM addresses a critical challenge in the Generative AI (GenAI) landscape. This new offering from SAS is part of their advanced AI-driven initiatives, promising to deliver fast, accurate, and context-aware AI responses from unstructured content.
Read also:
- Saudi Arabia's TASI Surges Past 11,000, Leading Gulf Equities Rally
- TikTok's deal negotiations continue following a conversation between Xi and Trump
- Trump announces agreement with Chinese authorities on TikTok deal
- Quantum Computing Market in the Automotive Sector Forecast to Expand to $6,462.13 Million by 2034
