More than 80% of enterprise data exists in unstructured formats such as text, images, and documents and that volume is expanding by up to 60% each year. Turning this information into useful business insight remains one of the biggest challenges and opportunities for generative AI [GenAI]. SAS has introduced Retrieval Agent Manager [RAM]. SAS RAM is a new solution designed to help organisations harness the potential of GenAI without the complexity and coding burden that often comes with current approaches.
RAM streamlines the process of converting raw, unstructured enterprise data into relevant, timely answers that support better decision-making. It enables companies to integrate AI into existing systems whether through chatbots or automated agents in a scalable and trustworthy way.
“SAS Retrieval Agent Manager transforms fragmented, unstructured information into actionable enterprise knowledge.” 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 modernise organisational 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, context-aware AI responses from enterprise content.
It ingests and processes documents, selects optimal configurations for quick information retrieval. Then connects with external GenAI services such as large language models [LLMs] and vector databases. An agentic AI layer automates complex workflows while ensuring data transparency and traceability.
Broad Industry Applications
RAM is for a wide range of sectors:
Banking and Financial Services: Supports anti-fraud and risk teams by surfacing relevant documents and insights to speed up investigations and regulatory compliance.
Insurance: Enables adjusters to access claim histories, policy wording, and case notes instantly, reducing turnaround times.
Public Sector: Helps call-centre agents retrieve consistent, policy-based responses from extensive archives and case records.
Healthcare: Assists clinicians by synthesising patient information, clinical guidelines, and research to provide faster, accurate insights.
Manufacturing: Enhances predictive maintenance by retrieving historical maintenance data, manuals, and reports to identify causes and corrective actions efficiently.
Trust and Transparency
RAM operates without using enterprise data to train or fine-tune LLMs, maintaining separation between the model and corporate data. Its design ensures that responses and recommendations are grounded in an organisation’s own verified documents, with clear attribution to data sources.
“RAM makes it easier for companies to apply chatbots and conversational AI to their knowledge bases,” said Jason Mann, VP of IoT at SAS. “It allows integration of GenAI-powered knowledge services into existing applications and supports the development of AI agents at scale.”
As organisations look to turn the hype around AI into tangible business value, SAS Retrieval Agent Manager offers a pragmatic route helping them navigate their vast stores of unstructured data and turn it into insight that drives results.
