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Transforming Unstructured Data with SAS Retrieval Agent Manager

In a world where more than 80% of enterprise data is unstructured—comprised of text, images, and various other formats—the challenge of harnessing this data is growing exponentially. According to recent forecasts, this vast volume of unstructured data is increasing by 50% to 60% annually, presenting both challenges and opportunities for businesses aiming to leverage generative AI (GenAI).

SAS Institute, a leader in analytics and artificial intelligence, has introduced the SAS Retrieval Agent Manager (RAM), designed to enhance business productivity by efficiently converting raw, unstructured data into relevant and actionable insights. This breakthrough solution is set to transform how organizations interact with their data and inform their decision-making processes.

Streamlining the Data Transformation Process

Traditionally, approaches to managing unstructured data have been cumbersome, relying heavily on code and complex processes that are both inefficient and often result in inconsistent outputs. Recognizing this gap, SAS developed RAM to offer a no-code solution built on the retrieval augmented generation (RAG) framework. RAM simplifies the process by delivering fast, precise, and context-aware AI responses extracted from unstructured information.

SAS RAM works by ingesting various documents, ranging from policy manuals to operational reports, evaluating them, and selecting the best configurations for rapid interaction through APIs or integrated chatbots. This allows businesses to quickly derive meaning from their data without an exhaustive overhaul of existing systems.

Cross-Industry Applications of RAM

The deployment of RAM's capabilities spans multiple industries, unlocking new efficiencies and enhancing performance across the board. Here are some notable use cases:

1. Banking and Fraud Management: RAM empowers financial institutions to detect suspicious behavior and regulatory compliance issues instantly. By rapidly identifying red flags, fraud teams can proactively address potential risks, while risk management officers can access vital data needed for audits and capital planning with unprecedented speed.

2. Insurance Claims: Adjusters can leverage RAM to quickly retrieve policy terms and previous claim data, accelerating claims processing and ensuring compliance with regulations, ultimately leading to heightened customer satisfaction.

3. Public Sector Services: The solution aids public sector contact centers in accessing information from various sources—like 311 tickets and policy manuals—ensuring accurate, consistent, and prompt responses to citizens, thus minimizing wait times.

4. Healthcare Support: Clinicians can utilize RAM to synthesize relevant patient insights with safety under HIPAA regulations, streamlining the delivery of better healthcare outcomes.

Enhancing Predictive Maintenance in Manufacturing

In the manufacturing sector, where predictive maintenance is vital, RAM can be particularly transformative. It allows firms to analyze massive amounts of unstructured data—like legacy manuals and maintenance reports—enabling technicians to understand the root causes of equipment issues quickly. By merging real-time data with the organization's historical knowledge, RAM generates clear, actionable work orders for engineers, greatly improving maintenance efficiency.

A Trustworthy AI Solution

One of the standout features of the SAS Retrieval Agent Manager is its emphasis on trustworthiness. By utilizing an agentic AI layer, RAM ensures that it relies on the enterprise's own data and documents to generate meaningful responses. Importantly, RAM keeps the enterprise data separate from any underlying models during the operation, preserving the integrity of sensitive information while still generating relevant knowledge.

Jason Mann, VP of IoT at SAS, notes that RAM's architecture is capable of scaling to support large volumes of continually updated data, allowing businesses to seamlessly integrate advanced technologies like chatbots with their existing knowledge bases.

As organizations navigate the complexities of AI adoption, RAM serves as a critical tool in aligning generative AI technologies with existing corporate systems, thus ensuring that enterprises can reliably harness their data to derive substantial value.

Conclusion

In an era defined by the increasing significance of AI, the SAS Retrieval Agent Manager stands out as a powerful solution that facilitates the transformation of unstructured data into actionable insights. It offers businesses the ability to make informed decisions rapidly, strengthen customer relationships, and achieve better outcomes through enhanced knowledge utilization. With SAS leading the charge, the future of data-driven decision-making looks promising.