Alternatives Watch: Driving competitive advantage: RAG models and agentic AI

With the alternative investment sector long relying on vast amounts of unstructured data that can be challenging and expensive to analyze, retrieval-augmented generation (RAG) models¹ and agentic AI² can help firms better manage and, more importantly, derive actionable business insights. Through these technologies, smaller firms can harness the intellectual property and computing power typically available only to larger tech companies, eliminating the need to invest significant time, money, and manpower into developing their own in-house models.

Defining RAG models and agentic AI

RAG models and agentic AI are transforming how firms can approach operations by enhancing productivity, increasing efficiency, and reducing costs across workstreams. RAG models enhance the accuracy and reliability of generative AI by fetching facts from external sources and linking AI services to detailed resources. Meanwhile, according to Nvidia, agentic AI uses advanced reasoning and iterative planning to autonomously tackle complex, multi-step problems by ingesting vast amounts of data from multiple sources. These systems can support investors by independently evaluating obstacles, developing effective strategies, and handling tasks like streamlining supply chains and identifying cybersecurity risks.

From client services and process automation to human resources, contract analysis, and code development, these technologies can also streamline back-, middle-, and front-office operational processes. For example, SEI enabled this digital transformation with SEIGPT, a proprietary framework that enables the rapid experimentation and implementation of generative AI applications. Currently used by employees, SEIGPT enhances client service by optimizing operational processes, streamlining tasks, improving communication, and driving productivity –driving operational efficiency so firms can focus on what matters most: serving their customers.

Gaining a competitive edge

RAG and agentic AI models can offer small and mid-sized alternative investment firms a competitive edge by enabling the rapid synthesis of complex information so firms can reap the potential benefits:

• Data analysis and insights generation: AI-powered RAG models can process and quickly analyze vast amounts of unstructured data. For example, a small alternative investment firm could use RAG models to scan and synthesize market reports, financial statements, or even social media sentiment to uncover trends and patterns. This analysis can help inform decisions on portfolio adjustments and risk assessments, as well as identifying emerging market opportunities.
• Operational efficiency and cost reduction: Back-office processes can be streamlined through RAG models and agentic AI. Automating routine compliance monitoring, data entry, and reconciliation tasks can deliver faster and more accurate reporting. Automation can also help reduce human error, cut down operational costs, and improve overall efficiency, allowing employees to focus on higher-value activities and enhance productivity.
• Risk management and scenario analysis: AI models can run complex simulations to predict the impact of various market scenarios on an investment portfolio. Alternative investment managers could stress-test a portfolio under different economic conditions, such as a potential market downturn or interest rate hikes. This ability enables the firm to develop proactive risk mitigation strategies and make adjustments in real time, ensuring they are prepared for unexpected market fluctuations to minimize potential losses.
• Predictive analytics for investment opportunities: By training AI models on proprietary data, firms can forecast trends, returns, and risks within specific asset classes or sectors. These models can help identify growing sectors, such as emerging technology, and allow firms to anticipate market movements. By capitalizing on high-potential investment opportunities before they are widely recognized, the predictive analysis can provide a significant advantage in a competitive market.

Integrating AI technologies

RAG models and agentic AI are revolutionizing how small and mid-sized alternative investment firms can operate. By enabling more efficient data analysis, streamlining operations, enhancing risk management, and providing predictive insights, these technologies empower firms to compete with larger, resource-rich competitors. As the investment landscape evolves, smaller firms can mistakenly think they aren’t ready to integrate these tools. Nevertheless, now is the time to evaluate how AI-driven tools can empower firms to make informed decisions and gain a competitive edge.

¹ Rick Merritt, “What Is Retrieval-Augmented Generation, aka RAG?,” Nvidia, Nov. 18, 2024.
² Erik Pounds, “What Is Agentic AI?,” Nvidia, Oct. 22, 2024.