Since the advent of GenAI, your research does not have to be static. As you analyze the risk for the next quarter, you can tweak your queries based on the responses and drill down into specific topics, enabling a more accurate projection.

Under the hood, obtaining a unified picture with all the relevant data including publicly available information and the company’s data, is a major technological challenge. While an AI assistant uses information from an LLM trained primarily on unstructured data, most organizations hold their data in a structured format, be it databases, XMLs, spreadsheets and more. Without access to the organization’s specific data and knowledge base, LLMs cannot provide accurate and relevant answers to questions that rely on this information. This reduces the value that organizations can derive from GenAI.

Ensuring effective privacy and governance 

Another equally important concern is that of protecting enterprise data, and ensuring effective privacy and governance during interactions with enterprise databases. While most AI security approaches focus on static evaluations of LLMs, interactions with structured dynamic data like organizational databases are that much more complex. These dynamic interactions require continuous monitoring to ensure that natural language inputs and GenAI generated outputs comply with regulations and do not introduce bias, errors, or unintended consequences. 

To solve this issue, GigaSpaces eRAG allows users – even non-technical users – to chat in natural language with relational databases. Integrating structured enterprise data, eRAG is built to complement LLMs and deliver hallucination-free responses, enabling teams to use all relevant business information in their decision-making processes. 

To enable early detection of governance issues, eRAG provides real-time evaluation of inputs and outputs, which enables early detection of governance issues and reduces risk before the communication reaches the LLM. Once the LLM interactions begin, AI risk detectors continuously monitor the interactions and assess if they are compliant with regulations or organizational policies. eRAG’s customizable AI risk agents enable organizations to tailor risk detection to specific corporate policies or scenarios. 

GigaSpaces eRAG integrates with IBM watsonx.governance and Amazon SageMaker to deliver real-time risk assessment and mitigation. It sends a risk score to IBM watsonx.governance for comprehensive AI governance insights, and collects the metrics, feeding them into Amazon SageMaker for analysis and visualization. eRAG’s extensible AI risk agents let organizations customize risk-detectors to assess specific corporate policies or other risk scenarios.

Use Case: Enhancing customer service in eCommerce

Delivering exceptional customer service is crucial to maintain customer satisfaction and loyalty in eCommerce. An eCommerce company can leverage GenAI to optimize customer service inquiries, personalize customer interactions, and improve overall efficiency. 

Personalizing Responses

Customized responses require access to each customer’s data, which is found in a number of internal repositories. The Customer Relationship Management (CRM) system holds data about customer interactions, purchase history, preferences, and feedback.  The company’s Order Management System (OMS) holds customer orders, including order ID, product details, shipping status, and customer information. This data is organized in relational databases, making it easily accessible for query and analysis. 

When customers inquire about their order status or how to handle returns, the company can leverage the data found in the CRM, OMS, inventory and other systems. The LLMs can then use this data to provide quick, accurate, and personalized responses via a chatbot or other technology, while continuously optimizing the accuracy of its responses, based on updated information. For example, when a customer asks about the status of their order, the LLM retrieves the specific order details from the OMS and provides a precise update. If a shopper asks about specific product details, the LLM can query the Product Information Database which contains structured information about products, such as descriptions, specifications, prices, and inventory levels to be able to address shopping inquiries effectively.

You may be considering an LLM-powered chatbot for your website and mobile app. Once it has been trained on vast amounts of customer service interactions and product-related queries, it can easily understand and respond to a wide range of questions with high accuracy. 

A few factors can improve the LLM’s effectiveness, such as incorporating Natural Language Understanding (NLU)  techniques to comprehend the intent behind customer inquiries, even if they are phrased differently or contain slang. This ensures that the chatbot can handle diverse queries and provide relevant answers. Also adding Multilingual Support allows your company to cater to a global customer base, and improve the customer experience for non-English speaking customers.

Benefits of a comprehensive eRAG solution

The benefits of the eRAG solution include: 

• • Enhanced Customer Satisfaction: with quick, accurate, and personalized responses 
  • Improved Efficiency: The LLM-powered chatbot handles a significant portion of customer inquiries, reducing the workload on human agents and allowing them to focus on more complex issues
  • Data-Driven Insights: Analyzing interactions between the LLM and customers provides valuable insights into common issues and customer preferences; these insights can enhance business decisions
  • Assured privacy and governance: the integration with IBM watsonx.governance and Amazon Sagemaker ensure that queries on your organization’s data are protected and compliant 
  • Scalability: The LLM can handle an increasing volume of inquiries as your company grows, while ensuring consistent and high-quality customer service

Last words

GigaSpaces eRAG capabilities and the integration with watsonx.governance and Amazon Sagemaker continuously evaluate all dynamic human interactions with structured data for safety and compliance. Organizations can realize the full potential of GenAI for enterprise use cases with the knowledge they are not violating privacy or security regulations and policies.