Winning with Enterprise Federated Search and Retrieval Augmented Generation - Swirl

How to Use AI Safely in Your Business

Modern enterprises deal with an overwhelming amount of data. Information is fragmented across countless systems, from cloud-based applications and data stores to legacy on-premise databases and content repositories. It’s a needle-in-a-haystack scenario—employees waste countless hours hunting down the right files, answers, and insights. Used safely, AI can accelerate business efficiency and productivity.

How to Use Retrieval Augmented Generation Safely in Business

Retrieval Augmented Generation (RAG) is a powerful technique that combines the benefits of large language models (LLMs) and federated search to provide personalized and real-time generative AI solutions. RAG can handle complex queries and generate new content, insights, or actions from multiple information sources, such as databases, documents, websites, and APIs.

However, using RAG in the enterprise also poses some challenges and risks, such as:

♦ Data security: How to access and process sensitive and confidential data without compromising its integrity or confidentiality.

♦ Data verifiability: How to provide evidence and explanations for the generated results and ensure their accuracy and relevance.

♦ Data personalization: How to tailor the generated results to the user’s preferences, context, and goals.

To address these challenges and risks, enterprises are seeking solutions like Swirl that have the following characteristics:

♦ Secure query processing: The solution should move the query to the data source rather than the other way around and use encryption and authentication to protect the data transmission and processing.

♦ Provenance and attribution: The solution should provide the source and origin of the data used for generation, as well as the rationale and logic behind the generation process.

♦ Advanced Search Capabilities: To support the user, the solution should use advanced NLP and search techniques to understand the user’s query, evaluate the data source, and generate the desired output.