Introduction
Enterprise search is getting an overhaul, moving beyond traditional keyword-based search to deliver more pertinent knowledge through conversational experiences.
With Large language models (LLMs) and Vector Databases, we can now understand the intent behind user queries, surface relevant information from diverse sources, and provide personalized responses.
Who Benefits
Front-office Employees: Generative AI empowers employees across all departments with faster and more accurate access to the information they need to perform their jobs effectively.
Researchers: These professionals often handle complex information needs. Generative AI assists them by quickly summarizing large volumes of data, uncovering hidden patterns, and accelerating research processes.
Business Analysts: Anyone who relies on accessing and processing information as part of their work benefits from generative AI's ability to personalize information delivery and provide contextually relevant answers.
Business Value
Improved Productivity: Generative AI drastically reduces the time employees spend searching for information, leading to significant productivity gains and allowing them to focus on higher-value tasks.
Enhanced Decision-Making: By providing faster access to relevant information and insights, generative AI enables better and faster data-driven decision-making across the enterprise.
Capabilities
Semantic Search: Vector Databases allows indexing of content based on the meaning of natural language documents, which combined with LLMs can generate tailored responses based on relevant results.
AI-Powered Chatbots and Virtual Assistants: These tools provide conversational interfaces with both natural languages queries and iterative exploration of knowledge.
Knowledge Base Integration: A lot of the magic happens when we integrate diverse knowledge bases with internal document repositories, allowing for hybrid search across structured and unstructured data.