The Challenge
Enterprise search is getting an overdue overhaul with AI, moving beyond traditional keyword-based search to deliver more pertinent knowledge through conversational experiences.
But generic search solutions aren't well-suited for specific processes, where exact information is required at the right time in the right place. In other words, we need to build smarter search into tailored solutions to enable our staff to work smarter.
The Solution
Knowledge Hubs: A lot of the magic happens when we integrate diverse knowledge sources into curated knowledge repositories, in support of specific business processes, as exemplified with Cube5 RFP.
Images, Tables and Charts: Finding text snippets is nice, finding the right visual is nicer. Enabling "similar to this" search on non-text content can make a 10x improvement in productivity and quality.
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.
Technical Capabilities
Document Parsing: Ingesting documents, and decomposing them into their core elements - titles, sections, paragraphs, tables, chart and images - is the baseline for building Knowledge Hubs.
Embedding Models: Modern LLMs come in variants that can embed both text and images into multi-dimensional vectors, with similar content represented as similar vectors, laying the foundation for indexing in vector databases.
Semantic Search in Vector Databases: Finding content based on the semantic meaning is a game changer for enterprise data, allowing precise knowledge to be delivered in process-specific ways.