Digital Event Horizon
Unlocking Enterprise Data: The Power of AI Query Engines
Enterprises generate vast amounts of data (317 zettabytes annually) by 2028, with 78% being unstructured. AI query engines are needed to efficiently process and extract knowledge from large datasets. The engines connect AI applications to data, enabling informed decision-making and intelligent actions. Key capabilities of AI query engines include data processing, knowledge extraction, embedding, vector search, and continuous learning. Examples of AI query engine applications include customer service assistants and software vulnerability analysis agents.
In an era where data is the fuel of AI applications, enterprises are struggling to make use of their vast amounts of data due to its sheer volume and complexity. According to IDC's Global DataSphere forecast, by 2028, enterprises will generate 317 zettabytes of data annually, with 78% of that being unstructured data, including audio and video formats.
To harness the full potential of this data, enterprises need an AI query engine – a sophisticated system that efficiently processes large amounts of data, extracts and stores knowledge, and performs semantic search on that knowledge, enabling more accurate, context-aware responses. An AI query engine serves as a bridge between an organization's knowledge base and AI-powered applications, making it a critical component of agentic AI.
The core function of an AI query engine is to connect AI applications – or AI agents – to data, allowing them to gather information, plan, reason, and take action. These AI agents can communicate with users or work in the background, where human feedback and interaction will always be available. By leveraging this technology, enterprises can unlock intelligence in unstructured data, derive more insights, and create data-driven AI flywheels that continuously improve their AI applications.
AI query engines possess several crucial capabilities that make them an essential tool for any enterprise looking to harness the power of AI. These include:
* Efficiently processing large volumes of structured, semi-structured, and unstructured data
* Extracting knowledge from multiple sources, including text, PDFs, images, video, and specialty data types
* Providing high-accuracy, high-performance embedding, vector search, and reranking of knowledge from multiple sources
* Continuous learning capabilities that allow the AI query engine to store and incorporate feedback from AI-powered applications
To illustrate the potential of AI query engines, consider examples such as an AI virtual assistant providing personalized, 24/7 customer service experiences or an AI agent for analyzing software vulnerabilities. These applications demonstrate how an AI query engine can bridge the gap between raw data and AI-powered applications, enabling enterprises to extract value from their data.
Several companies are already leveraging AI query engines in innovative ways, such as DataStax and NetApp building AI data platforms that enable customers to harness the power of next-generation applications. By embracing this technology, enterprises can unlock new levels of productivity, efficiency, and innovation.
In conclusion, AI query engines represent a critical step forward for enterprises seeking to harness the full potential of their data in an era where AI applications are increasingly dependent on high-quality inputs. As research and development continue to evolve these capabilities, it is clear that AI query engines will play a vital role in helping organizations extract value from their data.
Related Information:
https://blogs.nvidia.com/blog/ai-query-engines-agentic-ai/
Published: Tue Jan 7 13:49:35 2025 by llama3.2 3B Q4_K_M
