Why Enterprises Need AI Query Engines to Fuel Agentic AI

Data is the fuel of AI applications, but the magnitude and scale of enterprise data often make it too expensive and time-consuming to use effectively.

According to IDC's Global DataSphere1, enterprises will generate 317 zettabytes of data annually by 2028 - including the creation of 29 zettabytes of unique data - of which 78% will be unstructured data and 44% of that will be audio and video. Because of the extremely high volume and various data types, most generative AI applications use a fraction of the total amount of data being stored and generated.

For enterprises to thrive in the AI era, they must find a way to make use of all of their data. This isn't possible using traditional computing and data processing techniques. Instead, enterprises need an AI query engine.

What Is an AI Query Engine? Simply, an AI query engine is a system that connects AI applications, or AI agents, to data. It's a critical component of agentic AI, as it serves as a bridge between an organization's knowledge base and AI-powered applications, enabling more accurate, context-aware responses.

AI agents form the basis of an AI query engine, where they can gather information and do work to assist human employees. An AI agent will gather information from many data sources, plan, reason and take action. AI agents can communicate with users, or they can work in the background, where human feedback and interaction will always be available.

In practice, an AI query engine is a sophisticated system that efficiently processes large amounts of data, extracts and stores knowledge, and performs semantic search on that knowledge, which can be quickly retrieved and used by AI.

An AI query engine processes, stores and retrieves data - connecting AI agents to insights. AI Query Engines Unlock Intelligence in Unstructured Data An enterprise's AI query engine will have access to knowledge stored in many different formats, but being able to extract intelligence from unstructured data is one of the most significant advancements it enables.

To generate insights, traditional query engines rely on structured queries and data sources, such as relational databases. Users must formulate precise queries using languages like SQL, and results are limited to predefined data formats.

In contrast, AI query engines can process structured, semi-structured and unstructured data. Common unstructured data formats are PDFs, log files, images and video, and are stored on object stores, file servers and parallel file systems. AI agents communicate with users and with each other using natural language. This enables them to interpret user intent, even when it's ambiguous, by accessing diverse data sources. These agents can deliver results in a conversational format, so that users can interpret results.

This capability makes it possible to derive more insights and intelligence from any type of data - not just data that fits neatly into rows and columns.

For example, companies like DataStax and NetApp are building AI data platforms that enable their customers to have an AI query engine for their next-generation applications.

Key Features of AI Query Engines AI query engines possess several crucial capabilities:

Diverse data handling: AI query engines can access and process various data types, including structured, semi-structured and unstructured data from multiple sources, including text, PDF, image, video and specialty data types.

Scalability: AI query engines can efficiently handle petabyte-scale data, making all enterprise knowledge available to AI applications quickly.

Accurate retrieval: AI query engines provide high-accuracy, high-performance embedding, vector search and reranking of knowledge from multiple sources.

Continuous learning: AI query engines can store and incorporate feedback from AI-powered applications, creating an AI data flywheel in which the feedback is used to refine models and increase the effectiveness of the applications over time.

Retrieval-augmented generation is a component of AI query engines. RAG uses the power of generative AI models to act as a natural language interface to data, allowing models to access and incorporate relevant information from large datasets during the response generation process.

Using RAG, any business or other organization can turn its technical information, policy manuals, videos and other data into useful knowledge bases. An AI query engine can then rely on these sources to support such areas as customer relations, employee training and developer productivity.

Additional information-retrieval techniques and ways to store knowledge are in research and development, so the capabilities of an AI query engine are expected to rapidly evolve.

The Impact of AI Query Engines Using AI query engines, enterprises can fully harness the power of AI agents to connect their workforces to vast amounts of enterprise knowledge, improve the accuracy and relevance of AI-generated responses, process and utilize previously untapped data sources, and create data-driven AI flywheels that continuously improve their AI applications.

Some examples include an AI virtual assistant that provides personalized, 24/7 customer service experiences, an AI agent for searching and summarizing video, an AI agent for analyzing software vulnerabilities or an AI research assistant.

Bridging the gap between raw data and AI-powered applications, AI query engines will grow to play a crucial role in helping organizations extract value from their data.

NVIDIA Blueprints can help enterprises get started connecting AI to their data. Learn more about NVIDIA Blueprints and try them in the NVIDIA API catalog.

IDC, Global DataSphere Forecast, 2024.

LINK:	https://blogs.nvidia.com/blog/ai-query-engines-agentic-ai/...
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