In the world of artificial intelligence, data is undeniably the lifeblood that powers applications. However, for many businesses, the sheer volume and complexity of enterprise data can turn it into a costly and time-consuming obstacle to effective use.
As projected by IDC’s Global DataSphere, enterprises are on track to generate a staggering 317 zettabytes of data annually by 2028. This includes around 29 zettabytes of unique data, with a whopping 78% being unstructured data—much of which includes audio and video content. This immense and varied array of data types often leads most generative AI tools to capitalize on only a small fraction of the total data available.
To navigate the AI-driven landscape successfully, companies must leverage all their data, which cannot be achieved using conventional computation and processing methods. Enter the AI query engine—an essential tool for modern enterprises.
Understanding AI Query Engines
So, what exactly is an AI query engine? Simply put, it’s a system that connects AI applications, or agents, to the vast pools of data an organization holds. This connectivity is crucial for agentic AI, acting as the bridge that enables AI applications to deliver accurate, context-aware responses.
AI agents are the core of an AI query engine. They can source and process data from multiple channels, assisting human employees by gathering information, reasoning, and taking actions. These agents can engage directly with users or work silently in the background, always incorporating human feedback and interaction.
In practical terms, an AI query engine is a sophisticated apparatus designed to handle extensive data processing, knowledge extraction, and semantic searching—ultimately allowing AI to utilize information efficiently.
Unlocking Unstructured Data Intelligence
The magic of an AI query engine lies in its ability to access knowledge stored in various formats, particularly when it comes to unstructured data. Most traditional query engines depend on structured queries and databases, requiring users to specify their queries precisely using SQL and limiting results to standard data formats.
In contrast, AI query engines are equipped to handle structured, semi-structured, and unstructured data. Common forms of unstructured data include PDFs, log files, images, and videos, often stored in object stores or file servers. AI agents communicate using natural language, allowing them to interpret user intent—even when the request is vague—by exploring diverse data sources. Consequently, results can be delivered conversationally, making them easier to understand and utilize.
For instance, innovative companies like DataStax and NetApp are developing platforms that equip their customers with AI query engines capable of powering next-generation applications.
Essential Features of AI Query Engines
AI query engines bring several significant capabilities to the table:
- Diverse data handling: They can access and process a myriad of data types—structured, semi-structured, and unstructured—from various sources such as text, PDFs, images, videos, and more.
- Scalability: AI query engines manage petabyte-scale data efficiently, ensuring rapid access to enterprise knowledge for AI applications.
- Precise retrieval: They offer high-accuracy and high-performance embedding, vector search, and knowledge reranking across multiple sources.
- Continuous learning: These systems store feedback from AI applications, fostering an AI data flywheel that refines models and enhances application performance over time.
Moreover, retrieval-augmented generation (RAG) is a feature of AI query engines that harnesses generative AI models to provide a natural language interface to data, enriching the response generation process with relevant information from extensive datasets.
By employing RAG, businesses can transform their technical documents, policy manuals, videos, and other data into accessible knowledge bases. AI query engines can leverage these sources for tasks ranging from customer relations to employee training and developer productivity.
As research and development continue, we expect AI query engines’ capabilities to evolve rapidly, offering even more advanced information retrieval techniques and knowledge storage strategies.
The Transformative Power of AI Query Engines
By utilizing AI query engines, businesses can fully embrace the potential of AI agents to connect their teams to vast reservoirs of enterprise knowledge. This connection enhances the accuracy and relevance of AI-generated responses, taps previously unused data, and fosters the creation of data-driven AI systems that improve continuously.
Consider tangible examples like AI virtual assistants offering personalized 24/7 customer service, AI agents that can search and summarize video content, or those analyzing software vulnerabilities. These scenarios highlight just how essential AI query engines are for extracting meaningful value from raw data.
As we see these technologies advance, AI query engines will play an increasingly important role in bridging the chasm between business intelligence and AI-driven applications.
If you’re eager to learn more about how to connect AI to your data, NVIDIA Blueprints can help you jumpstart your journey. Check out their offerings in the NVIDIA API catalog.
The AI Buzz Hub team is excited to see where these breakthroughs take us. Want to stay in the loop on all things AI? Subscribe to our newsletter or share this article with your fellow enthusiasts.
