The integration of artificial intelligence (AI) into modern society has accelerated in 2025, and today, reports estimate that nearly 40% of U.S. adults aged 18 to 64 have used AI in some form. One of the ways that AI has integrated into everyday lives is through intelligent applications. These applications are software that incorporates AI to enhance human workflows, improve decision-making, and automate tasks.
As a result, the intelligent application market size is growing at a rapid rate. In 2024, it was valued at $40.99 billion and is projected to reach $53.98 billion this year and eventually be worth $488.54 billion by 2033, growing at a CAGR of 31.70% during the forecast period. The proliferation of smartphones and mobile applications significantly drives this growth, which in turn has led to a demand for new intelligent applications to be built.
One key component that allows these intelligent applications to respond to queries in real time is the databases on which they have been built. One database that is increasingly being used to build modern intelligence applications is the vector database. This unique data management system is configured to store and search data in a way that can support intelligence applications.
How Data Powers Intelligence Applications
In today’s data-driven society, access to real-time data is no longer a luxury—it’s a necessity. In the business world, we highlighted how real-time analytics allows businesses to gain immediate insights from data as it is generated, enabling swift decision-making and agile responses to market changes. At the forefront of real-time analytics are intelligent applications that use the data to train algorithms so that the applications can continuously learn and improve. In order to be effective, the intelligence applications need to have access to data management systems that can store vast volumes of data, which can be constantly updated, and are able to search through the data in a way that provides the most accurate responses to queries. Vector databases are one such data management system and are being used to build modern intelligence applications.
Vector Databases
Vector databases are unique in how they store, sort, and allow applications to use data. Unlike traditional databases, a vector database retrieves data based on similarity, reducing both the complexity of the code and the time taken for data retrieval. This is achieved by converting the data through embedding models into strings of numbers known as vectors. The vectors are stored in a vector database where their natural indexing allows the database to easily perform a similarity search across vast volumes of datapoints.
These functions make vector databases vital enablers that can assist developers in transforming intricate AI blueprints into practical, value-driven tools. Intelligence applications are one such tool that is being built upon the capabilities of the vector database.
Why Intelligent Apps Are Built Using Vector Databases
There are many different types of intelligent applications, including virtual assistants, recommendation systems, and narrow AI applications. The most famous example is the chatbot, which uses natural language processing capabilities to analyze text to extract sentiment, identify key topics, and even summarize content. Vector databases are used to build intelligent applications for these three reasons:
Similarity Searches
Instead of keyword searches, vector databases perform a similarity search where they can be matched even if two pieces of data aren’t identical but are contextually or semantically similar. By having a vector database as the foundational knowledge center of an intelligent application, when a user inputs a query, the application can use the similarity search to understand the context of what is being asked and provide a much more accurate answer. A common use case for similarity searches in intelligent applications is providing recommendations. Ecommerce intelligence apps use product embeddings to personalize product recommendations, and “these embeddings are created based on the characteristics and relationships of the products and the order history of millions of other users”.
Continuous Training
Once an intelligent application is released, it needs to use an external source to stay up-to-date in order to provide an accurate service. Vector databases can be used in the application’s building process to ensure that users can access and process information beyond their pre-trained knowledge.
Because vector databases can hold vast amounts of data, they can be easily updated with new information that the intelligent application can access, such as current events, new products, or changes to the service. Incorporating a vector database into the building of intelligent applications will also help decrease the generation of factually incorrect answers.
Real Time Data Analytics
Vector databases are designed for real-time data analytics, which allows intelligent applications to easily adapt to changing data and instantly discover patterns. This is vital for intelligent applications that discover patterns, such as financial, image recognition, and healthcare applications. The natural way in which the vectors are stored in the database allows the application to compare vast amounts of data to provide accurate results or find anomalies. The ability to detect these patterns and provide users with actionable information allows intelligent applications to be agile, and is why those built with vector databases will be more competitive.
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