By Aveekshith Bushan
The intersection of AI and consumer services is rapidly evolving, and multi-model AI applications are at the forefront of this transformation. Multi-model AI is a breakthrough in artificial intelligence due to its ability to integrate multiple data types, making human-computer interactions more natural and intuitive. According to industry reports, the global multi-model AI market, valued at approximately $893.5 million in 2023, is expected to grow at a compound annual growth rate (CAGR) of 36.2%, reaching $1050 billion by 2031. Businesses can leverage multi-model AI by breaking the text barrier and incorporating various forms of data by combining key-value, vector, and graph searches to offer more personalised and efficient consumer experiences.
Consider common household examples like Siri, Alexa, and Google Assistant. They rely on multi-model AI systems to evaluate user interactions in the form of speech and text for generating exact responses and learning behavioural patterns for subsequent interactions. These AI applications mark a new era of digital personal interactions.
Enhancing Personalization with Key-Value Stores
Key-value databases play a crucial role in enabling personalisation at scale, allowing businesses to cater to millions of users with real-time, tailored content and recommendations. With contextual intelligence, these stores help applications quickly retrieve user-specific information and contribute to improved decision-making by processing vast amounts of data efficiently. For example, e-commerce platforms use them to store user preferences and browsing history and to suggest related products for highly personalised interaction.
Optimizing Search with Vector Databases
Vector search allows businesses to handle complex, unstructured data such as images, audio, or videos. Vector databases are also invaluable in areas like voice and image recognition, which are becoming prevalent in consumer applications.
Vector searches work by embedding data into numerical formats that allow AI to identify similarities between different pieces of content. This helps with improving the accuracy of recommendations and with handling diverse data types for a richer consumer experience. For instance, if a user watches a specific type of movie, a vector database can suggest other movies with similar themes or visual styles based on how closely the data matches, going beyond traditional keyword-based searches to identify deeper connections between data points.
Graph Databases for Complex Relationships
While key-value and vector databases handle personalisation and unstructured data, graph databases excel at mapping complex relationships between data points. Businesses can use graphs to understand how different entities are interconnected, such as users, products, or behaviours. This ensures better user engagement through relationship mapping and enhanced network-based recommendations
Social media platforms rely on graph searches to uncover connections between users, interests, and interactions, allowing for more relevant and meaningful recommendations. For example, graphs help with analysing user connections, identifying influencers, and recommending friends or content based on mutual interests.
The Power of Multi-Model AI
The true power of multi-model AI lies in its ability to integrate these different data models into a single, unified system. By combining key-value, vector, and graph, businesses can create more sophisticated and intuitive consumer interactions. An e-commerce platform, for example, may use key-value stores to retrieve customer data instantly, vector searches to recommend visually similar products, and graph databases to suggest items based on previous purchases.
In combination, these models provide highly personalised experiences tailored to each user’s specific needs and behaviour, increasing customer satisfaction and loyalty. This approach paves the way for the next generation of consumer innovation, where AI seamlessly enhances personalisation, search accuracy, and relevance across platforms.
The author is vice president and GM, APJ, Aerospike. The opinions expressed are personal and are not necessarily those of financialexpress.com.