In a recent talk with AI User Group, our CEO Daniel delved into the challenges of integrating structured data into GenAI applications.

He highlighted that while GenAI models excel with unstructured data like text and images, they often struggle with structured data—think numbers, timestamps, and categorical information. This limitation can lead to subpar results in applications such as financial chatbots or e-commerce recommendation systems, where understanding structured data is crucial.

‍

Daniel introduced Superlinked's vector compute framework as a solution to this problem. By creating custom vector embeddings that seamlessly combine structured and unstructured data, Superlinked enables enterprises to enhance their GenAI models without the need for extensive custom model training. This approach accelerates time-to-market and improves the explainability of AI-driven results.

For those looking to bridge the gap between structured and unstructured data in their AI applications, Daniel's insights offer a practical path forward. You can watch his full talk below. If you want a technical deep-dive, this VectorHub article on e-commerce recommender systems will be useful!

If you’re interested in the strategies I’ve outlined or want to discuss your own e-commerce hurdles, let’s connect —we’d be thrilled to help you craft smarter, more scalable vector-powered solutions!

‍