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Real-Time AI Recommendation Engine
Help your customers navigate today’s never-ending sea of choices and easily find exactly what they need with a product recommendation engine built with real-time AI.
Deliver Better Product Recommendations with Data Streaming & Vector Search
Harnessing the power that lies within AI to deliver better product recommendations is becoming a vital strategy for businesses that want to improve customer engagement levels and drive sales. When fueled by real-time data streams, AI has the ability to analyze vast amounts of user data, discern patterns including product similarities, and instantaneously deliver personalized suggestions when consumers are actively engaged.
During this demo, you’ll see an ecommerce company’s real-world product listing embeddings, produced by OpenAI, that capture the underlying meaning of unstructured data like text, audio, images, and videos in a format more easily leveraged by computational models. You’ll see how they’re streamed in real time by Confluent Cloud to Rockset’s vector search database, which returns recommendations in milliseconds.
Improve customer engagement levels with highly relevant, real-time product recommendations.
Increase conversion rates with a solution powered by predictive AI and vector search.
Save time and reduce costs at scale with fully managed cloud services Confluent and Rockset.
Build with Confluent
This use case leverages the following building blocks:
Reference Architecture
Confluent and Rockset power a critical architecture for efficiently developing and scaling AI applications built on real-time streaming data.
Generate product description and search query embeddings with the OpenAI API.
Fuel AI algorithms with high-value data streams using Confluent Cloud, a cloud-native data streaming platform built by the original creators of Apache Kafka®.
See product recommendations created by Rockset, a real-time search and analytics database capable of low-latency, high-concurrency queries on streaming data.