In this episode at the AI Quality Conference, we caught up with Daniel, co-founder of Superlinked, who shared insights into their product's unique capabilities in information retrieval and feature engineering. Superlinked is a computing framework designed to transform complex data into vector embeddings.
Key Discussion Points:
*Multi-modal Vectors - integrating text, images, and structured metadata into multi-modal vectors that comprehensively describe entities in their complex context.
*Multi-objective Queries - navigating the trade-offs among multiple competing goals such as relevance, freshness, and popularity.
*Infrastructure as Code - managing the compute layer between data infrastructure and vector database using a simple Python SDK.
*Multiple Use Cases, One System - solving challenges in information retrieval and feature engineering with vectors.
AI professionals can utilize Superlinked to develop advanced search and personalization systems, including:
- Search with Semantic Relevance and Document Freshness, making search results more accurate.
- Personalized Recommendations based on user vectors constructed from SKU interactions, enabling real-time personalized e-commerce product feeds.
- Behavioral Cluster Analytics of customers using a vector index in your data warehouse.
