We are building EVA, an AI-Relational database system with first-class support for deep learning models. Our goal with EVA is to create a platform that supports AI-powered multi-modal database applications operating on structured (tables, feature vectors, etc.) and unstructured data (videos, podcasts, pdf, etc.) with deep learning models. EVA comes with a wide range of models for analyzing unstructured data, including models for object detection, OCR, text summarization, audio speech recognition, and more.

The key feature of EVA is its AI-centric query optimizer. This optimizer is designed to speed up AI-powered applications using a collection of optimizations inspired by relational database systems. Two of the most important optimizations are:

+ Caching: EVA automatically reuses previous query results (e.g., inference results), eliminating redundant computation and saving you money on inference.

+ Predicate Reordering: EVA optimizes the order in which query predicates are evaluated (e.g., running faster, more selective deep learning models first), leading to faster queries.

Besides saving money spent on inference, EVA also makes it easier to write SQL queries to set up multi-modal AI pipelines. With EVA, you can quickly integrate your AI models into the database system and seamlessly query structured and unstructured data.

We are constantly working on improving EVA and would love to hear your feedback!