Eventual

Building the AI data engine for any modality and scale

Y CombinatorWinter 2022ActiveB2BInfrastructureComputer Vision

What they do

Problem

AI teams struggle to efficiently process and query massive multimodal datasets because existing data platforms are built for tabular analytics rather than images, video, audio, and other complex data at scale.

Solution

Eventual builds Daft, an open-source AI data engine that enables intuitive querying and production-scale processing of multimodal data while coordinating external APIs and GPU clusters.

Who it's for

AI/ML developers and data teams at companies building foundation models and other large-scale AI applications.

Founders

Sammy Sidhu

Founder

Sammy Sidhu is co-founder and CEO of Eventual. Sammy's background is in High Performance Computing (HPC) and Deep Learning and has over a dozen patents/publications in the space. In the past, he has worked on high frequency trading on wall street, medical AI research at Berkeley and self-driving cars at both DeepScale (acquired by Tesla) and Lyft Level 5 (acquired by Toyota). Native to the Bay Area, Sammy graduated from UC Berkeley with a degree in Electrical Engineering and Computer Science.

LinkedIn · X

Jay Chia

Founder

Jay is based in San Francisco and graduated from Cornell University where he did research in deep learning and computational biology. He has worked in ML Infrastructure across biotech (Freenome) and autonomous driving (Lyft L5), building large-scale data and computing platforms for diverse industries. Jay is originally from Singapore and spent 2 years as a tank commander in the military and then as the head of recruiting at a unicorn Singapore startup.