Our Work

Our platform supports virtually endless iteration and perfect annotation that drive better AI object detection, activity recognition and more.


Self-Serve Synthetic: A Technical Workshop on Augmenting Training Datasets

Creating training data sets for visual AI has traditionally been an exhausting and expensive task involving human annotation
and low-grade images. The end result is usually frustration, cost and delay together with an unpredictable neural net. In this
session, learn how AI.Reverie solves that problem by creating perfectly marked up images in the tens of thousand at a vastly reduced cost and in a fraction of the time.

Featured Case Study

RarePlanes: Synthetic Data Takes Flight

CosmiQ Works and AI.Reverie release RarePlanes, now the largest
openly available, very-high resolution dataset that can test the value
of synthetic data from an overhead perspective.

Sample Environments

We use gaming techniques and machine learning to develop virtual
worlds in a collaborative, iterative, scalable process.

Simulated Urban Environment
Simulated Construction Site
Simulated Interiors
Simulated Machinery
Simulated Wildfires
AI.Reverie Overview