Synthetic Data for Smarter AI

Generate years of training data in minutes.

THE PROBLEM

Computer vision has trailed other AI technologies because visual data limitations made it expensive, slow and ineffective to train.

1. Lack of Diversity

Not enough image variation and volume to fit an accurate model.

2. Limited Access

Sparse data due to privacy constraints, rarely seen events and hard-to-reach places restricts some of the most useful applications.

3. Long and Costly Labeling

Labor-intensive, error-prone hand-labeling process prohibits adoption and growth for most companies.

OUR SOLUTION

AI.Reverie generates an entirely new class of data at scale that makes AI training affordable, fast and productive.

10x Diversity

at a tenth of the cost to improve AI performance when there’s no room for error or expense

100% Accuracy

for all annotation types to accelerate AI projects and stay on budget

What We Do

Data Generation

Data Generation

We generate rare objects and events so you can deploy AI in any scenario.

Data Labeling

Data Labeling

We create perfect procedural annotation to accelerate projects and stay on budget.

Data Enhancement

Data Enhancement

We provide fast and iterative cycles of measurement and data improvement.

Our Advantage image asset

Our Advantage

We provide unique expertise honed with government R&D labs and Fortune 100 companies across a range of industries including smart cities, defense, and retail.

We’re convinced that [synthetic data] is going to be the future in terms of making things work well.

Stacey on IoT, June 2020

[AI.Reverie] offers a suite of synthetic data and vision APIs to help businesses across different industries train their machine learning algorithms and improve their AI accuracy and repeatability.

Forbes, April 2020

When you consider how expensive it is to collect these real datasets, being able to simulate these areas is going to be a big cost savings.

Jake Shermeyer, Research Scientist, CosmiQ Works

AI.Reverie goes further…its approach is particularly useful for exposing software to scenarios that might be hard to find in data gleaned from the real world.

Economist, October 2019