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.
AI.Reverie generates an entirely new class of data at scale that makes AI training affordable, fast and productive.
at a tenth of the cost to improve AI performance when there’s no room for error or expense
for all annotation types to accelerate AI projects and stay on budget
What We Do
[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.
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