AI Training and Data Enhancement Services

AI.Reverie partners with businesses across industries to support their unique training data and data enhancement needs.
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We develop robust cycles to improve synthetic datasets by verifying in real time the impact of synthetic data on performance and machine learning applications.

The goal?

To lower the cost of training all while improving the quality, diversity, and accuracy of metadata.


Baseline: AI Training with Real Data
Split the real world labeled dataset by {train/val/test %} to determine the performance of the real world baseline.

Cycle 1: AI Training with Synthetic Data Only
Train and validate on our synthetic data and test on the same held out dataset used in baseline experiment.

Cycle 2: AI Training with Domain Adapted
Perform a domain adaptation step to enhance our synthetic data with real unlabeled images and train to get better results.

Cycle 3: AI Training with Transfer Learning
Apply transfer learning by fine tuning with a small subset of the labeled data to achieve the best performance.

Our Framework


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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 the business problem of data enrichment by creating perfectly marked up images in the tens of thousand at a vastly reduced cost and in a fraction of the time.


What is Data Enhancement?

Data Enhancement solutions generate automated annotations for AI training via robust simulations in computer-modeled 3D environments that train AI in object detection, activity recognition, and generate an endless supply of annotations for AI models and algorithms.

What’s more, data enhancement allows for a literally limitless array of unique scenarios with minimal effort. This all results in massive, measurable improvements in the diversity of datasets. Our tools are scalable, fast, and iterative.

This all adds up to lower the cost and time demands of data collection while improving data quality and performance.

How Much Data Does it Take to Train AI?

Training computer-vision AI to be accurate and nuanced requires diverse, complex annotations — a lot of them.

This isn’t just challenging, but it’s also expensive, and laborious. It’s also expensive, both in terms of real-world capital as well as computing cycles. It’s also slow, tough to benchmark, and of questionable accuracy.

And, perhaps most importantly, it’s only as good as the simulations, data, and scenarios it’s given.

What are AI and Machine Learning Attributes?

An attribute or feature in machine learning refers to an aspect of an instance such as temperature, humidity and individual object characteristics, ex. the wing shape of an airplane.

How is Deep Learning Training Data Priced?

Deep learning training data can be expensive when acquired in the real world, particularly when data must be collected from hard-to-reach places and when data is required for rare events. Synthetic data is a dramatically more cost-effective and efficient input for machine learning models that rely on large volumes of diverse data, because simulation costs become a factor of GPU rather than man power.