A human-in-the-loop approach to synthetic data generation for every ML team in the world.Get Started
Our technology works on a simple three-step loop: Generate, Feedback, Repeat
Use a seed dataset to guide the training of a generative model, which outputs new, synthetic samples to augment your original dataset
Distribute the new samples to a set crowdworkers, verify that the output appears real, and remove any unrealistic or unwanted data.
Use crowdworker evaluations to refine the data and models - continue this proccess until sufficient samples are realistic.
Veris provides a rapid and accurate way for autonomous vehicle manufacturers to produce, analyse, and verify vital sensory data.
Veris promises a future of high-quality data for all. We plan to accomplish this through three key components: "smart" synthetic data generation, human-in-the-loop crowdworker networks, and an iterative feedback process.
Our "smart" synthetic data complements your existing dataset to give you exactly what you need to train the best model you can.
As ML engineers, we know just how important high-quality data is. That's why every sample we produce is carefully analyzed and vetted by a real person.
Crowdworker feedback allows us to improve our generative models with each iteration, making our data generation of higher quality than incumbents.
Veris is powered by an experienced team of researchers and engineers.