Data Synthesis
Pebblous creates and provides synthetic data for the training of customers' AI models. Using synthetic data for training AI is a powerful approach, especially in scenarios where real-world data is scarce, sensitive, or costly to obtain.
3 Types of Data Synthesis
Computer Graphics
This method involves creating visually realistic images or animations using advanced software and rendering techniques.
Generative AI
This approach uses deep learning models to automatically generate new data that mimics real-world data patterns.
Modeling & Simulation
Simulation involves creating virtual environments based on scenarios and governing equations to model and generate data that reflects real-world processes and behaviors.
Synthetic Data Portfolio
We create and provide various synthetic data based on the requests of our customers and potential clients.
Plastics Recycling
Synthetic data for training AI to detect and classify plastic waste
Meal Monitoring
Synthetic data for training AI to estimate calorie and leftover
Behavioral Biology
Synthetic data for training AI to detect body points of moving rats
Human Characters
Synthetic data for training AI to detect changes in body shape
Product Classification (Vehicles)
Synthetic data
Natural Exploration (Birds)
Synthetic data using generative AI
R&D
Experiments for Synthetic Data Generation
