synthetic data for machine learning.
Labelled training data and simulations generated in Unity, for computer vision and machine learning models that need more, safer or rarer examples than the real world provides.
what we build.
Generate massive volumes of perfectly labeled synthetic training data
Test autonomous systems in safe virtual environments before real-world deployment
Simulate rare and dangerous scenarios that are impossible to capture in the field
Reduce data collection costs compared to manual labeling
Domain randomisation for robust, generalisable models
one team, start to finish.
Train Smarter with Synthetic Data
Real-world data is expensive to collect, difficult to label, and often lacks the diversity needed to train robust AI models. Unity-based synthetic environments solve these problems by generating unlimited volumes of perfectly annotated training data in controlled, repeatable conditions. From autonomous vehicle perception to robotic manipulation, our simulation environments accelerate AI development while reducing cost and risk.
Our Approach to AI Training Simulations
We work closely with your data science and engineering teams to understand the specific requirements of your ML pipeline. Our process covers:
Environment Design creates photorealistic or domain-appropriate virtual worlds that accurately represent the conditions your AI system will encounter. We model physical properties, lighting conditions, weather variations, and object behaviours to ensure the synthetic data transfers effectively to real-world performance.
Data Generation Pipelines use Unity Perception and custom tooling to automatically generate training images, point clouds, bounding boxes, segmentation masks, depth maps, and other annotation types at scale. Domain randomisation techniques vary textures, lighting, camera angles, and object placement to improve model generalisation.
Reinforcement Learning Environments built with ML-Agents provide safe, parallelizable training grounds for autonomous agents. We design reward functions, observation spaces, and curriculum learning schedules that accelerate convergence.
Validation and Benchmarking establishes performance baselines and provides controlled test scenarios for evaluating model behaviour before real-world deployment.
Why Choose Unity Developers for AI Training Simulations
Our team combines deep Unity expertise with an understanding of machine learning workflows. We build simulation environments that integrate cleanly with your existing ML infrastructure, whether you are training with PyTorch, TensorFlow, or custom frameworks. Unity’s real-time rendering capabilities and scriptable pipeline make it the ideal platform for generating high-fidelity synthetic data at the speed and scale modern AI demands.
recent projects.
all case studies →
Tools we use
- Unity Engine
- Unity Perception
- ML-Agents
- Python Integration
- HDRP
- Unity Simulation
common questions.
How much does a project cost?
It depends on scope, content and platforms. After a short discovery call Greg gives you an indicative estimate, and a firm quote once the scope is agreed.
Tell us about your project. We’ll take it from there.
