Synthesis AI

Synthesis AI is a synthetic data company focused on computer vision. Rather than collecting and labeling real photographs, it programmatically generates photorealistic images of people and environments, each rendered together with perfect, machine-generated labels. This produces large, controllable, pixel-accurate datasets for training and validating perception models.

What they provide

The platform creates 3D synthetic humans, faces, and scenes with fine-grained control over attributes such as identity, expression, pose, lighting, and camera setup. Because the data is generated, every image arrives with exact annotations like segmentation masks, landmarks, and depth, eliminating manual labeling. The approach blends traditional computer graphics with generative AI, and the company has extended into scene generation and text-to-3D capabilities.

• Segment: Synthetic data for computer vision
• Modalities: Photorealistic images and 3D of humans, faces, and scenes
• Labels: Automatic, pixel-perfect annotations (segmentation, landmarks, depth)
• Uses: Face and identity, AR/VR, driver and in-cabin monitoring, biometrics

Where it fits in the AI data supply chain

Synthesis AI operates at the synthetic-data layer for the visual modality, an alternative to gathering and hand-labeling real imagery. Its value is strongest where real data is scarce, expensive, biased, or privacy-sensitive, such as diverse human faces or rare edge cases, and where guaranteed label accuracy matters. Buyers are computer-vision teams that need large, balanced, precisely annotated training sets to build and stress-test perception systems.