Synthesis AI specializes in synthetic data generation for computer vision, with a focus on human centric datasets where privacy, diversity, and label quality are hard to achieve with real world collection. Public coverage and company materials describe products for synthetic humans and synthetic scenarios that enable the simulation of complex multi human environments such as home, office, and outdoor spaces for use cases like access control and security systems. The core value proposition is controllability: teams can generate large volumes of labeled images and videos with variation in factors like lighting, pose, and scene composition, then use those labels to train and test models. This approach can reduce reliance on sensitive real person data and accelerate iteration when collecting and labeling is expensive or restricted. Because synthetic data must match the target domain, evaluation should include domain gap testing, bias measurement, and checks that model performance transfers to real camera feeds. Implementation is strongest when synthetic data is used as a complement: combine it with a smaller curated real dataset, then tune distributions and scenarios based on observed failure cases. Pricing is not typically published as a simple self serve plan and is best handled as quote based for enterprise needs. For teams building human sensing systems, Synthesis AI can be a practical path to faster dataset creation with more controllable coverage and clearer labeling.