Generative AI is a family of models that learn the statistical structure of data (text, images, audio, code, etc.) and then sample from that learned distribution to create new content. These models are typically built with deep neural architectures such as transformers, diffusion models, and GANs, and can be conditioned on prompts, examples, or structured inputs. In applications, generative models are often combined with retrieval systems, tools, and business logic to ground outputs in real data and workflows. Effective use requires careful attention to safety, reliability, governance, and alignment with domain constraints.

Thin integration layer around a managed AI API, where most intelligence lives in an external provider and the application focuses on prompts, inputs, routing, and post-processing.

Managed AutoML platforms package feature engineering, model selection, training, deployment, and monitoring into a guided workflow so teams can ship predictive models quickly without owning a full bespoke ML stack.