To create synthetic data sets for complex systems, we need a comprehensive catalog of components that is significantly larger than the typical system design, potentially requiring hundreds of millions of parts.

To create effective AI models for engineering, we need to generate synthetic training data that is physics-based and informed by supply chain considerations for various physical product domains.

We're building a comprehensive data stack that includes tools like Azure Cosmos DB and Azure Databricks to enhance data accessibility and integration for AI applications.

The AI community is developing better tools that allow end users to easily customize AI systems without needing extensive technical knowledge.

We utilize various tools for package management, editing, deployment, and database management to support the AI agent's functionality.

We realized that the existing AI models were not sufficient for our needs, prompting us to develop our own models to enhance product quality.

We're developing tools for in-experience creation, allowing users to create content directly within the platform using AI.

We are getting our organization AI-ready by establishing the necessary infrastructure and data governance, while experimenting with productivity tools like chatbots.

On the product side, we focus on building a streamlined process for AI that resembles a Ford factory line, which enhances efficiency.