webAI’s AI platform now available for Macs, iPads, iPhones

webAI, which specializes “in democratizing AI access through a distributed model,” says its AI platform is available for Apple products including Mac, iPad, and iPhone.

Co-fonder and CEO David Stout says webAI is creating a first-of-its-kind distributed AI network across local devices offering the ability to interact with large AI models without data being sent to the cloud for AI processing. These local models empower businesses, providing them access to cutting-edge AI capabilities on their respective hardware, he adds.

This collaboration establishes a local AI ecosystem that businesses can fully own and control. Stout says these features include:

1. webAI Assistant: This alternative to other AI-enabled digital assistants, lives natively on devices and interacts with users’ screens, folders, and apps without any connection to the outside world.
2. webFrame: Converts complex LLMs into smaller components and distributes them across multiple iOS devices. Through this approach, webFrame is able to transform local hardware into sophisticated clusters for greater efficiency – rivaling existing GPU clusters on the market.
3. Navigator: Utilizing a unique and intuitive drag and drop interface for connecting cameras and other data inputs, Navigator allows AI models to train on existing hardware and collaborate for specific use cases.

The new capabilities announced today by webAI focus on giving enterprises the ability to deploy large language models and run cutting-edge AI locally on Apple products including Mac, iPad, and iPhone. Stout says that additional functionality includes:

1. Enhanced Privacy and Security: Customers can process AI tasks on their personal computing device (as opposed to relying on cloud services), enabling them access to advanced AI features while controlling their own data.
2. Unprecedented Cost Control: Thanks to Apple silicon, businesses can use webAI’s platform to run models on existing hardware, significantly reducing their cloud computing costs for AI.
3. Enterprise-Scale and Performance: Enterprise customers can scale AI deployment across their organizations using webAI – enabling collaboration within their secure workplace setting and reduced latency from local AI processing.