MegaPortal

Teams use MegaPortal to turn an existing trained model into something that runs inside an iPhone or iPad app. A typical workflow starts by bringing your model into the platform, then setting up the on-device steps around it: cleaning or reshaping inputs from the camera, microphone, sensors, or forms, running the prediction locally, and formatting the result so the app can act on it. Because inference happens on the device, it fits situations where network access is limited, where results need to appear instantly, or where sensitive data should stay on the user’s hardware.

In practice, MegaPortal is applied when you need repeatable, app-specific behavior instead of a fixed pipeline. You can define when inference is triggered, how often it runs, what happens when inputs are missing, and how outputs are filtered or converted into UI-ready signals. Builders use this to ship features like real-time classification, on-device text or image analysis, or background processing that continues in the field without a server dependency. The platform also supports day-to-day delivery work by keeping product details and policy references close at hand, making it easier to confirm usage terms and privacy expectations while you integrate the model into a production app.