In traditional apps, you have a fixed data model → fixed API → fixed UI.
With the AI-routed app, a self-describing payload + optional ask → AI router → materialized data/actions/UI
You start with an app that has only 2 AI routers/ interpreters, one in the BE and one in the FE. You say “store the financial data in these company reports / PDFs, saving the PDF for tracking”. Then the BE router extracts data and PDF and saves it as lets say a CSV. When data is loaded, the FE router knows that it’s got financial data and will guess an interface for that. Then user say: “now show me interactive charts for it” and the FE router creates it.
Importantly, the FE router doesn’t need to know beforehand that this is a “financial dashboard.” It receives a payload whose semantics are already exposed, plus the ask, and can decide that charts, tables, filters, etc. are the appropriate representation.
┌──────────────────────┐
│ SELF-DESCRIBING │
│ PAYLOAD │
└──────────┬───────────┘
│
+ ASK
│
▼
┌────────────────────────┐
│ BACKEND AI ROUTER │
└───────────┬────────────┘
│
┌─────────┴─────────┐
▼ ▼
DATA ACTIONS
│ │
└─────────┬─────────┘
│
▼
┌────────────────────────┐
│ FRONTEND AI ROUTER │
└───────────┬────────────┘
│
┌─────────┴─────────┐
▼ ▼
UI INTERACTIONS