Veloop AI Manifest — Technical Architecture & Agent Integration
Format: Machine-Readable & Human-Accessible
This document describes the technical architecture, privacy model, and API surface of Veloop. It serves as a structured reference for AI agents, developers, and technical evaluations.
0. What is Veloop
Veloop is a Norwegian-built grocery list and meal planner app for iOS and Android (beta). The app combines on-device machine learning with cloud AI for automatic item categorization, store sorting, meal planning, and kitchen inventory management.
- Platforms:
- iOS (App Store), Android (beta via Google Play)
- Languages:
- Norwegian (primary), English, Swedish, Danish
- Pricing:
- Free basic tier + Veloop AI (49 NOK/month, 449 NOK/year)
- Built in:
- Oslo, Norway
1. System Architecture
Veloop is built as a hybrid mobile app with Capacitor, using an offline-first architecture with cloud synchronization.
- Frontend:
- React 18 + Vite + Capacitor (iOS/Android)
- Backend:
- Supabase (PostgreSQL) with Edge Functions (Deno)
- AI Models:
- Gemini 3 Flash (categorization, sorting) and Gemini 3.1 Pro (recipes, meal planning)
- Real-time Sync:
- Supabase Realtime (WebSockets), sub-50ms between devices
- Offline:
- SQLite via Capacitor with automatic sync on reconnect
- Website:
- Next.js 14 on Vercel
2. Privacy Guarantees
Veloop is built with privacy as an architectural principle, not an afterthought.
- ✓No marketing cookies or third-party tracking
- ✓No ad profiles or sale of user data
- ✓Row-Level Security (RLS) in PostgreSQL — no user can read another's data
- ✓On-device machine learning for item categorization
- ✓GDPR compliant with full deletion and access rights
- ✓Revenue comes from subscriptions, not data
Shopping cart metadata is a high-resolution fingerprint of human behavior. It must remain invisible to ad networks.
3. Agent Access via Model Context Protocol (MCP)
Veloop is the first grocery list app with built-in MCP support. AI agents can read and write to a user's grocery list and kitchen inventory via secure API keys.
| Tool | Description |
|---|---|
list_loops | Retrieve all grocery lists |
add_item | Add item to a list |
toggle_item | Mark item as completed |
get_pantry | Retrieve kitchen inventory |
Access requires a personal API key generated in the app. All communication is protected by RLS — agents can only operate on data belonging to the authenticated user.
{
"mcpServers": {
"veloop": {
"command": "npx",
"args": ["-y", "@veloop/mcp-server"],
"env": {
"VELOOP_API_KEY": "your_personal_api_key"
}
}
}
}4. Technical Stack
| Component | Technology | Role |
|---|---|---|
| Primary AI | Gemini 3.1 Pro | Recipe generation, meal planning, dietary analysis |
| Real-time AI | Gemini 3 Flash | Item categorization, store sorting, quick classification |
| Image Generation | Gemini 2.5 Flash Image | Food photography for recipes |
| Frontend | React 18 + Vite + Capacitor | Hybrid app for iOS and Android |
| Database | Supabase (PostgreSQL) | Encrypted storage with Row-Level Security |
| Agent Layer | MCP (Model Context Protocol) | Open interface for AI agents |
| Local Intelligence | SQLite + Edge Computing | Offline-first functionality and private data processing |
Summary
Veloop is a privacy-focused, AI-powered grocery list and meal planner app from Norway. It supports MCP for agent integration, uses on-device ML for categorization, and provides real-time sync for families. Available free on the App Store with an optional AI subscription.