Adding Offline Edge AI to Rork Apps — No-Internet AI Features Without Cloud APIs

When you build an AI-powered app with Rork, connecting to OpenAI or Gemini APIs is the obvious first step. But requirements sometimes push in a different direction: users in areas without reliable internet, apps that shouldn't transmit sensitive user data, or budgets where API costs need to stay at zero.

That's where edge AI — AI running locally on a server within the same network, or eventually on-device — becomes relevant. This article covers the basics of connecting a Rork app to a local Ollama instance, with practical code for the patterns that matter most.

When Local AI Makes Sense for Your App

Not every app benefits from local AI. Some honest assessment:

Good fits:

Journal or notes apps where privacy is the primary value proposition
Outdoor apps (hiking, emergency guides) used in areas without cell coverage
Healthcare or legal apps where data cannot leave the device or network
Free apps where eliminating API costs changes the business model

Poor fits:

Apps needing real-time news or current events
Use cases requiring broad general knowledge or creativity
Apps targeting low-spec devices as the primary audience

Connecting a Rork App to a Local Ollama Server

The most practical starting architecture: run Ollama on a PC in the same local network. The app connects over WiFi via HTTP — no internet required.

Ollama exposes a simple generate API that works well from React Native:

// Simple Ollama call from Rork app
const callLocalAI = async (userMessage) => {
  const response = await fetch('http://192.168.1.10:11434/api/generate', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      model: 'gemma4:9b',
      prompt: userMessage,
      stream: false
    })
  });
  
  const data = await response.json();
  return data.response;
};

Replace 192.168.1.10 with the local IP of the machine running Ollama.

Fallback Pattern for When Local Isn't Available

Users take their phones outside the home network. A graceful fallback to cloud API keeps the app functional:

const callAI = async (userMessage) => {
  try {
    const controller = new AbortController();
    const timeoutId = setTimeout(() => controller.abort(), 3000);
    
    const response = await fetch('http://192.168.1.10:11434/api/generate', {
      method: 'POST',
      signal: controller.signal,
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
        model: 'gemma4:9b',
        prompt: userMessage,
        stream: false
      })
    });
    
    clearTimeout(timeoutId);
    const data = await response.json();
    return { text: data.response, source: 'local' };
    
  } catch (localError) {
    // Fallback to cloud API
    return await callCloudAI(userMessage);
  }
};

The source: 'local' flag lets you show the user a privacy indicator when their data stayed local.

A Basic Chat Screen Implementation

// ChatScreen.js — integrate into Rork-generated app
import React, { useState } from 'react';
import { View, TextInput, FlatList, TouchableOpacity, Text } from 'react-native';
 
export default function ChatScreen() {
  const [messages, setMessages] = useState([]);
  const [input, setInput] = useState('');
  const [loading, setLoading] = useState(false);
 
  const sendMessage = async () => {
    if (!input.trim() || loading) return;
    
    const userMessage = { id: Date.now(), role: 'user', text: input };
    setMessages(prev => [...prev, userMessage]);
    setInput('');
    setLoading(true);
    
    try {
      const response = await callAI(input);
      const aiMessage = {
        id: Date.now() + 1,
        role: 'assistant',
        text: response.text,
        isLocal: response.source === 'local'
      };
      setMessages(prev => [...prev, aiMessage]);
    } finally {
      setLoading(false);
    }
  };
 
  return (
    <View style={{ flex: 1, padding: 16 }}>
      <FlatList
        data={messages}
        keyExtractor={item => item.id.toString()}
        renderItem={({ item }) => (
          <View style={{
            alignSelf: item.role === 'user' ? 'flex-end' : 'flex-start',
            backgroundColor: item.role === 'user' ? '#007AFF' : '#E5E5E5',
            borderRadius: 12,
            padding: 12,
            marginBottom: 8,
            maxWidth: '80%'
          }}>
            <Text style={{ color: item.role === 'user' ? 'white' : 'black' }}>
              {item.text}
            </Text>
            {item.isLocal && (
              <Text style={{ fontSize: 10, color: '#888', marginTop: 4 }}>
                🔒 Processed locally
              </Text>
            )}
          </View>
        )}
      />
      
      <View style={{ flexDirection: 'row', marginTop: 8 }}>
        <TextInput
          value={input}
          onChangeText={setInput}
          placeholder="Type a message..."
          style={{ flex: 1, borderWidth: 1, borderRadius: 8, padding: 8 }}
        />
        <TouchableOpacity
          onPress={sendMessage}
          disabled={loading}
          style={{ marginLeft: 8, backgroundColor: '#007AFF', borderRadius: 8, padding: 8 }}
        >
          <Text style={{ color: 'white' }}>Send</Text>
        </TouchableOpacity>
      </View>
    </View>
  );
}

Getting Started

The minimum viable test: start Ollama on your development machine, note its local IP address, update the endpoint in the code above, and build a Rork app that makes a simple API call. The mechanics are simpler than most AI integrations.

From there, the interesting questions become architecture questions: how do you distribute the local server setup to users? How do you handle model updates? How do you build truly on-device inference?

The premium article covers those questions in depth — on-device model integration, model distribution strategies, background inference, and building a fully offline-capable app.