How to Use Realtime AI in Mobile Apps: Complete Guide 2025

How do you implement realtime AI in mobile apps?

Implement realtime AI in mobile apps by using streaming APIs (OpenAI/Claude streaming), WebSockets for bidirectional communication, edge computing for low latency, and optimized token buffering. Realtime AI enables instant responses, voice interactions, and live translations—creating experiences that feel truly conversational and responsive.

Realtime AI is transforming mobile user experiences. From live voice assistants to instant image analysis, users now expect AI to respond instantly—not in seconds, but milliseconds. This guide shows you how to build realtime AI features that feel magical.

What is realtime AI and why does it matter?

Realtime AI processes and responds to user input with minimal latency (under 200ms perceived delay). Unlike traditional AI that processes requests in batches, realtime AI streams responses as they're generated, creating fluid, conversational experiences.

Key benefits of realtime AI in mobile apps:

• Instant feedback - Users see AI thinking and responding in real-time
• Better UX - No waiting for complete responses, reduces perceived latency
• Voice interactions - Essential for natural speech-to-speech AI
• Live translations - Translate speech and text as users speak
• Interactive AI - Users can interrupt, redirect, or refine AI mid-response

How to implement streaming AI responses in React Native

Streaming is the foundation of realtime AI. Instead of waiting for the entire response, you receive tokens as the AI generates them:

// Streaming AI implementation with OpenAI
const streamAIResponse = async (message: string) => {
  const response = await fetch('https://api.openai.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${OPENAI_API_KEY}`,
    },
    body: JSON.stringify({
      model: 'gpt-4-turbo',
      messages: [{ role: 'user', content: message }],
      stream: true, // Enable streaming
    }),
  });

  const reader = response.body?.getReader();
  const decoder = new TextDecoder();

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value);
    const lines = chunk.split('\n').filter(line => line.trim());

    for (const line of lines) {
      if (line.startsWith('data: ')) {
        const data = line.slice(6);
        if (data === '[DONE]') return;

        try {
          const parsed = JSON.parse(data);
          const token = parsed.choices[0]?.delta?.content;
          if (token) {
            // Update UI with each token
            setResponse(prev => prev + token);
          }
        } catch (e) {
          console.error('Parse error:', e);
        }
      }
    }
  }
};

Best practices for realtime AI performance

Optimizing realtime AI requires careful attention to latency, bandwidth, and user experience:

• Token buffering - Buffer 3-5 tokens before displaying to smooth animation
• Edge computing - Use edge functions (Cloudflare Workers, Vercel Edge) to reduce latency
• Connection pooling - Reuse WebSocket connections instead of creating new ones
• Graceful degradation - Fall back to batch processing if streaming fails
• Cancel tokens - Allow users to stop generation to save costs and improve UX

WebSockets vs Server-Sent Events vs HTTP Streaming

Three main approaches for realtime AI communication:

• WebSockets - Bidirectional, low latency, best for voice AI and interactive chat
• Server-Sent Events (SSE) - Unidirectional, simpler than WebSockets, good for text streaming
• HTTP Streaming - Works everywhere, easier to implement, slightly higher latency

For most React Native AI apps, HTTP streaming with fetch() provides the best balance of simplicity and performance.

Building realtime voice AI in React Native

Voice requires the lowest latency possible. Here's the complete flow:

• Speech-to-Text (STT) - Use Whisper API or Deepgram for transcription
• AI Processing - Stream response from GPT-4 or Claude
• Text-to-Speech (TTS) - Use OpenAI TTS or ElevenLabs for natural voice
• Total latency target - Under 1 second from voice input to audio output

How AI Mobile Launcher simplifies realtime AI

Building realtime AI from scratch requires weeks of optimization. AI Mobile Launcher includes:

• Pre-built streaming chat components with token buffering
• Optimized WebSocket manager for voice AI
• Edge function templates for minimal latency
• Voice AI module with STT + LLM + TTS pipeline
• Connection retry logic and error handling

For Developers: Try AI Mobile Launcher's realtime AI modules to ship voice and chat features in days, not weeks.

For Founders: Need a production-ready realtime AI app? CasaInnov builds custom AI mobile solutions with guaranteed sub-second latency.