Voice-First AI Companion with Memory
Native mobile app featuring an emotionally intelligent AI companion with long-term memory, proactive recommendations, and real-time voice interaction via Gemini 2.0.
The Challenge
Most virtual assistants are reactive and stateless — they answer questions but don't remember context, preferences, or past conversations. Our client wanted to build an AI companion that genuinely knows the user: remembering preferences, tracking habits, and proactively initiating helpful conversations.
Our Approach
We built a native iOS/Android mobile application combining real-time multimodal AI with a persistent memory layer.
Voice-First Interaction
WebSockets and WebRTC enable real-time bidirectional communication. The app supports seamless voice and text interaction with low-latency streaming, making conversations feel natural and responsive.
Persistent Memory
MongoDB stores long-term user preferences, conversation history, and behavioral patterns. The AI references this memory during every interaction, creating a personalized experience that improves over time.
Proactive AI
Powered by the Gemini 2.0 Live Multimodal API, the companion doesn't just respond — it initiates. Based on stored preferences and patterns, it proactively suggests recommendations, reminders, and conversation topics.
Backend Architecture
A FastAPI backend manages messaging, memory persistence, and AI orchestration. Firebase handles authentication and push notifications for proactive engagement.
Technical Stack
- Mobile: Native iOS & Android
- AI: Gemini 2.0 Live Multimodal API
- Backend: FastAPI, Firebase
- Database: MongoDB (memory/preferences)
- Communication: WebSockets, WebRTC
- Languages: Swift, Kotlin, Python
Results
- Emotionally intelligent AI companion with persistent memory
- Seamless voice and text interaction in real-time
- Proactive recommendations based on learned preferences
- Scalable architecture supporting concurrent users