Voice AI Agent for Personalized Task Automation
A next-generation voice assistant MVP that adapts its personality and knowledge base per user profile, achieving sub-800ms response latency — built to secure early-stage investor interest.
Client
Stealth-Mode AI Productivity Startup (NDA)
Role
Lead AI Architect & Backend Engineer
Timeline
8 weeks
Team
1 dev, 1 design
Overview
A stealth AI startup needed an investor-ready MVP for a next-generation voice assistant — one that behaves like a personalized chief-of-staff rather than a generic command executor. The agent needed to understand user context, adapt its tone and suggestions to individual profiles, and respond in near-human time. Built end-to-end in 8 weeks for an investor demo.
Process
Architected a real-time voice pipeline: Whisper STT → context injection via RAG → GPT-4o LLM → ElevenLabs TTS → WebSocket streaming back to client. Built a user profiling system storing preferences, task history, and schedule context in PostgreSQL with Redis session management for low-latency retrieval.
Key Features
Challenges & Solutions
Optimized FastAPI with async processing, selected GPT-4o Turbo for speed, and streamed TTS output in chunks rather than waiting for full response generation — achieving consistent sub-800ms latency.
Implemented RAG pulling relevant user profile snippets (preferences, history, schedule) to prime the agent's context window before every response — delivering dynamic personalization at scale.
Built frontend VAD logic that instantly pauses the AI audio stream when the user begins speaking, enabling natural conversation interruption without dead air.
Built robust WebSocket management in React and FastAPI with automatic reconnection, audio chunk buffering, and graceful degradation — maintaining session continuity on poor connections.
Results
Response Latency
end-to-end
Task Mapping Accuracy
voice to structured tasks
Investor Outcome
early-stage funding
User Trust Score
with visual state indicators
Session Continuity
Redis session persistence
MVP Delivery
8 weeks
Goals
- •Build an investor-ready voice AI MVP in 8 weeks
- •Achieve sub-second response latency for natural conversation
- •Create genuine personalization through user profile memory
- •Deliver a polished UI reflecting AI listening, thinking, and speaking states
Tech Stack
- •Python
- •FastAPI
- •ReactJS
- •WebSockets
- •OpenAI
- •ElevenLabs
- •PostgreSQL
- •Redis
Target Users
- •Busy professionals and executives
- •Entrepreneurs
- •Early tech adopters
Key Learnings
- •Voice UX is as critical as screen UX — visual state indicators dramatically improve user trust
- •RAG-based personalization outperforms rule-based customization at any scale
- •Streaming audio in chunks, not after full generation, is the key to low perceived latency
- •VAD interruption handling is what separates a natural assistant from a frustrating one
Future Plans
- •Move to on-device processing for enhanced privacy
- •Implement LangGraph for multi-step complex task planning
- •Add calendar and email integrations for real executive assistant capabilities
- •Expand to multilingual support for global user base