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Strategy for Advancing the Voice Pipeline

Overview

The current voice pipeline faces rigidity, delays, poor voice handling, clunky agent transitions, weak error recovery, and maintenance challenges. The solution is a flexible, LLM-powered, streaming voice AI architecture prioritizing real-time adaptability, low latency, and voice-specific optimizations.

Detailed Plan

1. Core Architecture Redesign

Goal: Build an adaptable, low-latency system for non-linear conversations and robust voice processing.

Steps:

  • Unify Agents: Consolidate agents into one LLM-powered agent using context-aware memory (Redis) to reduce handoff errors.
  • Non-Linear Flows: Enable dynamic rerouting for interruptions (e.g., user jumps to pricing) via intent detection.

Tools/Frameworks:

  • Grok 3 (xAI): Semantic understanding for complex dialogues.
  • ElevenLabs: Low-latency TTS with dynamic tone.
  • Rasa: Non-linear conversation flows.
  • LangChain/LangGraph: Orchestrate LLM and tools.
  • Redis: State persistence.

Implementation:

  1. Initialize Rasa project and define stories for non-linear scenarios.
  2. Use LangGraph for single-agent orchestration, storing state in Redis.
  3. Deploy ElevenLabs TTS with rules for adaptive speed/tone based on user metadata.

2. Prototype

  • Build a minimal pipeline for one use case (e.g., scheduling) using Rasa, Grok, and ElevenLabs.
  • Test with simulated calls via Twilio or similar platforms.