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Kushalgouda Patil
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What if your voice AI was private, modular, deterministic wherever possible, and generative wherever necessary?

I am currently building this system and will be sharing both the architecture and the implementation details in a series of upcoming posts.

Problem: Modern voice assistants rely deeply on third party providers and nearly every request is routed through LLMs. While this approach offers flexibility, it introduces concerns around privacy, data control, latency and predictability.

Risk: For enterprises and regulated systems sending sensitive voice data to third party cloud is unacceptable. Deterministic commands such as Device Controls, Structured Operations, System Level Instructions etc should not require generative reasoning. A purely LLM driven architecture increases cost, latency and unpredictability.

Solution: These challenges highlight the need for a different architecture: A Voice AI system that runs with private infrastructure, separates deterministic intent recognition from generative reasoning, balances control with intelligence through a modular, scalable, cost effective hybrid design

Design Goals

  • Deployable entirely on premise or within private cloud infrastructure
  • No dependency on external AI APIs for core processing
  • Modular microservice architecture with clear service boundaries
  • Deterministic intent handling for predefined commands
  • Generative fallback using LLM inference when required
  • Tool augmented reasoning layer
  • REST based interfaces for edge and web integration
  • Horizontal scalability and service isolation
  • Privacy first data handling and controlled logging

High Level System Architecture

Microphone → STT Service → Intent Recognition → (Deterministic Handler | LLM Inference Service → Tool Integration Layer) → TTS Service → Speaker

Privacy and Deployment Model

The system is designed to operate entirely within private infrastructure. All core components such as speech to text, intent recognition, LLM inference, and text to speech run as internal microservices deployed on premise or within a private cloud environment.

Hybrid Intelligence Approach

  • Structured commands are detected using regex based intent recognition for predictable handling, reducing cost and latency.
  • Queries that do not match deterministic patterns are processed by the LLM for reasoning.
  • The LLM can access external tools such as weather services or web search when additional information is required.

Upcoming Deep Dives

This post introduces the overall architecture of the system. In the coming posts, I will explore each core component in detail and discuss the design decisions and implementation. Upcoming deep dives will cover:

  • Speech to Text microservice and audio ingestion pipeline
  • Intent recognition and deterministic command handling
  • LLM inference service and reasoning workflow
  • Tool integration layer for external capabilities like weather and websearch
  • Text to Speech service and response generation pipeline

This project is a personal side project built independently and does not represent the systems or views of my employer.