ITNeuroNet
AIspeaky

Privacy-first AI voice architecture with provider-agnostic speech workflows

AIspeaky combines a local speech engine, desktop interface, backend services for accounts and licensing, and integrations with multiple AI, STT, and TTS providers.

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Why it mattered

Voice and AI workflows can expose sensitive user input if provider routing, credentials, and system boundaries are poorly designed.

What ITNeuroNet did

The architecture uses a privacy-first relay pattern, provider-agnostic pipeline design, contract-first backend work, and a hybrid desktop/cloud model.

Architecture / delivery pattern

The patterns that shaped this system

  • 01

    Blind Proxy / Privacy Relay

  • 02

    Provider-agnostic voice pipeline

  • 03

    Contract-first backend

  • 04

    Hexagonal / ports-and-adapters architecture

  • 05

    Desktop + cloud hybrid execution

Public scale signals

What the system looks like from the outside

  • 5,300+ tracked files
  • 2.1M+ lines of text artifacts
  • 2,798 Python files
  • 1,129 TypeScript/TSX files
  • 565 Markdown documents
  • 1,493 test files
  • 9 backend domains
  • 53 OpenAPI operations
  • 16 Postgres tables
What it proves

Sensitive AI workflow design, provider abstraction, secure relay patterns, hybrid desktop/cloud architecture, and contract-first governance.

The case demonstrates how AI voice systems can be designed with privacy, provider flexibility, local/cloud boundaries, licensing, governance, and implementation scale in mind.

Relevance to future clients

Useful for teams building AI systems that must handle sensitive user input, multiple providers, voice workflows, desktop/cloud boundaries, or privacy-first architecture.

What this made faster
  • 01Swapping AI / STT / TTS providers without rewriting the pipeline
  • 02Shipping desktop + cloud increments under one contract surface
Quality / safety controls
  • 01Privacy relay limiting what reaches third-party providers
  • 02Hexagonal architecture with explicit ports for providers and storage
  • 031,493 test files supporting refactor confidence
Related trendSecurity review and hardening
Relevant services

Engagements where this case applies

Public safety note

Sensitive implementation details are omitted from the public version. Deeper technical breakdowns may be discussed during discovery where appropriate.

Next step

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