When people think about AI in audio, they tend to think in terms of models: speech-to-text (STT), text-to-speech (TTS), voice changers, noise suppression, speaker diarization, etc. Startups, investors, and the media following them tends to focus on what’s possible with a particular class of real time audio model. That’s especially true of voice to voice models and text to speech companies that are increasingly focusing on real time voice agent use cases. And to be clear, the advancements in these models are both impressive and game-changing in terms of the new use cases that are now possible. 

But the real challenge begins after training.

If you want to build a real-time, AI-powered audio application, you’ll quickly find that training the model is just one small part of a sprawling, deeply technical stack. A huge amount of work lies in deploying these models in real-world conditions—especially when trying to support low-latency, low-power, on-device inference.

This is what much of the industry doesn’t even recognize yet, as well as where many product teams stumble to bridge the gap between a model and a usable product. This is our focus at Synervoz. And it’s also why we built the Switchboard SDK.

Real-Time Audio AI Is More Than Just Models

1. The Myth of "Just Plug In a Model"

Many product teams assume they can easily plug models into an app. And to some extent that’s true. The model companies generally have APIs available and you can get a prototype up and running with few lines of code. But in production you’ll quickly run into constraints that force an on device audio graph to be built. Things like:

• Getting access to the microphone

• An on-device voice activity detector (VAD) to decide when to run the model and avoid sending and receiving endless amounts of data to the cloud unnecessarily while incurring extremely high costs and draining the battery.

• Chunking audio into batches that align with model input expectations.

• Adding buffering and jitter management to ensure you're not getting dropouts or overlap.

• You often need preprocessing filters like automatic gain control, high-pass filters, or denoising before feeding the model.

• You may need to resample or convert formats between parts of the pipeline

All of this happens in real time.

2. System Integration Is the Hidden Beast

Models run inside ecosystems. In real-time audio, that ecosystem has very tight constraints:

• Latency: Anything over ~150ms and you break the illusion of real-time.

• Power: On-device inference drains battery fast, especially on mobile or wearables.

• Memory: Devices like earbuds or edge gateways don’t have room for large models.

• Hardware quirks: You may be deploying across ARM64, x86, Android, iOS, or embedded Linux, each with their own constraints and opportunities to optimise.

• Concurrency: Models must run alongside audio playback, network streaming, UI rendering, and other real-time services.

This means you must:

• Optimize for specific hardware acceleration (like Apple Neural Engine or Qualcomm DSPs).

• Strip down models or distill them to smaller, faster versions.

• Build scheduling logic to avoid CPU/GPU contention in multi-tenant systems.

• Ensure audio pipeline synchronization, which is often harder than it sounds.

The model is just an engine

To power AI use cases like live translation, voice avatars, real-time captioning, or noise suppression, you need a robust, adaptable pipeline that wraps around the model like scaffolding:

A car analogy might help:

• The model is the engine.

• The audio pipeline is the entire drive train: from the crankshaft through the transmission and tires.

• If any part fails, the whole thing breaks down.

• And you can’t just drop an engine into any chassis. The whole design needs to fit together. 

• An assembly line is the only way you can put this together fast, and at scale.

The Audio Pipeline Responsibilities:

• Capture: Mic input with minimal delay, echo-cancelled, gain-controlled.

• Buffer: Manage frames, timestamps, jitter correction.

• Process: Feed the right frames to the right models at the right time.

• Route: Send outputs to playback, network, logs, transcripts, analytics, or other agents.

• Sync: Maintain tight coordination with other streams (e.g. media playback in a watch party or robot perception in multimodal systems).

Where Switchboard SDK Comes In

Most teams aren’t set up to solve these problems well. They:

• Waste months building glue code for pipelines.

• Struggle to switch platforms (e.g. from iOS to Android or Web) and end up hiring experts to rebuild for each.

• Can’t run multiple models together (like STT + Voice Cloning + Noise Cancellation).

• End up building monoliths with no modularity, flexibility to make changes, or reuse components.

Switchboard solves this.

What Switchboard Does:

• Provides a modular, real-time audio graph engine. Similar to modular DSP systems, but it’s real time, and with all the nodes you need for Voice AI and other real time audio pipelines.

• Comes with built-in nodes for audio capture, playback, VAD, STT, TTS, media players, custom DSPs, etc.

• Supports hybrid graphs—run some nodes locally, others in the cloud.

• Has first-class support for on-device inference, multi-platform SDKs (Swift, Kotlin, JS), and BYO model integration.

• Handles cross-thread timing, buffering, and low-latency audio IO.

• Allows devs to rapidly compose new use cases like voice agents, real time podcast generation with co-listening, or real-time audio effects chains for your social app or game.

The Future: Smarter Pipelines, Not Just Smarter Models

As AI models become more commoditized, differentiation will come from system integration:

• Who can run them faster, with lower latency?

• Who can run multiple models in parallel?

• Who can run them on device, not just in the cloud?

• Who can adapt them to quirky edge conditions like dropped frames, language switches, or Bluetooth handovers?

That’s where the real innovation is happening now.

There’s where we live, and that’s what Switchboard enables you to do. Switchboard ushers in a future where building a real-time voice app with multiple AI models running in parallel is as easy as spinning up a web app. Where latency-aware pipelines and hardware-aware inference are no longer science projects, but developer tools.

AI is transforming real-time audio—but only for teams that embrace the full stack. The future isn't just about training better models. It’s about shipping better systems.

With Switchboard, developers can stop worrying about plumbing and focus on building magical, real-time audio experiences.