How to Add Voice AI to Your Product

Voice has been “the next thing” in product development for years. What’s changed is that it now actually works. OpenAI Whisper, Deepgram, ElevenLabs — the infrastructure is reliable, the pricing is reasonable, and the accuracy has reached the point where you can ship it without apologizing.

But just because voice is buildable doesn’t mean it fits every product. This post covers the three components of a voice AI system, when to build each one, and the practical challenges most teams discover after they’ve already started.

What “Voice AI” Actually Means

Voice AI is three separate capabilities. Most products need one or two of them — rarely all three at once.

Speech-to-text: turning audio into text

This is the input layer. The user speaks; your backend receives audio, sends it to a transcription API, and gets back text. That text then feeds into whatever your product does with it — an LLM prompt, a search query, a database write.

This is the most mature part of the voice stack. Accuracy is high, latency for async transcription is measured in seconds, and the integration is a simple API call.

Text-to-speech: turning text into audio

This is the output layer. Your product generates a response — whether from an LLM or a template — and reads it back to the user in a synthesized voice.

The quality gap between TTS providers is wide. OpenAI’s TTS sounds good but robotic under a magnifying glass. ElevenLabs produces voices that are close to indistinguishable from human speech, including emotion and pacing. The tradeoff is cost: ElevenLabs charges per character, and long responses add up fast.

Real-time voice interfaces: the full loop

This is the hard one. Audio in, audio out, with a short enough delay that the conversation doesn’t feel broken. Think of it as a live phone call with an AI on the other end.

Real-time requires WebRTC or a persistent WebSocket connection, streaming transcription (not batch), streaming LLM responses, and streaming TTS — all chained together. The latency budget for a usable voice conversation is around 800ms end-to-end. Each step in that chain needs to stay well under its share.

When to Build Voice Features

Use cases where voice wins

Voice AI is genuinely useful when the user’s hands or eyes are occupied — field technicians logging work orders, medical staff documenting during procedures, users driving or cooking. If typing would be awkward given the physical context, voice is the right call.

It also wins for long-form input. Dictating a 500-word daily update takes 2 minutes by voice, 10 minutes by typing. Products that involve journaling, notes, or detailed data entry see real adoption when you add transcription.

TTS works well for content that’s dense but consumed passively — financial summaries, status updates, lengthy AI responses. Users can listen while doing something else. This is why most AI assistant UX has added voice output as a standard feature.

Use cases where voice is the wrong choice

Most desktop B2B products don’t need voice. If your users are at a desk with a keyboard, typing is faster, quieter, and less disruptive to people nearby. Adding voice to a project management tool or analytics dashboard typically gets ignored.

Voice also breaks down in noisy environments if you haven’t accounted for it. Background noise tanks transcription accuracy and frustrates users. If your product is used in unpredictable acoustic conditions, you need noise suppression in the pipeline — which adds complexity and cost.

The Tech Stack in 2026

Speech-to-text

• Deepgram — best for real-time use cases; streaming transcription with sub-300ms latency
• OpenAI Whisper — strong default for async transcription; multilingual, including solid Hebrew support
• AssemblyAI — good if you need speaker diarization (who said what), content moderation, or chapters

Text-to-speech

• ElevenLabs — highest quality, supports voice cloning; use for products where the voice experience is a feature, not just a utility
• OpenAI TTS — easier to integrate if you’re already in the OpenAI ecosystem; good quality at a lower price point
• Google TTS / Azure Cognitive Services — enterprise options; not as natural-sounding but easier to get approved for regulated industries

Real-time infrastructure

Real-time voice requires a different architecture than async transcription. You need persistent connections, not REST calls. Most teams use WebRTC to handle the browser-to-server audio channel, then stream the transcription and LLM response over WebSocket. Daily.co and Livekit are popular managed infrastructure options that abstract the WebRTC complexity.

What to Build First

Start with async transcription

If voice is new to your product, ship async first. Let users upload or record audio and get back a transcript within 5–10 seconds. This is a single API integration, works reliably, and validates whether your users actually want voice input before you invest in real-time.

Add TTS for AI responses

If you already have an LLM feature, adding a “listen” button that reads the response aloud is low effort and high value. One API call to OpenAI TTS or ElevenLabs, returning an audio file — it takes a week to implement well. Link back to a quickdev AI development service engagement if this is the full context.

Go real-time only when you must

Real-time voice is a product decision, not an engineering default. Build it when your core UX requires it — a voice assistant, a conversational interface, hands-free workflows. Not because it’s technically possible. The engineering cost is 3–5× the async version, and the operational complexity (latency monitoring, connection handling, mobile audio routing) stays with you after launch.

The Hard Parts Nobody Mentions

Latency compounds. Every step in the pipeline adds delay. Transcription: 200ms. LLM call: 400ms. TTS: 300ms. That’s 900ms already — before network round trips. In real-time voice, you have to stream every component in parallel or the conversation feels like a landline call on a bad connection.

Accents and domain terms hurt accuracy. Generic models transcribe standard American English well. Israeli accents, technical terms, product names, and industry jargon reduce accuracy significantly. Plan for a fine-tuning step or a post-processing layer that corrects domain-specific vocabulary.

Cost surprises teams. Audio is cheap per minute, but users speak a lot. A product with 10,000 daily active users generating 3 minutes of audio each is 30,000 minutes per day. At $0.004/minute, that’s $120/day — before TTS costs. Model this before committing to a voice-heavy architecture.

For Israeli founders building mobile apps or AI-native products, voice is becoming a competitive feature — not a luxury. The question isn’t whether to build it but which capability to start with and how to contain the scope.

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Yaniv Amrami is founder of quickdev. He has built voice AI features for mobile and web products serving Israeli and global markets.