Build voice AI systems that ship
Master the tools, design patterns, and deployment strategies behind conversational voice AI — from speech recognition pipelines to production-ready voice agents. Build systems that actually talk back.
"Every module is built around a decision you'll actually face in production — because that's where voice AI either works or falls apart."— Dora Edney
What you'll learn
What you'll be able to do
- Design and deploy a full end-to-end voice AI pipeline covering speech-to-text, LLM reasoning, and text-to-speech
- Evaluate and select the right STT/TTS providers (Whisper, Deepgram, ElevenLabs, Google, Azure) for a given use case
- Build low-latency voice agents with interruption handling, turn-taking logic, and fallback strategies
- Integrate voice AI into web and telephony surfaces using WebSockets, WebRTC, and SIP/PSTN APIs
- Prompt-engineer and fine-tune conversational flows so voice responses sound natural, concise, and on-brand
- Monitor, evaluate, and iterate on live voice AI systems using transcription audits, latency metrics, and user feedback loops
How it works
A school that adapts to you
This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.
We learn your level
A quick placement check tailors your starting point so you're never bored or lost.
Lessons adapt as you go
Each lesson is written for your pace and your goal, adjusting as your skills grow.
Your AI coach keeps you moving
Checkpoints, feedback, and gentle nudges turn progress into a real result.
The curriculum
What's inside your school
6 modules · 21 lessons
How Voice AI Systems Work
Establish the conceptual and technical foundation every student needs before writing a single line of voice code. By the end of this module, students can describe every stage of the pipeline, reason about audio data, and run a working end-to-end round-trip — giving them a mental model to hang all later learning on.
- 1.1Anatomy of a Voice AI PipelineIncluded
- 1.2Audio Fundamentals for DevelopersIncluded
- 1.3Voice Activity Detection and Utterance SegmentationIncluded
- 1.4Your First End-to-End Voice Round-TripIncluded
Speech-to-Text — Choosing and Tuning Your Ears
Equip students to evaluate, select, and optimize STT providers for real-world conditions. Students benchmark accuracy and latency across providers, implement streaming for low-latency transcription, and apply domain-specific tuning techniques — directly fulfilling the 'evaluate and select STT providers' outcome.
- 2.1STT Provider Landscape and BenchmarkingIncluded
- 2.2Streaming STT and Interim TranscriptsIncluded
- 2.3Improving STT Accuracy for Your DomainIncluded
LLM Reasoning and Conversational Voice Design
Teach students to shape LLM behavior specifically for the constraints and expectations of spoken conversation — brevity, naturalness, state continuity, and graceful failure. This module bridges raw transcription output to a response that is safe to speak aloud.
- 3.1Prompt Engineering for Spoken ResponsesIncluded
- 3.2Managing Dialogue State and Multi-Turn MemoryIncluded
- 3.3Fallback Strategies, Error Handling, and Edge CasesIncluded
Text-to-Speech — Crafting a Voice Worth Listening To
Students evaluate TTS providers, optimize for low latency through audio streaming, and control prosody and voice character to produce output that sounds natural, on-brand, and appropriate for the deployment surface — fulfilling the TTS selection and voice-design outcomes.
- 4.1TTS Provider Evaluation and Voice SelectionIncluded
- 4.2Streaming TTS and Reducing Time-to-First-AudioIncluded
- 4.3SSML, Prosody, and Voice CustomizationIncluded
Real-Time Voice Agents — Interruptions, Turn-Taking, and Deployment
Bring all pipeline components together into production-grade real-time agents that handle the complexities of live conversation: interruptions, barge-in, turn-taking, duplex audio, and deployment across web and telephony surfaces. This is the integration capstone module.
- 5.1WebSockets and WebRTC for Real-Time VoiceIncluded
- 5.2Interruption Handling and Turn-Taking LogicIncluded
- 5.3Telephony Integration — SIP, PSTN, and TwilioIncluded
- 5.4Deploying Voice Agents on Web ProductsIncluded
Production, Evaluation, and Continuous Improvement
Ensure students can safely release, monitor, and iteratively improve live voice AI systems. This module closes the loop between deployment and refinement, covering latency optimization, transcription quality auditing, observability, and structured user-feedback pipelines — fulfilling the monitoring and iteration outcome.
- 6.1Latency Profiling and Performance OptimizationIncluded
- 6.2Transcription Audits and Accuracy EvaluationIncluded
- 6.3Security, Privacy, and Compliance FundamentalsIncluded
- 6.4Monitoring, Alerting, and the Feedback LoopIncluded
Who it's for
Is this you?
Backend developers
You're fluent in APIs and services but have never touched an audio pipeline — this course gives you the complete system-design map to build voice AI without the guesswork.
Technical founders
You're scoping a voice-first product and need to make fast, defensible decisions on provider selection, architecture, and deployment before you commit to a stack.
Product managers
You spec voice features but need to deeply understand what's technically hard — latency, turn-taking, fallback logic — so your roadmap reflects reality, not demos.
Full-stack engineers
You've built web apps end-to-end and want to add real-time voice interfaces using WebSockets and WebRTC without starting from scratch on the audio layer.
AI/ML engineers
You know how the models work but haven't wired them into a live, low-latency conversational system — this course bridges the gap from model to shipped product.
Telephony builders
You're integrating voice AI into phone channels via Twilio, SIP, or PSTN and need the full stack — from speech recognition tuning to production monitoring — in one place.
Questions
Frequently asked
Your teacher
A note from your teacher
Dora Edney
If you've searched for voice AI tutorials, you've probably found one of two things: a five-minute demo that wires OpenAI's API to a microphone and calls it a day, or an academic deep-dive into speech models that never touches deployment. Neither of those helps you build a product.
I designed Voice AI Studio because I kept running into the same gap — developers who understood APIs but had no map for the system design decisions that make voice AI actually work. What do you do when your STT misrecognizes domain-specific terms? How do you handle a user who interrupts the agent mid-sentence? How do you keep end-to-end latency low enough that the conversation doesn't feel robotic? These aren't edge cases — they're the job.
This course is built around the decisions you'll face in a real build. We benchmark providers against each other, not just explain what they do. We stream audio from the moment we can, because batch processing kills the conversational feel. We write prompts specifically for spoken output — concise, natural, and tolerant of the way people actually talk — not for chat UIs. We integrate with telephony because a huge share of real voice AI use cases live on the phone, not in a browser. And we close with production operations: how to monitor a live system, run transcription audits, profile your latency stack, and build the feedback loop that makes your voice AI better over time.
I built this for developers who want to ship. The concepts are rigorous because imprecision costs you in production. The pace is fast because your time matters. And every module ends with something working — because that's the only way to know you've actually learned it.
Come build something that talks back.
— Dora Edney
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- 6 modules, 21 lessons
- AI-adaptive lessons tuned to your level
- Quizzes & checkpoints to lock in progress
- Your own AI learning coach
- Learn on any device, at your pace
- Full access for as long as you're subscribed