Speech-to-Text Integration
Streaming ASR tuned for accuracy on your domain vocabulary and call-quality audio.
Voice-First AI Development
Voice-first AI covers speech-to-text, text-to-speech, and the voice agents built on top of them: systems where the primary interface is a phone call or spoken interaction, not a text box. That means different architecture decisions than a chat product: streaming audio pipelines, turn-taking and interruption handling, telephony integration, and latency budgets measured in hundreds of milliseconds, not seconds.
2-3 Weeks
Typical Delivery Timeline
The Architecture Behind a Real Voice Agent
1
Speech-to-Text (ASR)
- Streaming vs. batch transcription (Whisper, Deepgram, AssemblyAI, Google STT)
- Word-error-rate tradeoffs across accents, domains, and audio quality
- Custom vocabulary/boosting for product names and domain jargon
- Handling background noise and crosstalk on real phone lines, not just clean studio audio
2
Text-to-Speech (TTS)
- Voice and latency tradeoffs across ElevenLabs, Azure/Google neural TTS, and OpenAI TTS
- Streaming synthesis so the caller hears the first words before the full response finishes generating
- SSML control for pacing, emphasis, and pronunciation
- Voice cloning considerations, including consent and licensing requirements
3
Conversation & Turn-Taking
- Voice activity detection (VAD) to know when the caller has actually stopped speaking
- Barge-in/interruption handling so callers can cut off the agent mid-sentence
- End-to-end turn latency budget, typically 300-800ms, to feel like a natural conversation
- Fallback and escalation to a human when confidence drops or the caller asks
4
Telephony & IVR Integration
- SIP/PSTN integration via Twilio, Vonage, or Telnyx
- Replacing or augmenting legacy IVR trees with a conversational front end
- Call recording, transcription logging, and consent compliance, which varies by state and country
- DTMF (touch-tone) fallback alongside voice for callers who prefer it
5
Latency Architecture
- End-to-end pipeline (ASR then logic then TTS) with latency budgeted per stage
- Streaming every stage rather than waiting for full completions before starting the next
- Model/provider size selection tradeoffs for time-sensitive turns
- Regional deployment to cut network round-trip time
Our Voice-First AI Services
Speech pipelines and telephony integration, built for real conversation
Text-to-Speech & Voice Selection
Low-latency streaming TTS with voice, pacing, and tone matched to your brand.
Telephony & IVR Integration
SIP/PSTN connections via Twilio or Telnyx, replacing or augmenting existing IVR systems.
Conversation Design
Turn-taking, barge-in handling, and escalation logic tuned against real call recordings.
Latency Optimization
End-to-end pipeline tuning to hit sub-second response times that feel natural on a call.
Call QA & Transcription
Recording, transcription, and structured logging for quality review and analytics.
Compliance & Consent Handling
Call recording consent and data handling built to match applicable state and regional rules.
Why Teams Pick SpeedMVPs for Voice AI
Latency budgeted stage by stage
We measure and optimize ASR, logic, and TTS latency separately, because a voice agent that takes seconds to respond feels broken even if each component works.
Built for interruption, not just dictation
Real callers talk over the agent, change their mind mid-sentence, and pause. We design for barge-in and natural turn-taking, not a rigid Q&A script.
Telephony is a first-class integration
SIP/PSTN connectivity, DTMF fallback, and call routing are part of the initial build, not something bolted on after a browser-mic demo works.
Escalation paths are designed in
Every voice agent we build has a defined path to a human when confidence drops or the caller asks for one.
Why Teams Pick SpeedMVPs for Voice AI
Latency budgeted stage by stage
We measure and optimize ASR, logic, and TTS latency separately, because a voice agent that takes seconds to respond feels broken even if each component works.
Built for interruption, not just dictation
Real callers talk over the agent, change their mind mid-sentence, and pause. We design for barge-in and natural turn-taking, not a rigid Q&A script.
Telephony is a first-class integration
SIP/PSTN connectivity, DTMF fallback, and call routing are part of the initial build, not something bolted on after a browser-mic demo works.
Escalation paths are designed in
Every voice agent we build has a defined path to a human when confidence drops or the caller asks for one.
Voice-First AI, FAQ
Human conversation has roughly 200-500ms of gap between turns; anything beyond about 800ms-1 second round trip starts to feel like a delay callers notice. Hitting that requires streaming at every stage: partial transcription while the caller is still talking, generating a response as early as possible, and streaming TTS audio as it's produced rather than waiting for the full response.
A chatbot with voice input still waits for you to finish speaking, transcribes, and reads a text-shaped answer aloud. A true voice-first agent handles interruption (barge-in), manages turn-taking without explicit push-to-talk cues, and is built around telephony infrastructure (SIP, call routing, DTMF fallback) rather than a browser microphone. The latency and conversation-design requirements are different enough that they're usually separate builds.
In most cases, yes, either as a full replacement or as a smarter front end that still falls back to the existing menu tree for edge cases. The usual approach routes common intents to the conversational agent and keeps DTMF/touch-tone fallback available, so callers who prefer the old menu, or whose speech recognition confidence is low, aren't stuck.
Almost always hosted APIs for ASR and TTS (Deepgram, AssemblyAI, ElevenLabs, or cloud-provider equivalents), because training competitive speech models from scratch isn't a good use of an MVP budget. The engineering work is in pipeline architecture, latency tuning, telephony integration, and conversation design, not training data-hungry audio models.
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From content platforms and AI assistants to analytics dashboards and fintech solutions: see how we've transformed ideas into production-ready MVPs in 2-3 weeks across diverse industries. Each product launched successfully, serving users globally.
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