Voice AI Development

Voice agents and speech features that handle real conversations, not just scripted prompts.

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In short

Voice AI development is building applications that understand and respond to spoken language — voice agents for customer support, IVR replacement, transcription, and hands-free workflows — combining speech-to-text, an LLM for understanding and response, and text-to-speech, often in real time. DuCodes builds these pipelines with the same escalation and fallback discipline we apply to text-based AI features.

Voice adds real constraints text-based AI doesn't have: responses need to happen in near real time, speech recognition needs to handle accents and background noise, and a voice agent that gets confused mid-call needs a graceful way to hand off to a human, not a dead silence. We build voice AI features — call-handling agents, voice-driven internal tools, transcription and summarization pipelines — around those constraints specifically.

This typically combines a speech-to-text layer, an LLM handling understanding and response generation, and text-to-speech for the reply, wired together with the latency budget a real conversation actually requires.

Why work with DuCodes on this

Designed for real-time constraints

Latency budgets for a natural-feeling conversation are part of the architecture decision from the start, not discovered after the demo feels sluggish.

Graceful human handoff

A voice agent that's uncertain or stuck gets a defined path to transfer to a person, the same discipline we apply to chat-based agents.

Built for real speech, not clean demo audio

Accents, background noise, and interruptions get accounted for in testing, not just clear studio-quality sample audio.

Integrated with your existing phone/support stack

Voice agents connect into your actual telephony or support system rather than existing as a standalone demo.

How we work

1

Scope the conversation

What the voice agent should handle end-to-end, and where it needs to hand off to a human.

2

Choose the speech stack

Speech-to-text and text-to-speech providers selected for your language, accent, and latency requirements.

3

Build the conversation logic

LLM-driven understanding and response, tuned against real (or realistic) call transcripts.

4

Test with real audio conditions

Background noise, accents, and interruptions tested explicitly, not just clean sample recordings.

5

Deploy & monitor call quality

Ongoing review of real call transcripts to catch misunderstandings and tune the system after launch.

Technology we use

Speech-to-text (Whisper, Deepgram) Text-to-speech (ElevenLabs, Google/Azure TTS) OpenAI / Anthropic / Gemini APIs Telephony integration (Twilio) Python / Node.js

Frequently asked questions

For some call types, yes; for others, it should triage and hand off. We scope this per call type during discovery rather than assuming full replacement is the goal.

We test explicitly against realistic audio conditions, not just clean sample recordings, and choose speech-to-text providers accordingly.

It's designed to recognize low confidence and hand off to a human agent rather than guessing or looping the caller through repeated prompts.

Yes, typically through a telephony provider like Twilio connecting to your existing number and call routing.

Ready to talk about your project?

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