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Valtair

Voice AI agent development

We build voice systems that hold a useful conversation at a latency people can live with, then hand off cleanly to a person when they should. Real telephony and CRM integration are part of the build, not an afterthought.

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A conversation that can actA phone call passes through speech recognition, the language model with its tools, and text-to-speech, with guardrails, and ends as a booking or CRM update, a call routed to a person, or call analytics.Caller audioSpeech to textModel + toolsText to speechHuman handoffBooking
A phone call passes through speech recognition, the language model with its tools, and text-to-speech, with guardrails, and ends as a booking or CRM update, a call routed to a person, or call analytics.
01

What voice AI agent development involves

A voice agent answers or places phone calls and holds a real conversation: it listens, understands, decides what to do, and speaks back, often while looking up or updating records mid-call. It can take bookings, qualify enquiries, answer routine questions, and route calls, at any hour and at peak volume when staff cannot pick up.

The engineering challenge is time. People expect a reply within moments of finishing a sentence, and every stage of a voice pipeline (telephony, speech recognition, model reasoning, speech synthesis) adds delay. We build streaming pipelines so each stage starts before the previous one finishes, handle interruptions so callers can cut in naturally, and keep heavy reasoning off the critical path. The details are in Designing voice agents for latency people can live with, and the full system is mapped in Voice AI agent architecture.

A useful voice agent also knows its limits. We design the escalation paths before the happy path: when the agent should hand the call to a person, and how it passes on what it has learned so the caller does not repeat themselves. Our Cedarline demonstration build shows the approach, with one agent definition shared across voice, WhatsApp, web chat, and email. We have shipped integrations with ElevenLabs and Fish Audio for speech, Retell AI as a hosted platform, and Twilio for telephony, and choose between them per project.

02

Problems we solve

  • Calls are missed or abandoned at the times they matter most.
  • A voice agent feels slow or robotic and callers give up.
  • The system needs to know when to escalate to a human.
03

What's included

  • Speech recognition
  • Language-model orchestration
  • Text-to-speech
  • Telephony
  • Call routing
  • CRM integration
  • Guardrails
  • Call analytics
04

How we approach it

  1. 01

    Design

    Map the conversation, escalation paths, and guardrails.

  2. 02

    Orchestrate

    Wire speech recognition, model reasoning, and speech synthesis for low latency.

  3. 03

    Integrate

    Connect telephony, routing, and the CRM.

  4. 04

    Measure

    Track resolution, latency, and escalation with call analytics.

05

Decisions that matter

Turn-taking is a design problem

Deciding when a caller has finished speaking is harder than it sounds. Too eager and the agent interrupts; too cautious and it leaves awkward silences. We tune end-of-turn detection for the conversation and support barge-in so callers can interrupt.

Design for the phone line

Telephone audio is narrowband and often noisy, and callers speak in fragments. We test speech recognition on real call audio, confirm names, numbers, and addresses back to the caller, and keep spoken responses short.

Hand off with context

When a call needs a person, the agent transfers it with a summary of who called, what they wanted, and what was already done, and writes the outcome to the CRM. See When a voice agent should hand off to a person.

06

When it is the right fit

A good fit when

  • You miss or abandon calls at peak times, out of hours, or during spikes in demand.
  • A large share of calls are routine: bookings, rescheduling, status checks, qualification, or common questions.
  • Calls need to read from and write to real systems, such as a calendar, scheduling tool, or CRM.

Probably not when

  • Most calls are complex, emotional, or high-stakes conversations that need a person from the first sentence.
  • Call volume is low enough that staff already answer promptly. The integration work may not pay for itself.
07

Platforms we have integrated

  • ElevenLabs

    Text-to-speech for agent voices that sound natural on a phone line, chosen per brand and language, and streamed so callers hear the first words quickly.

  • Fish Audio

    Text-to-speech and voice cloning where a specific voice or language matters, kept behind the same interface so voices can be swapped without touching the agent.

  • Retell AI

    Hosted voice-agent platform for getting a use case live quickly, with our own tools, guardrails, and integrations wired in behind it.

  • Twilio

    Telephony: phone numbers, SIP trunking, media streaming into the agent, and warm transfer to staff when a call needs a person.

We stay provider-agnostic: each platform sits behind an interface, so it is chosen per project and can be changed without rebuilding the system around it.

08

Representative technologies

ElevenLabsFish AudioRetell AITwilioSpeech-to-textText-to-speechSIP and WebRTCStreaming model APIsCRM APIsCall analytics
09

Voice AI FAQs

Can it connect to our existing phone system?

Yes. We integrate with real telephony and route calls into your existing stack and CRM.

What happens when the agent cannot help?

It escalates to a person with context, so the caller does not have to start over.

How do you handle latency?

We stream recognition, reasoning, and synthesis and design the pipeline so the caller is not left waiting.

Can the agent make outbound calls as well as answer them?

Yes. The same pipeline supports outbound calls such as reminders, follow-ups, and confirmations, with consent tracking and calling-hours limits designed in.

Which voice AI platforms have you integrated?

We have shipped integrations with ElevenLabs and Fish Audio for speech synthesis, Retell AI as a hosted voice-agent platform, and Twilio for telephony. We stay provider-agnostic: each is chosen per project for voice quality, latency, language, and cost, and kept behind an interface so it can be changed later.

Should we use a platform like Retell AI or a custom pipeline?

A hosted platform is the fastest way to get a use case live and prove it. A custom pipeline gives more control over latency, turn-taking, and integrations, and usually costs less at high call volume. We build both, and often start on a platform and move the parts that matter in-house later. The trade-offs are set out in Voice AI agent architecture.

Will callers know they are speaking to an AI?

We recommend the agent says so at the start of the call, and in some jurisdictions disclosure is required. Being upfront also sets the right expectations for the conversation.

Can voice agents speak Arabic?

Yes. Our voice agents speak and understand Arabic as well as English, hold full conversations in Arabic, and take actions such as bookings in Arabic.

How do you test a voice agent before it takes real calls?

With simulated calls built from real scenarios, scored on task completion, correctness, and latency, then a controlled rollout on live traffic. We describe the method in How to evaluate a voice agent before it takes real calls.
Proof
Industry

Healthcare

AI voice and chat that turn missed calls into booked appointments for US medical, dental, chiropractic, and physical therapy practices.

See healthcare solutions
Industry

Real estate

Voice and chat agents, viewing booking, CRM, and lead automation for portals, developers, agents, and rental and leasing businesses.

See real estate solutions
Industry

Home services

Voice and chat agents, job booking, and follow-up automation for trades and home services businesses.

See home services solutions
Live demo

Cedarline Home Services

Multi-channel AI agent suite

See the demo
Illustrative case study

An inbound voice agent for high-volume scheduling

Operations

Read case study

Further reading

Cascaded or speech-to-speechSeparate speech-to-text, language model and text-to-speech stages with a transcript are compared with one speech-to-speech model. Both carry call audio over telephony.Cascaded pipelineSpeech to textTranscriptModelText to speechSpeech-to-speechTelephony
Article7 min read

Voice AI agent architecture: from phone line to response

The layers of a production voice agent, what each one does, and how to choose between a cascaded pipeline and speech-to-speech.

Voice and conversational AI
Voice / human handoffAn editorial illustration of a conversation branching to a human, with its context carried along.ConversationTool executionYour teamContext
Article4 min read

When a voice agent should hand off to a person

The signals that a call needs a person, how to transfer context with the call, and how to measure the balance.

Voice and conversational AI
Voice / evaluationAn editorial illustration of call scenarios being tested, reviewed, and fed back into evaluation.Call scenariosSimulateHuman review
Article4 min read

How to evaluate a voice agent before it takes real calls

What to measure, how to build and simulate call scenarios, and how to roll a voice agent out safely.

Voice and conversational AI

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