SERVICE
AI Voice Agent Development
Phone and web voice agents that sound human and never sleep.
concurrent calls in production
Reviewed by Mohammed Affaan Khan, GenAI & Agentic AI Engineer · Updated July 2026
How much does voice agents cost? See what drives the price — and get a quote scoped to your budget.→What voice agents actually means
An AI voice agent is software that holds a real spoken conversation over a phone line or a web session: it listens, decides, and answers in a natural voice, without a person on the line. It is not an IVR with better recordings — there is no menu tree, and the caller can interrupt, change subject, or ask something the script never anticipated.
The engineering problem is time. A human conversation tolerates about half a second of silence before it starts to feel broken; past roughly 800 milliseconds callers begin talking over the agent and the exchange collapses. Everything below — model choice, hosting region, how audio is streamed — is downstream of that single constraint.
What you get
- Inbound & outbound call automation
- Sub-500ms STT→LLM→TTS pipelines
- Multi-turn memory and role switching
- CRM, calendar and escalation workflows
How we build it
Speech to text, streaming
Audio is transcribed as it arrives rather than after the caller stops talking. Waiting for a complete utterance costs a full turn of latency before any thinking has started.
Intent and state
The transcript is resolved against conversation state — who is calling, what has already been agreed, which step of the task they are on. This is what separates a multi-turn agent from a question-answering toy.
Grounded reasoning
The model decides the next action against your business rules, calendar and records. Where it needs a fact it retrieves one rather than recalling it, for the same reason a RAG chatbot does.
Text to speech, streamed back
The reply is synthesised and streamed in chunks so the first syllable reaches the caller while the sentence is still being generated.
Post-call automation
CRM write-back, appointment creation, follow-up messaging and escalation routing. This is where most of the measurable return actually comes from.
The stack
TELEPHONY
SIP trunking and programmable voice, with carrier failover. Number provisioning and call recording handled per jurisdiction.
SPEECH
Streaming STT with domain vocabulary tuning; neural TTS with optional branded or cloned voices where licensing allows.
REASONING
LLM orchestration with tool calling, retrieval for grounding, and explicit guardrails on what the agent may commit to.
STATE
Per-call memory plus durable conversation history, so a caller who rings back is not starting from nothing.
INFRASTRUCTURE
Kubernetes with autoscaling sized for peak concurrency rather than average — the Monday-morning spike is the design case, not the exception.
OBSERVABILITY
Per-turn latency traces, containment rate, escalation reasons and full transcripts. You cannot improve what you are not recording.
What it connects to
- Salesforce, HubSpot and custom CRMs
- Google Calendar, Outlook and practice-management scheduling
- Twilio, Vonage and SIP carriers
- Zendesk, Freshdesk and ticketing systems
- Payment and verification providers where a call takes a transaction
- Internal APIs over REST or GraphQL
Where teams use it
Healthcare
Appointment booking, rescheduling and reminder calls, with anything clinical escalated to a human rather than answered.
Restaurants
Taking phone orders and reservations through a Friday rush, when nobody is free to pick up.
Real estate
Qualifying inbound enquiries within seconds of the call, because the agent who answers first usually wins the instruction.
Logistics
Shipment status and proof-of-delivery calls that otherwise consume an operations desk.
Financial services
Tier-one queries and document chasing, with a complete audit trail of what was said and decided.
Hospitality
Multilingual booking and in-stay requests across time zones, where the gap in coverage is what ends up in the review.
How a build runs
Scoping
1 week
One call type chosen, success measured in containment rate and caller satisfaction rather than vibes.
Prototype
2–3 weeks
A working agent on a real number, handling the happy path and escalating everything else.
Integration
2–4 weeks
CRM, calendar and escalation wired; latency tuned against the 500ms budget.
Pilot
2 weeks
Live on a slice of real traffic, with transcripts reviewed daily and prompts corrected against them.
Scale
ongoing
Additional call types, languages and concurrency headroom as the numbers justify them.
When this is the wrong answer
If your call volume is low and your team already answers promptly, this will not pay for itself. The economics come from volume or from hours you cannot staff.
Telephony is usually the cost nobody budgets for. At scale, per-minute carrier and speech charges routinely exceed the model tokens — which is why we architect for short, efficient calls rather than impressive ones.
Accents, hold music, poor lines and cross-talk are real degradations. We measure word error rate on your actual recordings before promising anything about them.
Anything with legal, clinical or financial consequence should escalate rather than resolve. An agent that confidently gets that class of question wrong costs more than the whole system saves.
Proof
Voice platform sustaining 10,000+ concurrent calls
End-to-end real-time voice intelligence platform with agentic decision loops, post-call automation and Kubernetes auto-scaling.
Frequently asked questions
How fast can an AI voice agent respond?
Our production pipelines respond in under 500 milliseconds end-to-end — fast enough that callers speak naturally, without the awkward pauses of typical bots.
Can the agent book appointments and update our CRM?
Yes. We wire post-call automation for CRM updates, appointment scheduling and human escalation, eliminating manual work on routine interactions.
How many calls can it handle at once?
The same architecture we build for clients sustains 10,000+ concurrent calls with a 99.9% uptime SLA, auto-scaling through traffic spikes.
Will callers know they are talking to AI?
Most will work it out, and the systems that perform best do not pretend otherwise. Several jurisdictions now require disclosure outright. In practice a brief, matter-of-fact statement at the start costs nothing and removes the risk of a caller feeling deceived when they realise mid-conversation.
What happens if the caller wants a human?
They get one. Escalation is a first-class path rather than a fallback — the agent recognises the request, transfers with the conversation context attached, and does not make the caller repeat themselves. An agent that traps people is worse for the business than no agent.
How does it handle accents and background noise?
We measure word error rate on your own recordings before promising anything, because performance varies enormously by accent, line quality and environment. If the numbers on your real calls are poor, you will hear that during scoping rather than after deployment.
Can it handle multiple languages?
Yes, with the caveat that quality differs by language and by voice. We test the specific languages your callers use rather than relying on a vendor's general claim.
What does it integrate with?
CRMs, calendars, ticketing and telephony — Salesforce, HubSpot, Google Calendar, Outlook, Twilio, Zendesk and custom internal APIs. Post-call automation is normally where most of the measurable return comes from, so integration is scoped as core rather than optional.
Is it legal to use AI voice agents for customer calls?
Generally yes, subject to disclosure and call-recording rules that vary by jurisdiction and sometimes by state or province. We build to the standard your legal team sets. We are engineers rather than your counsel, and we will say so rather than offer an opinion we are not qualified to give.
What happens when it gets something wrong?
That is a design question answered before the build: what the system does when it is unsure, what it escalates, and who sees it. Every deployment has a confidence threshold, a human path and logging that shows why a given answer was produced. A system with no defined failure behaviour is not finished.
What happens to our data?
It stays in infrastructure you control or a region you nominate. We do not train shared models on your data, and where a third-party model provider is involved we tell you which, what it receives, and what its retention terms are — before anything is sent.
Who owns the code and the models?
You do, from the first commit. Work happens in your repository under your licence and the contract assigns IP outright. We keep no rights, hold no keys you cannot rotate, and build nothing proprietary that makes leaving expensive.
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Before you choose anyone
Written to be useful whether or not you hire us — including the parts that argue against hiring an agency at all.
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