AI Voice Agents
AI That Can Listen, Reason and Act.
Voice agents that hold a natural conversation, retrieve real account information mid-call, complete the task in your systems, and hand to a person the moment the call needs one.
What this covers
- Inbound call handling
- Outbound calling
- Live system access
- Task completion
- Integrates with
- 9 system types
- Built on
- Deepgram · ElevenLabs
What it is
AI Voice Agents, in plain language
A voice agent is an AI agent with a telephony front end. Speech becomes text, the agent reasons and acts exactly as it would over any other channel, and the response is spoken back.
What makes voice hard is not the speech. It is latency and interruption. A pause that reads as normal in chat reads as a broken line on a phone call, so the pipeline has to retrieve, reason and begin speaking inside a window measured in hundreds of milliseconds — and it has to handle being talked over.
The other hard part is knowing when to stop. A voice agent that will not transfer is worse than an IVR, because the caller has been led to expect more.
Capabilities
What the system does
Inbound call handling
Answers, identifies the caller, and completes routine requests without a queue
Outbound calling
Confirmations, reminders, follow-ups and qualification, with consent and opt-out handling
Live system access
Retrieves account, order and appointment data during the call
Task completion
Books, reschedules, updates records and triggers workflows mid-conversation
Warm transfer
Hands to a person with a spoken summary and the transcript already in the CRM
Call summaries and analytics
Every call transcribed, summarised, and filed against the right record
How it works
The architecture, not the pitch
Incoming call → speech recognition → intent → agent reasoning → knowledge retrieval → business action → spoken response, with escalation available at any step.
- InputIncoming call
- SystemSpeech recognition
- DecisionIntent
- ReasoningAgent reasoning
- RetrievalKnowledge retrieval
- ActionBusiness action
- ActionSpoken response
- Human escalationWarm transferAvailable at any step
The hard parts
Why voice is harder than the same agent in chat
The reasoning is identical. The constraint is a person on a phone line who will not wait, and who will talk over you.
- 01
The budget is time to first sound
Total response time is the wrong number. What a caller experiences is the silence before speech begins, and transcription, retrieval, reasoning and synthesis all spend from one window measured in hundreds of milliseconds — so the architecture is chosen around that budget rather than measured against it afterwards.
- 02
Barge-in has to stop the agent mid-word
People interrupt. Handling it means detecting speech while audio is still playing, cutting synthesis immediately, discarding the abandoned turn and reconciling what the caller actually heard — because an agent that talks over someone reads as a machine however good the voice is.
- 03
Transcription error is the input, not an edge case
Accents, background noise, spelled-out reference numbers and names all arrive imperfect. Anything consequential is read back for confirmation, and the recogniser is biased toward the vocabulary of your business rather than left general.
- 04
Endpointing decides whether the agent seems rude
Telling the difference between a caller who has finished and a caller who is thinking is a tuned judgement, not a default. Too eager and the agent interrupts; too patient and the pause reads as a dropped line.
- 05
A transfer that is not warm is worse than an IVR
Handing over means passing the person, the transcript, the identified intent and whatever was already retrieved to whoever picks up. A cold transfer that makes the caller start again spends exactly the goodwill the automated part just earned.
- 06
Recording and consent are jurisdictional
Whether a call may be recorded, what has to be announced, and where audio may be stored differ by territory and are configured per deployment. They are settled in discovery, because retrofitting a consent announcement changes the shape of the call.
Use cases
From trigger to business outcome
Every row reads the same way, because every system does: something happens, the model interprets it, an action lands in a real system, and the business result follows.
| Use case | Trigger | AI reasoning | Action | Result |
|---|---|---|---|---|
| Appointment scheduling | A caller asks to book or move an appointment | Checks live availability against the caller’s history and constraints | Books it, sends confirmation, updates the calendar and CRM | Scheduling stops competing with in-person work |
| After-hours coverage | A call arrives outside business hours | Determines whether the request is routine or urgent | Resolves it, or takes structured details and escalates by the on-call path | Calls stop going to voicemail |
| Order and delivery status | A caller asks where something is | Looks up the order, interprets the current status | Explains it, and offers the next action — reschedule, redirect, refund request | Status calls stop consuming the support queue |
| Outbound qualification | A new lead needs a first conversation | Works through your qualification criteria conversationally | Books the meeting or disqualifies, writing notes to the CRM either way | Reps spend their time on qualified conversations |
Appointment scheduling
- Trigger
- A caller asks to book or move an appointment
- AI reasoning
- Checks live availability against the caller’s history and constraints
- Action
- Books it, sends confirmation, updates the calendar and CRM
- Result
- Scheduling stops competing with in-person work
After-hours coverage
- Trigger
- A call arrives outside business hours
- AI reasoning
- Determines whether the request is routine or urgent
- Action
- Resolves it, or takes structured details and escalates by the on-call path
- Result
- Calls stop going to voicemail
Order and delivery status
- Trigger
- A caller asks where something is
- AI reasoning
- Looks up the order, interprets the current status
- Action
- Explains it, and offers the next action — reschedule, redirect, refund request
- Result
- Status calls stop consuming the support queue
Outbound qualification
- Trigger
- A new lead needs a first conversation
- AI reasoning
- Works through your qualification criteria conversationally
- Action
- Books the meeting or disqualifies, writing notes to the CRM either way
- Result
- Reps spend their time on qualified conversations
Integrations
Built around the systems you already run
- Twilio
- SIP trunking
- Salesforce
- HubSpot
- Zendesk
- Google Calendar
- Outlook Calendar
- Scheduling platforms
- Custom APIs
Technology
What this is built with
- Deepgram
- ElevenLabs
- OpenAI
- Anthropic
- Twilio
- WebRTC
- Python
- FastAPI
- Redis
- PostgreSQL
How we work
Five phases, each with a decision point
- InputDiscover
- ReasoningArchitect
- DecisionPrototype
- SystemProduction
- ActionOptimize
FAQ
Questions we are actually asked
Will callers know they’re talking to an AI?
Yes — the agent identifies itself. Beyond being required in several jurisdictions, callers who are told tend to be more direct, which makes the conversation work better.
What happens if the agent can’t handle the call?
It transfers, with a spoken handover summary to the person receiving it and the transcript written to your CRM. Escalation is available at every step, including on explicit request.
How natural does it actually sound?
Good enough that the conversation flows, including interruptions and corrections. It is not indistinguishable from a person and we would not present it as such.
Can it work with our existing phone system?
Usually. We integrate over SIP or via Twilio. Existing numbers can be ported or forwarded.
What about accents and background noise?
Modern speech recognition handles both well, though accuracy varies by language and audio quality. We test against recordings of your actual calls before committing to a design.
Are calls recorded?
Only with the disclosure and consent your jurisdiction requires. Retention is configurable and specified before launch.
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