Conversations that finish the job.
iLeaf builds voice agents that hold a real phone conversation and complete the task behind it — qualifying a lead, booking a slot, chasing a document. They interrupt gracefully, escalate to a person when they should, and leave a full transcript. In one deployment they absorbed 70% of phone workflows.
Ask Lia about ai voice agentsWhat this includes
- Real telephony
- Inbound and outbound on actual numbers, with IVR replacement, routing and voicemail handling.
- Barge-in
- Callers can interrupt mid-sentence and be understood, which is the difference between a conversation and a recording.
- Task completion
- The agent updates your CRM, books the appointment or sends the follow-up — not just logs a summary.
- Warm escalation
- Hand-off to a human carries the transcript and context, so the caller does not start again.
- Transcripts & audit
- Every call transcribed, stored and searchable, with the actions taken recorded against it.
- Multilingual
- Language matched per caller or region, on the same underlying workflow.
How we run it
Every phase ships something usable on its own, so you are never holding a half-finished system waiting on the next milestone.
Start with the transcripts
We read how these calls actually go today before designing anything. Real calls contain the edge cases a script never does.
One call type first
A single, well-bounded call type goes live — typically outbound follow-up, where the downside of a poor call is lowest.
Always offer a human
A route to a person is available at any point in every call, permanently. It is never buried behind retries.
Tune on recordings
Real recordings drive prompt, latency and voice tuning weekly, because voice quality is judged in milliseconds.
What we build it with
- Twilio
- LiveKit
- Deepgram
- ElevenLabs
- Claude
- Python
- TypeScript
- WebRTC
- Redis
- PostgreSQL
- Temporal
- Governor Engine
Questions we get asked
Will callers know it is not a person?
We tell them, and we recommend you do too. Disclosure is a legal requirement in several of the markets our clients operate in, and in practice it does not hurt completion — callers mind being trapped in a system far more than they mind talking to a competent one that can hand them to a person.
How fast does it respond?
Sub-second response is the target, because voice tolerates far less delay than chat. That constrains model and infrastructure choices, so we design the pipeline for latency first and measure it continuously on real calls rather than in a lab.
Can it handle accents and interruptions?
Interruption handling — barge-in — is built in, so a caller can cut across the agent and be understood. Accent robustness is a function of the speech model and tuning on your own recordings, which is why we start from your existing call audio rather than generic benchmarks.
Let’s talk about ai voice agents.
Tell us what you are running and what it needs to do next. We will tell you honestly whether we are the right team for it.