Service 05
Voice AI
Front-desk voice agents that handle scheduling and support 24/7.
Service 05 · Voice AI
Latency, turn-taking and hand-off are the engineering problem, and they are why most voice deployments fail rather than the speech model. The transcription is tuned per domain, because the words a front desk actually hears are the ones a general model gets wrong.
Who Voice AI is for
03 profiles- 01 / 03
Operations teams whose front desk is the bottleneck: scheduling, intake, after-hours coverage.
- 02 / 03
Clinical and back-office teams whose transcription needs vocabulary the generic models don’t know.
- 03 / 03
Companies whose previous voice deployment failed because of latency, drops, or hand-off gaps.
How Voice AI works
04 steps- Step 01
Workflow and integration map
The real call flow, with the operators
Walk through the actual call flow with the operators who run it today, map the systems the agent has to touch, and decide where humans stay in the loop.
- Step 02
Voice stack build
Transcription, scheduling and CRM, hand-off on low confidence
Realtime transcription, intent and slot handling, scheduling and CRM integrations, hand-off to a human when confidence drops.
- Step 03
Latency and reliability work
The call feels like a call
Tune turn-taking, partial response handling, and fallback behavior so the call feels like a call, not a chatbot read aloud.
- Step 04
Domain tuning
Clinical vocabulary, accents, structured note output
Clinical or back-office vocabulary, accent robustness, and structured note output tuned to whatever the workflow needs the transcript to do downstream.
What you get
04 deliverables- D-01
Production voice agent integrated with scheduling and CRM systems.
- D-02
Domain-tuned transcription with structured output for downstream workflows.
- D-03
Hand-off and escalation paths with full call transcripts and agent rationale.
- D-04
Latency and reliability dashboards covering turn time, drops, and escalation rate.
Where we've shipped this
04 engagementsFrequently asked questions
04 questionsWhat’s the realistic accuracy of a voice agent for clinical scheduling?
For domain-tuned models on a constrained workflow like appointment booking, transcript accuracy in the high 90s is normal once vocabulary, accents, and call quality are handled. Task-completion rate, which is the metric that actually matters, depends on how the agent handles edge cases and handoffs. We measure both and tune against real call recordings, not synthetic benchmarks.
How do you handle handoffs to a human?
The agent is told upfront which situations require a handoff: low confidence, sensitive content, explicit user request, or any scenario flagged in the runbook. When a handoff fires, the call routes to the right human queue with full context attached, including the transcript, what the agent already tried, and why it stopped.
Does voice AI work in noisy environments or with strong accents?
Yes, with caveats. Noise suppression and accent-tolerant ASR are now table-stakes, but the quality you get out of the box varies by provider. We test against your actual call audio early and tune the stack: ASR choice, noise model, prompt phrasing, and confirmation patterns. A demo on clean audio is not the test that matters.
What’s the typical timeline to a live voice agent?
4-6 weeks to a production-grade agent handling a single workflow end to end is typical. The first one to two weeks of that window are spent on call-flow design, persona, and integration with your booking or CRM system. The rest is build, eval, and tuning against real call recordings before going live.
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