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AI Voice Receptionist Handling 4,200 Calls Monthly

Deployed multi-language voice AI handling appointment bookings, insurance verification, and triage.

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Healthcare8 WEEKSVERIFIED RESULTSMulti-clinic healthcare group

AI Voice Receptionist Handling 4,200 Calls Monthly

Deployed multi-language voice AI handling appointment bookings, insurance verification, and triage.

Founder note: this is written to be practical. We focus on what we learned in delivery, what changed for the team, and what results held up after rollout.

INDUSTRYHealthcare
CLIENT TYPEMulti-clinic healthcare group
TIMELINE8 weeks
DELIVERY SCOPEVoice AI, EHR scheduling, 4,200 bookings/month
domain

The Real Problem

1. Peak hours (8-11am) created 60+ call backlog

2. 40% of calls were routine questions (hours, insurance, location)

3. After-hours appointments couldn't be booked

4. Manual appointment entry had 7% error rate

5. No initial symptom assessment before talking to doctor

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What We Actually Changed

Deployed Vapi-powered voice receptionist:

- Bilingual (English/Spanish) conversational AI

- Real-time appointment calendar integration

- Insurance verification system

- Symptom triage questionnaire

- Direct routing to appropriate provider

- 24/7 call availability

- Integration with patient management system (Athena)

- Natural voice synthesis indistinguishable from human

TOOLS AND METHODS USED

VapiOpenAI GPT-4TwilioNode.jsPostgreSQLAthena EHR APIStripe (payment processing)
layers

Delivery Journey

PHASE 1

Voice Training

Recorded clinic staff, trained AI voice model

PHASE 2

Workflow Design

Mapped call flows, appointment logic, triage questions

PHASE 3

Integration

Connected to patient system, calendars, insurance lookup

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What Improved

The voice AI sounds so natural that patients are amazed when we tell them it's AI. It handles our volume perfectly and gives us better data on incoming patients. This is the future of healthcare reception.

4,200+
Calls/Mo
91%
AI Resolved
+43%
NPS
AI handles 4,200+ calls monthly, 24/7
91% of calls resolved by AI without human transfer
Reduced errors from 7% to 0.2% with automation
Handles bookings and triage 24/7 automatically
Staff can focus on patient care, not phones
NPS increased from 61 to 87 post-implementation
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FOUNDER BRIEF

INDUSTRY
Healthcare
TIMELINE
8 weeks
CLIENT TYPE
Multi-clinic healthcare group
terminal

DELIVERY STACK

VapiOpenAI GPT-4TwilioNode.jsPostgreSQLAthena EHR APIStripe (payment processing)

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