Per-location numbers
Each restaurant has its own Twilio number and localised knowledge base.
Case StudyRestaurantsAI Voice Agent
AI Voice Agent for a Multi-Location Restaurant Group
Replacing the ringing landline during dinner rush with an AI Voice Agent that takes reservations, answers hours and menu questions, and hands off cleanly.
- Client
- Three-location restaurant group (anonymized)
- Region
- Europe
- Published
- May 8, 2026
Quick Summary
A three-location restaurant group was drowning in phone calls during dinner service — reservations, hours, and menu questions all interrupted floor staff. GetLeadExpo built a Twilio + OpenAI voice agent that answers calls per location, takes reservations, and only escalates to a human when needed. Staff stay on the floor, and callers get faster, more consistent service.
Client challenge
What the team was struggling with
Phones rang non-stop during dinner service, pulling staff away from tables.
Reservations were sometimes lost between paper notes and the online booking tool.
Callers got different answers about menu, hours, and dietary options depending on who picked up.
Late-evening voicemails went unanswered until the next morning.
Our solution
How GetLeadExpo solved it
We built a per-location voice agent on Twilio + OpenAI. It answers calls, takes reservations directly into the booking system, and answers the top FAQs (hours, menu highlights, allergens, dress code). When a caller genuinely needs a human — private events, complaints — it warm-transfers with context.
How It Works
Implementation process
Every step we walked through — from discovery to production.
- 01
Menu & policy intake
Structured each location's hours, menu, allergens, and reservation rules into a shared knowledge base.
- 02
Voice agent build
Trained the agent on tone, brand voice, and per-location differences with strong guardrails.
- 03
Twilio routing
Set per-location Twilio numbers so each restaurant has its own dedicated voice line.
- 04
Reservation flow
Connected reservations directly into the booking tool so bookings appear in real time.
- 05
Handoff rules
Defined exactly when to warm-transfer to a human — private events, complaints, VIPs.
Tools used
The stack we shipped
Twilio
Voice, SMS, and telephony routing.
OpenAI
GPT-based reasoning, drafting, and classification.
n8n
Workflow automation and orchestration.
Google Sheets
Lightweight data store and reporting layer.
Key Features
Key features shipped
Everything included in this deployment.
Live reservations
Reservations are written straight to the booking tool with no manual re-entry.
Consistent answers
Every caller gets the same on-brand response for hours, menu, and policies.
Always answered
Late-evening and pre-open calls are handled — no more missed voicemails.
Warm human handoff
Private events and complaints go straight to a manager with context attached.
Call transcripts
Every call is transcribed so operators can review tone and coach the agent weekly.
Expected Results
Expected results
Qualitative outcomes observed after go-live.
Faster inquiry handling
Callers get answers immediately instead of waiting during dinner service.
Reduced repetitive manual work
Staff stop repeating the same three FAQs a hundred times a night.
Improved response consistency
Every location answers hours, menu, and policy the same way.
Better lead organization
All reservations and event enquiries land in one system with transcripts.
Lessons learned
What we'd tell the next team
Voice agents need per-location knowledge — one shared script produces confusing answers.
Warm transfers are critical: cold hangs kill trust for VIPs and event enquiries.
Weekly transcript reviews caught tone drift early and kept the agent on-brand.
Reservation flow should always write to the booking system, never to a spreadsheet in production.
Related services
Services behind this case study
Related industry
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FAQ
FAQ — AI Voice Agent for Restaurants
The most common questions we hear about this build.
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