Full case study • Hospitality
L’Ancien Four à Pizza
I owned the client journey from account acquisition through launch and enablement, then designed a bilingual reservation ecosystem connecting AI-assisted intake, guest booking, physical-table logic, staff operations, CRM automation and reputation workflows.
Live build
Public demonstration links are provided for portfolio review. The dashboard contains sample data and is not a live client operating environment.
01 / Project overview
The restaurant needed one reliable workflow to accept reservations, enforce booking and table rules, keep staff informed and maintain a usable view of service activity. Without that structure, booking details, customer records and follow-up could be fragmented across calls, messages and manual notes.
Website, staff, Conversation AI and Voice AI reservations all create the same operational record. Shared rules, source tracking and lifecycle communication allow the restaurant to manage demand consistently regardless of where the request begins.
Bilingual text and phone journeys collect the same required reservation details and share availability and handoff rules.
A branded booking page and staff dashboard provide different interfaces around one operational source of truth.
GoHighLevel data and workflows coordinate confirmations, staff alerts, reminders, cancellation, completion and reputation follow-up.
02 / System architecture
Website, staff, Conversation AI and Voice AI capture structured reservation intent.
One Worker applies availability, table and lifecycle rules across every booking source.
D1 stores guest, time, party, table, source and status in one consistent model.
CRM updates, SMS, staff alerts and review follow-up keep the experience moving.
03 / Implementation detail
The implementation standardizes reservation data, physical-table rules, source tracking and follow-up so staff do not have to reconcile separate versions of the customer journey.
AI channels gather required fields before creating a reservation and preserve context when a person needs to take over.
Custom fields and lifecycle data make each contact useful for automation, service and troubleshooting.
The early virtual-slot model did not match the way staff worked with real tables.
The system was documented as a repeatable white-label pattern without blending client data or credentials.
04 / Account ownership
My responsibility extended across the full customer lifecycle: acquiring the account, uncovering the problem, mapping the solution, onboarding the client, implementing the system and making it easy to use.
Won the client relationship, led discovery and translated business goals, guest pain points and restaurant constraints into a clear implementation scope.
Documented guest and staff journeys, booking rules, data requirements and exceptions; gathered inputs and aligned the client on the future workflow.
Configured, built, tested and launched the solution, then simplified the experience and created repeatable handoffs for sustained adoption.
Launch validation
Adoption & customer value
Bilingual guided flows and consistent communication make reservations easier for guests and staff.
Accurate table logic, one guest record and targeted follow-up support more consistent service.
Enablement, documentation and practical workflows were designed to encourage use and reduce churn risk.
Systems & tools
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