Inbound AI Receptionist System, Recall Automation, Analytics Dashboard
Industry: Dental (U.S. multi-location)
Timeframe measured: 90 days (modeled from baseline tracking)

Client overview

Bright Smile is a U.S. multi-location dental network with multiple hygienists per site focused onpreventive hygiene, cosmetic treatments, and bringing recall patients back consistently.

Challenges (before)

Bright Smile had strong inbound demand but performance was capped by operationalbottlenecks:

• Calls missed during peak hours and lunch overlaps
• Manual recall and follow-up creating staff overload
• Inactive and treatment-pending patients not re-engaged
• Limited visibility into scheduling performance by location and provider

The result was lost appointments, hygiene gaps, and avoidable admin work.

What we implemented

A streamlined front-desk and recall system built for multi-hygienist U.S. workflows:

1. Inbound AI Receptionist System (overflow + after-hours coverage)
2. Outbound Recall and Reactivation System (hygiene, no-shows, pending treatment)
3. Custom Analytics Dashboard (coverage, booking, fill-rate, recovery)

Inbound AI Receptionist System

Acts as an always-available receptionist layer alongside the front desk:

• Answers overflow calls instantly
• Schedules and reschedules appointments
• Identifies visit type (hygiene, exam, consult)
• Sends confirmations and reminders
• Routes complex treatment cases to staff

Call handling rule: If the reception team cannot answer, the system takes the call. If a calldrops, a callback is triggered within 45 seconds.

Outbound recall and reactivation

Targets and re-engages:

• Hygiene recall cycles
• Cancelled and no-show patients
• Cosmetic and whitening inquiries
• Inactive and former patients

It offers real availability, confirms intent, and books when the patient is ready.

Custom analytics dashboard

Live reporting on:

• Call coverage and overflow handling
• After-hours booking activity
• Recall and reactivation outcomes
• Hygienist chair fill-rate trends
• Schedule recovery after cancellations

Results (90-day modeled outcomes)

Data metrics for graphs (exact values):

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Outcome summary

• Every inbound opportunity is answered or recovered
• Hygiene schedules stabilize near full capacity
• After-hours bookings become a consistent channel
• Recall becomes measurable and repeatable
• Front desk workload drops without sacrificing patient experience

“Our inbound demand was finally met. We tried 2 other agenices before Gloura”