Dental Practices, AI Patient Recall & Hygiene Reactivation System

Independent dental practices with an established patient base (typically 1,500+ active patients), currently working their overdue-recall list manually or not at all. Find them via Google Maps searches for 'dentist' plus a city name, dental practice management Facebook groups and forums, and practices already spending $150-400 per new patient acquired through ads (a strong buying signal - they're already paying a lot to bring in new patients while an overdue list of existing patients sits mostly unworked, which is a much cheaper source of production).

The practice's overdue-recall list gets segmented into dormancy bands (6-12, 12-18, 18-24, and 24+ months overdue), since reactivation likelihood and the right message tone both shift sharply by band. An AI-personalized message goes out automatically for each patient matched to their band: routine and light for the freshest gap, genuinely re-engaging and honest about the gap for longer dormancy, sent across SMS and email with a direct one-tap booking link that requires no login. The system checks real booking status before every scheduled follow-up so a patient who's already rebooked through another channel never gets a redundant message, and respects opt-outs immediately.

The practice dashboard tracks actual recall rate over time, broken out by band, turning a vague sense of 'we should call the overdue list' into a real, trackable number.

The dormancy-band logic and scheduling are reliable from day one; message tone is what benefits from the 2-week supervised pilot, since the honest re-engagement tone needed for an 18-24 month gap reads very differently from the light reminder appropriate for 6-12 months, and getting that wrong in either direction hurts response rates. Expect the freshest band (6-12 months) to convert fastest and most predictably, matching the 25-35% industry reactivation rate for that window, with longer-dormant bands converting at a genuinely lower but still real rate worth capturing at scale. Practices running this report finally having an actual recall-rate number to manage against instead of a vague sense that the list exists.