Two months of live data from the UK’s first AI Front Desk beta reveal what patients actually want, when they want it, and what the sector has been missing.
“I don’t think we realised we had an out-of-hours booking problem. We would never have had the data to show that.”
That’s Rianna Thompson, Head of Sales and Marketing at Damira Dental Studios, describing one of the most striking findings from two months of live beta testing with Boxly’s AI Front Desk. Damira tracked their call volumes meticulously. They knew Monday mornings were the busiest. They knew overflow calls were high. But what they couldn’t see was the demand that existed outside office hours, online, where there was nothing to capture it.
In January 2026, Damira became the first dental group to beta test AI Front Desk at scale, across all 44 of their practices. In the first month, it handled 6,801 patient conversations. Roughly half of those happened outside office hours. Not casual browsers. Patients who booked, paid deposits, joined waitlists, and rescheduled appointments, all without speaking to a person, at times when no person was available.
That 50% figure held steady into month two. It wasn’t a spike. It was the norm.
Patients searching for a dentist at 10pm, wanting to rebook at 6am, checking NHS availability on a Sunday morning. None of that showed up in any report.
“We monitored calls closely,” says Rianna, “but the online demand was completely invisible to us. We haven’t advertised anywhere else, it’s just been beta testing. Where we can add this to our out-of-hours phone number, our website, this is only the start of what AI Front Desk can deliver.”
Without a system capturing that demand, the data doesn’t exist. The patient who tried to book at 9pm and gave up is a ghost. The emergency that came in at midnight and went to a competitor by morning leaves no trace.
Nearly three quarters of the callback requests coming through AI Front Desk were clinically urgent: patients in pain, dental emergencies, trauma, post-treatment complications. Adults in pain made up the largest single category at 35% of all callbacks, followed by emergencies at 12% and dental trauma at 11%. When patients couldn’t book directly, the reason they wanted a callback was almost always clinical.
The bookings tell their own story. Of the enquiries AI Front Desk generated, 37% resulted in a booked appointment, with £10,500 in deposits collected. NHS and private bookings split roughly evenly. All of it organic, with zero marketing and no active promotion to patients.
“Who knows whether we would have been able to take that if this wasn’t in place,” says Rianna, “whether we’d be able to reach all of those calls, or whether those patients would have followed through with booking.”
Tom Simons, Damira’s Operations Director, was struck by how willing patients were to book online for what is, ultimately, serious medical treatment. That confidence extended across all age groups, including older patients, which challenges the assumption that online self- service is just for the young.
As Rianna put it: “There are some people that really do want to speak to a human, and that connection matters. And then there’s the other camp of people that would prefer not to speak to anybody, would like to self-serve, book things in their own time.”
AI Front Desk serves the second group, and in doing so, frees up the contact centre to give more time to the first. The patients who prefer to sort themselves out on a Tuesday evening can now do exactly that. And the patients who need to speak to a person get a team with more time to listen.
Nikaisha Anane-Busia, Damira’s Contact Centre Manager, noticed it every morning: “We have a better understanding of what our diaries look like as soon as we get in in the morning because patients have been able to book themselves in the night before.”
The numbers backed her up. By March, the conversion rate had jumped from 25.3% to 35%. Total sessions rose to over 7,000.
Callback form volumes dropped. Patients who previously submitted a form and waited for a call the next day were now booking online. Nikaisha’s team were calling patients back far less from this group, because patients were getting themselves sorted as and when they wanted to.
Damira’s original brief was straightforward: reduce call volume and handle routine queries more efficiently. The beta delivered on both. But the data revealed something nobody had been looking for.
Half of patient engagement was happening when the practice was closed. The patients reaching out at midnight were in clinical need, not browsing. The willingness to self-serve crossed every age group. None of it showed up in any report until there was a system to capture it.
The demand was always there. The data wasn’t.
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