A dental AI receptionist helped Image Orthodontics extend call coverage across overflow and after-hours inquiries. Newo’s Q1 2025 case study reports call volume and attributed revenue, but the figures need to be read with their original scope.
Image Orthodontics is a multi-location practice in the Bay Area led by Dr. Yan Kalika. Calls include prospective patients looking for treatment, existing patients with appointment requests, and people seeking practice information. Overflow and after-hours coverage address times when those callers cannot reach the front desk.
Coverage across the reported deployment
Image Orthodontics deployed Newo to support call coverage. For Q1 2025, Newo reports 10,865 calls handled, including 1,563 after-hours inquiries, and $401,500 in additional new-patient revenue attributed to the deployment.
The Image Orthodontics case study provides the original reported results and customer quotation. These are Newo-reported outcomes from a named deployment. The after-hours inquiries form part of the coverage story; the full revenue figure should retain the broader deployment scope.
What the dental AI receptionist results show
Read the figures separately before using them to evaluate another practice.
Item |
What to record or do |
Check or owner |
|---|---|---|
10,865 calls |
Total calls reported for Q1 2025. |
Do not relabel as unique patients or completed appointments. |
1,563 inquiries |
After-hours inquiries within the reported call coverage. |
Do not attribute all deployment revenue to this subset. |
54 new patients |
After-hours outcome reported in Newo’s dental case material. |
Do not assume a shared, deduplicated funnel denominator without definitions. |
$401,500 |
Additional new-patient revenue attributed to the deployment. |
Revenue is not profit or a calculated return on investment. |
Newo’s dental case material reports the 54-patient after-hours outcome. Calls, inquiries, patients, and revenue answer different questions, so putting them in a single conversion funnel would require aligned definitions and cohorts.
What the case says about staffing
Newo reports that this deployment did not require added front-desk or call-center headcount. The published Image Orthodontics case does not establish that the practice tripled its location count during the period.
The human team remains part of the operating model. Staff handle requests requiring judgment and follow up where an automated workflow cannot complete the task. For a dental support organization (DSO), expansion planning should include that remaining workload as well as the call capacity added by technology.
Read the results in context
The case reports revenue and coverage outcomes. Calculating return on investment would also require implementation and operating costs, an agreed attribution method, and the financial margin on the additional treatment.
Before comparing another practice, document the reporting period, unique-caller rules, appointment outcomes, revenue attribution, implementation cost, recurring cost, and staff review time. Present bookings, exams, and treatment starts as separate measures unless their cohort relationship is established.
Use the NIST AI Risk Management Framework to assign responsibility for evaluating the workflow; it does not validate the case results.
Dr. Yan Kalika describes the deployment in Newo’s published case:
“In Q1 alone, our AI Receptionist handled 10,865 calls, captured 1,563 after-hours inquiries we would have missed, and directly generated $401,500 in new patient revenue. The ROI speaks for itself.”
His statement gives the customer’s assessment of the results. A practice evaluating its own deployment should pair that kind of operational account with the costs and outcome records needed for its decision.
Evaluate a dental AI receptionist using clearly defined call, appointment, and financial outcomes. The Image Orthodontics case offers a useful starting point; your own decision also needs deployment costs, staff workload, and consistent attribution.
