Panoramic X-rays in.
Clear cavity insights out.

No. HelloCavity is designed as clinical decision support, not an autonomous diagnostician. The AI highlights regions that may contain early decay and provides structured cues (e.g., confidence and severity staging), but the dentist remains the final decision-maker. In practice, the tool helps reduce missed findings during busy workflows, improves consistency between clinicians, and makes patient explanations easier—without changing who is responsible for diagnosis and care.

Our approach is “assistive-first”: surface subtle signals quickly, let the clinician verify, and keep documentation clear. This is typically how AI earns trust in clinics—by improving speed and consistency while keeping clinical judgment in control.

Panoramic radiographs are commonly used for new-patient exams, broad screening, and treatment planning, but they’re harder to interpret consistently for early caries. That creates a clear gap: a single image covering the full mouth, yet subtle early decay can be overlooked.

For a startup, a panoramic-first approach is also a strategic wedge: it’s a specific, under-served diagnostic surface where depth matters. We can own the niche, build defensible data and workflows, and then expand to adjacent modalities over time.

We design around minimal disruption. A clinic uploads or connects panoramic images, the AI returns highlights and structured outputs, and the dentist quickly verifies findings during their normal review. The goal is to add a “second set of eyes” that’s fast enough to use chairside.

For multi-location groups and DSOs, the platform can also support standardization: consistent review prompts, reporting structure, and roll-up analytics (e.g., usage trends and quality checks) to help scale best practices.

Patient data is sensitive, so we use a privacy-first approach. In practice, that means secure transport, restricted access, and careful handling of any identifiable information. For clinics, the intent is to support HIPAA-aligned workflows (and equivalents where applicable) as we move through validation and commercial partnerships.

On the clinical side, we position outputs as assistive and explainable: confidence indicators, visual overlays, and structured staging—so clinicians can validate results instead of trusting a black box.

The model is a per-clinic SaaS subscription with optional add-ons for integrations, multi-location analytics, and enterprise support. Pricing is aligned to ROI: improving detection consistency, increasing clarity in patient communication, and helping clinics close more appropriate treatment plans.

For DSOs, pricing can scale by location or image volume. For single practices, it remains simple: predictable monthly cost with clear value.

Near-term is about pilots and validation: partnering with clinics to measure performance, workflow impact, and patient communication improvements. In parallel, we expand dataset diversity, tighten model performance, and prepare the evidence package needed for regulatory progress and enterprise partnerships.

After pilots, the scale path is land-and-expand: start with practices, move into DSOs and imaging networks, and build partnerships with education and imaging vendors for distribution.