A dental clinical-AI startup, founded by a practicing oral surgeon, needed a platform that turns patient medical histories into clinical summaries before procedures, with clinical rules kept under practitioner control. Mindbowser built it as a multi-tenant SaaS platform on Google Cloud.
Discuss Your ProjectA dental clinical-AI startup founded by a practicing oral surgeon, serving single and multi-location dental practices.
Multi-tenant SaaS platform, clinical rules engine, three role-based portals, and a WordPress marketing site with Stripe onboarding.
React, TypeScript, Java Spring Boot, PostgreSQL, Google Cloud, Vertex AI with Gemini Flash 3.0.
Practices get AI-generated clinical summaries that answer to structured rules, histories that show what changed between visits, and one place to run every location.
A structured rules layer defines what the analysis has to check, instead of leaving it to the prompt.
Versioned histories let practices identify new or changed medications, diagnoses, and surgeries from one visit to the next.
The modular inference layer lets the model be evaluated or replaced without reworking the surrounding platform.
Locations, staff, patients, forms, billing, and clinical workflows are managed in one place.
Clinical rules update through structured JSON, not changes to application code.
Dental procedures depend on medical histories that are often incomplete. Dentists need the relevant medications, conditions, surgeries, and prescribing risks in front of them quickly, and the checks behind them have to be clinically governed.
Patients underreport, forget, or leave out medications, conditions, and surgeries, and the dentist has to find what matters before the procedure.
Medication interactions, condition-specific risks, and prescribing-safety considerations needed structured clinical rules, not a prompt.
A new medication, diagnosis, or surgery can change what a dentist needs to see. Overwriting the previous history would lose that context.
Multiple practices, locations, administrators, and clinicians had to share the platform with patient data isolated and PHI protected in every workflow.
React and TypeScript on the front end, Java Spring Boot and PostgreSQL behind it, Gemini Flash 3.0 on Vertex AI, all on Google Cloud, with Stripe for billing, Twilio for SMS, Zoho ZeptoMail for email, and a WordPress (Elementor) marketing site designed in Figma.
A JSON rules layer covers conditions, medications, surgeries, and prescribing-safety protocols, and acts as the structured clinical reference. Gemini Flash 3.0, called through Vertex AI, generates the medical-history summary and clinical analysis report on top of it.
Practices configure their own medical-history form without code changes. Each history is versioned automatically instead of overwritten, so previous versions stay accessible and comparable.
Three role-based portals separate platform administration, practice administration, and clinical work. Practices manage locations and staff, invite users, send reminders, and pay by subscription in the same platform.
Each practice's patient data stays isolated in the multi-tenant architecture. Access follows role, activity is logged, and reminder messages are designed to carry no PHI in their content.
We build healthcare products where rules engines, PHI handling, and AI inference work together. Talk to the team that built this platform.
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