Prevention was episodic
Families reached specialists too late, specialists worked in isolation, and administrators saw reports after the situation had already changed.
A new prevention paradigm after AI
Prevention can finally become a living system.
For decades, prevention systems were limited by cost, paperwork and the lack of continuous feedback. AI changes the technical and financial boundary. Prevention AI Platform is built as a hierarchy of applications: support for families, AI workspaces for specialists, and aggregate dashboards for institutions and governments.
Why this must exist
AI makes a public-health prevention architecture possible.
The goal is not a chatbot. The goal is a new operating layer for the prevention of risks among growing generations: grounded in public-health evidence, connected to specialist practice, and continuously learning from aggregate, privacy-safe signals.
Support can be continuous
Interviews, diaries, de-escalation and specialist preparation can happen between formal appointments, at a cost that was previously impossible.
A hierarchy of apps
Each application is an organ of one system: consumer support, specialist workflow, scientific analytics and territorial governance.
Scientific layer
Implemented foundation
Taxonomy, knowledge base and BigQuery research loop
The platform is not driven by generic prompting alone. It already includes a prevention taxonomy, a structured knowledge-base direction and an analytics contour where aggregate events can support research and evaluation.
Roadmap as the main story
From a live consumer product toward multi-level prevention infrastructure.
Level 1
Implemented
Teenology: the consumer prevention layer
Parents and teenagers get a free prevention check-up, AI accompaniment (not therapy), Family Bridge mediation, a local diary and offline support. This is the already-deployed bottom layer: early conflict softening before institutions are involved.
Pilot-ready client: The core B2C client engine is fully developed as a standalone PWA and ready for deployment.
Fund demo. On teenology.care sign in with Google or email — full Companion access (AI chat, Family Bridge) activates automatically for 30 days. No promo code.
Level 2
Implemented prototype
Prevention.AI: the specialist workspace
Specialists receive an AI assistant for quick consultation, methodological expertise and document drafts. The current specialist interface is already available as the Prevention.AI bot for professionals.
Level 3
Next funding milestone
Specialist terminals and dashboards for administrators
The next stage is to finish workstations and dashboards for every administrative level: school director, district coordinator, regional ministry and national prevention administration. Sensitive case data stays local; upper levels receive only aggregate signals.
Product evidence
Current product surfaces & interfaces
The Prevention AI Platform is being built incrementally. Consumer and specialist interfaces are live today. Territorial dashboards are an architectural blueprint (screenshot below): the design shows how anonymized macro telemetry would flow upward once Levels 1–2 are connected at scale—not a live government deployment yet.
Teenology family surface
Open full preview
Stand-alone progressive web app with client-side logging and offline support cards.
Prevention.AI specialist desk
Open full preview
Operational workspace for school psychologists. Live English prototype at web.prevention.school.
Territorial system dashboard
Open full preview
B2G analytics layer (design target)—no family identities. Illustrative capture; aggregate dashboards are not in production yet.
Why grants matter
The missing piece is not the idea. It is the runway to connect the layers.
Cloud and startup support would let the project move from implemented parts to a live end-to-end demonstration: long-context AI, scalable inference, specialist account federation, terminals and aggregate dashboards.
Microsoft path
Azure/OpenAI credits help scale deep AI accompaniment and move the specialist data layer toward production-grade PostgreSQL infrastructure.
Google path
Gemini and Vertex AI are natural fits for long family context, specialist workflows and international market validation through Google Ads.
Platform Founder
Built by a practicing psychologist who writes the code.
Roman Dubrovsky, PhD, connects 25 years of hands-on preventive practice with children and adolescents in schools and youth programs with full-stack software engineering to turn the Prevention AI Platform into a continuous evidence-production engine.
The scientific layer is built for continuous improvement: aggregate, privacy-protected signals can inform a statistical feedback loop as pilots grow. Field documentation is meant to refine protocol boundaries in software—not replace regulation, clinical judgment, or formal validation studies.