We are a research company at our core. Our work spans fundamental AI capabilities and their clinical applications, with every project driven by a singular question: how can we make mental health care universally accessible?
Understanding how someone truly feels requires more than analyzing their words. Our multimodal emotion recognition system integrates three channels of signal to build a comprehensive emotional portrait in real time.
Emotion classification accuracy (text)
Multimodal fusion accuracy
Distinct emotional states detected
Real-time classification latency
Risk signals identified through linguistic and behavioral pattern analysis.
Severity scored and appropriate intervention tier selected.
Evidence-based response generated with safety-first principles.
Automatic handoff to human clinicians and crisis resources.
In mental health crisis situations, every second matters. Our low-latency intervention models are designed to detect distress signals and generate appropriate clinical responses in under 200 milliseconds — turning hours of wait time into instant support.
The system employs a tiered response framework: from gentle check-ins for mild distress to immediate crisis protocols for high-risk situations, with automatic escalation to human professionals when needed.
All intervention models are validated against clinical benchmarks and reviewed by our independent Clinical Oversight Board on a quarterly basis.
Mental health is deeply intertwined with culture. The way people express distress, conceptualize well-being, and engage with therapeutic processes varies dramatically across cultures. A system that ignores these differences cannot serve the world.
Our cultural adaptation framework goes beyond simple translation. We build culturally-specific therapeutic ontologies that encode local idioms of distress, communication norms, family structures, and belief systems into our AI models.
Identify emerging mental health trends weeks before they appear in clinical data.
ML models that forecast regional mental health crises based on multi-source signals.
Actionable dashboards for public health officials and policy makers.
Understanding mental health at a population level is essential for effective public policy and resource allocation. Our analytics platform processes de-identified, federated data to map global mental health patterns without compromising individual privacy.
Using differential privacy guarantees and secure multi-party computation, we generate population-level insights that help governments, NGOs, and healthcare systems understand where to focus their resources for maximum impact.
Our team publishes at leading venues in AI, NLP, and clinical informatics.
Every research project at VeloPsy undergoes rigorous ethical review. We believe that advancing AI for mental health carries a special responsibility.
All research involving human data is reviewed by an independent Institutional Review Board with expertise in both AI ethics and clinical psychology.
We use differential privacy, federated learning, and synthetic data generation to advance research without ever exposing individual user information.
Every model undergoes demographic parity analysis across age, gender, ethnicity, and socioeconomic dimensions before deployment. Bias is not tolerated.
We actively seek collaborations with universities, clinical institutions, and research organizations worldwide. Let's advance the science of AI-driven mental health together.