About Dzvin.co
Dzvin.co is an evidence-based mental health analytics platform that combines psychometric instruments, behavioral signals, and predictive models for early risk detection and better decision support.
The platform works with daily state signals — stress, mood, energy, and focus — as well as psychometric test results to track change over time, identify influencing factors, and build both personal and team-level action scenarios. On this basis, it delivers user recommendations, analytical signals for teams, and a validation loop that checks model quality against real outcomes.
Core value
The platform’s value is not only in observing mental state, but in turning signals into explainable and verifiable decisions.
Works not only with isolated signals, but with cohort and behavioral patterns.
Provides recommendations with factor-level context rather than as a black box.
Checks what worked in practice and refines models using new outcomes.
From observation to prediction
Most mental health solutions describe only the current state. Dzvin.co moves from observation to prediction, and from prediction to verifiable action.
Platform logic
A closed loop of analysis, prediction, and verification
Each stage builds on the previous one: signals gain context, predictions become action, and action becomes validated outcome.
Signals
Behavioral, psychometric, and contextual data are treated as one system of interconnected factors.
Prediction
The model estimates change dynamics and highlights the scenarios most likely to unfold with or without intervention.
Validation
Actual outcomes are fed back into the system to test conclusions and increase model reliability.
What changes
The system captures behavioral signals, rhythm changes, and state dynamics without collapsing everything into a single score.
What it means
Each signal is interpreted in the context of the individual, cohort patterns, and organizational environment.
What comes next
The model estimates state trajectories and identifies the scenarios most likely to unfold next.
What can change the outcome
The platform compares intervention scenarios and shows which actions can change the expected outcome.
What the data confirms
Observed outcomes feed back into the system to test conclusions and improve model reliability.
Who it's for
B2C
personal layer
For users
Helps people notice personal patterns, better understand state changes, and receive recommendations grounded in everyday context.
B2B
organizational layer
For businesses
Gives leaders and HR early risk signals, team-level dynamics, and a stronger basis for decisions about wellbeing, productivity, and retention.
R&D
validation layer
For researchers and research institutions
Provides an environment for working with anonymized behavioral data, testing hypotheses, and evaluating model quality at the population level.
Platform architecture
Three layers, one decision system
The analytical core, user layer, and validation layer operate as one decision-support system.
Together they connect user personalization, team-level signals, and scientific verification of model quality.