Three service tracks of one platform
The platform works as a personal service for users, an analytics service for companies, and a validation service for programs and research. Below is how its role changes across these three scenarios.
Personal support, corporate analytics and scientific forecasting in one system
Dzvin.co helps identify stress, burnout and behavioral risk before they escalate into critical outcomes. The platform combines personal state tracking, team stability analytics, and research-grade forecasting models built on behavioral dynamics and uncertainty.
Personal
Self-checks, daily exercises and a personal trajectory.
Corporate
Company structure, team analytics and decision support for managers.
Research
Cohorts, validation loops and reporting for research institutes, universities and programs.
Personal support track
For individuals, the service combines daily observations, exercises and tailored recommendations into one coherent action path.
Focus: continuous personal support, short feedback loops and visible progress.
Corporate operations track
For companies, the service combines roles, groups, team analytics and risk signals into one manageable operating model.
Focus: aggregated team visibility, scoped access and timely management action.
Research validation track
For research programs, institutes and universities, the service connects cohorts, outcome measurement and evidence accumulation to validate hypotheses.
Focus: impact validation, repeatable observations and accountable program reporting.
For individuals
The personal service gathers state signals, offers simple practices and helps reveal what actually supports mental well-being in everyday use.
Brings state signals into one sequence.
Suggests a relevant practice right after self-check.
Keeps change history easy to revisit.
State becomes easier to understand over time.
The next step is clear right after assessment.
Support turns into a path that is easy to continue every day.
For companies
The B2B layer turns team state signals into an early-response operating system: leaders and HR get an aggregated risk view, see where teams are losing energy, and can act before the issue becomes burnout, attrition or a visible performance drag.
What a manager sees and controls
Org structure, roles and accountability zones
The platform brings teams, roles, groups and access scopes into one managed layer so managers can see the right slice of the organization without permission chaos, manual reporting merges or endless clarification loops.
State dynamics across teams and groups
Instead of stitching together fragmented signals, managers get one analytical surface for stress, energy, focus and mood trends, making it easier to spot which teams need attention right now.
Action priorities and support control
The system highlights critical risk zones, helps identify where intervention will have the biggest payoff, and moves management action from intuition-led to evidence-led.
What this gives the business
One management surface for team well-being
Leadership gets a transparent aggregated view of team state without exposing private stories, which makes well-being manageable as an operating discipline rather than a reactive afterthought.
Faster decisions with measurable operational payoff
When risk becomes visible earlier, companies can prioritize support faster, direct manager attention with more precision, and reduce losses from burnout, attrition and performance decline.
A stronger case for HR and executive buy-in
Data dynamics move the conversation from ‘well-being matters’ to ‘here is where the business is already losing capacity and where support creates measurable return’, making leadership alignment easier to secure.
For researchers, institutes and universities
The R&D layer turns the platform into a research environment: standardized self-checks, behavioral signals, cohort analysis, prediction validation and outcome tracking in one system.
The technical research layer
Build comparable cohorts
Unified collection instruments make it possible to build cohorts, compare subgroups and repeat an observation design across collection waves.
Validate predictions
The prediction-to-outcome loop shows model accuracy, drift and change after intervention at the cohort or program level.
Report impact
Data can be turned into material for reports, ethics packages, grant applications and communication with organizations that finance research.
What we actually do
Collect standardized observations
We work with the same scales, behavioral signals and controlled data collection flows so results remain comparable.
Build cohorts and analytical cuts
Participants can be segmented by state patterns, risk, intervention response and observation windows.
Validate predictions against outcomes
We show where the model was accurate, where drift appeared and what effect was actually observed after intervention.
Scientific value
Repeatable measurement logic
The same measurement logic works in the everyday product and in the research layer, so data does not break across environments.
Visible drift and accuracy
Teams can monitor how prediction quality changes over time and whether a model stays valid on new samples.
Evidence for papers and grants
Cohort comparisons, intervention dynamics and program outcomes accumulate into material that can support a publication or grant proposal.
How we help researchers
Reduce time to a working study design
Teams do not start from zero: the digital loop for data collection, cohort analysis and outcome capture is already in place.
Provide structure for reports and papers
Researchers get more than raw signals and can work with a structure that supports analysis, conclusions and accountable reporting.
Strengthen grant reasoning
We help explain what is measured, how effect is validated and why the approach is practically valuable for a research team or grant provider.