Explore the demo datasets
Review and interact with the section below to see how Flash Operations surfaces risk, workload, and bottlenecks before they turn into real world problems.
High-risk employees (sample)
The people most at risk of burnout or departure based on your data.
| Emp ID | Role | Risk | Burnout | Hours | Tenure |
|---|---|---|---|---|---|
| #15397032 | Registered Nurse | 96% | 8.6 | 59h | 7.3y |
| #14555690 | Physician | 94% | 8.3 | 60h | 9.7y |
| #10728400 | Surgeon | 94% | 7.9 | 60h | 4.1y |
| #10698815 | Physician | 93% | 7.6 | 60h | 1.4y |
| #13522266 | Nurse Practitioner | 92% | 7.8 | 59h | 6.4y |
Drill into a single employee with agents
Pick a scored employee to see why the model thinks they're at risk, and explore what happens if you adjust levers like burnout, coverage gaps, hours worked, or support signals. This uses the same explain + scenario engine that powers the full product view.
/api/demo/scored-employees, this panel will let you click into an employee and run explain + scenario agents directly from the demo.See how targeted interventions (rebalancing workload, fixing schedules, supporting high-risk staff) could shift overall risk and wellbeing.
These improvements are illustrative based on similar organizations. In production, projections are generated from your own data and chosen interventions.
Burnout by role
Switch between burnout and work-life to see where pressure is concentrated.
Risk by tenure bucket
Which tenure bands are most at risk?
What the model is trying to fix
A quick story about the pressure points surfaced in your workforce.
- Around 32% of the active workforce is flagged as at-risk.
- 16 people are in the high-risk cohort for burnout or departure.
- Average burnout is 6.4 with work-life scores as low as 3.1 in some roles.
- Identify overloaded roles and departments early, before departures start.
- Recommend schedule and staffing changes that reduce burnout hot spots.
- Highlight individuals who need support now, not after they’ve resigned.
In a live deployment, this panel would reflect your specific organization: the departments under strain, the roles at risk, and the expected impact of each intervention.
Sample underlying data
Schema glimpse · read-onlysample_rows in your demo response.