Can managers see individual answers?

No. Managers only see aggregated, anonymised team trends. Individual check-in data is never exposed — this is enforced at the database level, not just the UI. Even Restemb staff cannot access individual responses.

How is this different from a pulse survey?

Surveys are quarterly, long, and largely ignored. Restemb is 30 seconds, daily, and uses AI to spot patterns across time — not a single point-in-time snapshot. Daily data density is what makes the 4–6 week forecast possible. No tool that collects data weekly or quarterly can replicate this.

What research is Restemb built on?

The check-in is based on the Job Demands-Resources (JD-R) model (Bakker & Demerouti, 2007) and the Maslach Burnout Inventory (MBI-GS). Both are the gold standard for workplace burnout measurement with 40+ years of validation.

Does Restemb validate its own predictions?

Yes. Every high-risk prediction is logged with a timestamp and contributing factors. When HR logs a departure or medical leave, Restemb maps it back to see whether — and how far ahead — the risk signal appeared. Your account builds an auditable accuracy record over time, org by org.

What happens to data when we cancel?

You get 30 days to export everything. After that it is permanently deleted. We do not retain it or sell it. Full GDPR compliance with complete audit trails.

What is the minimum team size?

Manager dashboards activate at 5 active users. This protects employee privacy even in small teams — individual signals can't be reverse-engineered from aggregates when the denominator is below 5.

Restemb

Built on validated science · Not an AI wrapper

Your best people
don't quit suddenly.Burnout builds for months. You can see it weeks early.

No more surprise resignations from undetected burnout
Managers see team patterns — never individual data
Employees answer honestly because privacy is enforced in the database

Free during betaGDPR + DPDPA compliantIndividual data never shared

Anonymous signal

Workload ↑ 3 days running

5 weeks early

Energy declining trajectory

No names. Ever.

Team · 14 members

83% healthy this week

Individual identity hidden

53%

of employees

experience burnout

Gallup, 2024

4–6w

early warning

before burnout peaks

Daily signal density model

1.5×

annual salary

to replace one burned-out person

Deloitte research

30s

daily check-in

science-backed, non-intrusive

JD-R + MBI validated

The research behind the signal

Job Demands-Resources model

Bakker & Demerouti, 2007

The world's most validated framework for predicting workplace burnout. Restemb's three daily questions map directly to its core dimensions: exhaustion, demand overload, and resource availability.

Maslach Burnout Inventory

MBI-GS · Maslach, Leiter & Schaufeli

The gold standard for clinical burnout measurement since 1981. The weekly deep-dive question uses the verbatim exhaustion item from the MBI-GS — 40+ years of peer-reviewed validation behind every response.

Why daily frequency matters

Signal density vs. periodic surveys

Burnout develops across 6–8 weeks. Daily check-ins generate 20–30× more data points per month than weekly pulse surveys — the signal density that makes trajectory forecasting statistically meaningful.

How it works

Three steps that give you weeks of warning.

Step 01

30 seconds.
Every morning.

Three science-backed questions — energy, stress, workload — drawn from the Job Demands-Resources model. Works on web, Slack, or Microsoft Teams. No friction. Employees don't skip it.

Built on the JD-R model (Bakker & Demerouti, 2007) and the Maslach Burnout Inventory — 40+ years of validated science.

Daily check-in · Employee view

How is your energy level today?

How manageable is your workload?

How stressed are you feeling?

Your answers are never shown to managers individually

AI forecast · Anonymous team member

Trajectory detected 6 weeks before typical burnout indicators appear

Step 02

The pattern
surfaces automatically.

Daily data density is what makes early detection possible. Each response is scored against the JD-R model. When the trajectory slopes down over 2+ weeks, the system flags it — 4 to 6 weeks before crisis.

No tool that collects data weekly can replicate this. The gap between daily and weekly signals is exponential, not incremental.

Step 03

Signals reach managers.
Names never do.

Managers see that someone on their team is at risk. The pattern. The duration. The severity. They do not see who. Individual identity is protected at the database layer — not a UI preference, not an admin toggle.

Aggregated at database layer before queries return

Minimum 5-person cohort before any dashboard activates

Voluntary participation — no coercion built into the product

Privacy architecture · What managers see

Individual check-ins

Alex · Sam · Jordan · 11 others

Anonymised · aggregated · minimum 5 users

What the manager sees

Team avg energy3.8 / 5

Members flagged2 (anonymous)

Individual scoresNot accessible

Who it helps

One product. Three people it transforms.

Employees

Your wellbeing stays yours.

30 seconds every morning — less than checking Slack
See your own energy and stress trends over time
Your individual answers are never seen by your manager — enforced in the database, not a setting
Participation is voluntary. Skipping never gets flagged.

Managers

Act weeks before someone leaves.

Team-level stress signal — no individual data, ever
Early warning arrives 4–6 weeks before a typical burnout crisis
Know something is wrong without violating anyone's privacy
Suggested conversations to have — not just a dashboard to stare at

HR & Leadership

Company-wide signal with zero legal risk.

Org-wide burnout trend visible across departments
No individual data means no GDPR exposure from HR accessing it
Scoring built on 40+ years of published burnout research
See whether interventions actually worked — over time

Privacy by design

Your team's trust
is the product.

Check-ins only work if employees trust the system. Privacy isn't a setting in Restemb — it's enforced at the data layer. No UI toggle. No admin override. By architecture.

Individual answers never visible to managers — at the query level

Team dashboards require minimum 5 active members

Free-text notes encrypted with AES-256-GCM

Permanent deletion within 30 days of cancellation

GDPR, UK GDPR, and India DPDP Act 2023 compliant

Never sold to third parties

Data architecture · How privacy works

Employee check-ins

Individual responses · Encrypted at rest

Anonymised · aggregated · min 5 users

Manager view

Aggregated trends only · No individual data

2 signals

Further aggregated · org-level only

HR & leadership

Org-wide risk distribution · Anonymised benchmarks

Why Restemb

Generic HR tools aren't built for burnout.

Daily signals, not quarterly snapshots.

Burnout develops across weeks. A quarterly survey sees the aftermath — not the cause. Daily data is the only foundation for prediction.

Prediction, not reporting.

Every other tool tells you what already happened. Restemb tells you what will happen — 4 to 6 weeks before the peak. There's no comparison.

Privacy enforced by the database, not the UI.

Other tools promise privacy through settings. Restemb makes it structurally impossible for managers to see individual data. The query never returns it.

Capability	Restemb	15Five	Viva Insights	Culture Amp	Kona
Check-in frequency	Daily · 30s	Weekly	Calendar	Quarterly	Slack
Early burnout signal	4–6 weeks	1–2 weeks
Privacy at data layer					Partial
Works for teams < 20
Science basis	JD-R + MBI	Custom	Behavioural	NPS	—

FAQ

Common questions

Beta · Free access

Stop losing your best people
to something you could have seen.

Beta is free. You get full access, founder pricing locked for life, and you help shape the product.

No credit card required · GDPR compliant · Individual data always private

Your best peopledon't quit suddenly.Burnout builds for months. You can see it weeks early.