Attrition Risk
Private betaThe resignation letter is the last thing that happens, not the first
By the time someone gives notice, the damage is done — knowledge walks out, teams scramble, projects stall. Gitrevio detects the behavioral shifts weeks or months earlier, giving you time to act.
How It Works
How it works
HIGH RISK
Sarah Chen (Backend) Score: 0.78
Signals: review frequency -45% (8 weeks), commit
scope narrowing, cross-team interactions -60%,
1:1 skip rate increasing
Contributing factors (Shapley):
engagement decline 38%
reduced scope 27%
social withdrawal 22%
schedule pattern change 13%
Estimated departure window: 4-8 weeks
Impact if leaves: -38% review capacity, 3 orphaned services
MEDIUM RISK
James Park (Frontend) Score: 0.52
Signals: velocity stable but code review depth
declining, meeting attendance sporadic
Estimated departure window: 2-4 months
Attrition risk is not a single metric — it is a constellation of behavioral shifts detected across multiple dimensions simultaneously. No single signal is conclusive. A drop in commit frequency alone means nothing. The model looks at the pattern.
Each engineer's baseline is calibrated individually. A senior architect who reviews 20 PRs a week dropping to 12 is a very different signal than a junior developer whose review count fluctuates naturally.
The model updates continuously. Risk scores aren't a monthly snapshot — they adjust as new data arrives, so you see emerging patterns in real time.
Signal Categories
What the model watches
A focused set of behavioral signals derived from engineering activity. The model weighs them relative to each individual's established baseline. Signals marked planned are on the private-beta roadmap.
Commit cadence
Fewer commits per week versus the individual's own recent baseline. Raw velocity decline is the core computed signal.
Review withdrawal
Declining PR comments, shorter reviews, fewer review requests accepted — disengagement from the team's shared work.
Weekend / schedule shift
Changed working hours and weekend activity patterns, decreased overlap with team core hours.
Vacation imbalance
Unused or lopsided time-off relative to the team, a known correlate of burnout and disengagement.
Team-relative deviation
How far an individual's activity has drifted from their team's norm, not just their own baseline.
Scope narrowing
Working on fewer repos, avoiding new features, maintenance-only commits. Planned — on the roadmap.
Social disengagement
Fewer cross-team interactions, slower Slack response times, reduced discussion participation. Planned — on the roadmap.
Learning cessation
Stopped exploring new parts of the codebase — no new file touches or technology adoption. Planned — on the roadmap.
Mentorship withdrawal
Stopped answering questions, fewer pair-programming sessions, reduced onboarding involvement. Planned — on the roadmap.
Shapley attribution: know why, not just what
A raw risk score of 0.78 is not actionable. You need to know what is driving the risk so you can address it. Shapley values decompose the score into contributing factors with mathematically rigorous attribution.
This transforms a vague sense of concern into a targeted conversation. Sarah's risk is driven by engagement decline and reduced scope — not compensation or work-life balance. A challenging new project might help more than a raise.
Different root causes demand different interventions. The Shapley breakdown tells you which lever to pull.
# Shapley factor decomposition
Sarah Chen — Risk score: 0.78
Factor breakdown:
engagement decline ████████████░░░░ 38%
reduced scope ████████░░░░░░░░ 27%
social withdrawal ███████░░░░░░░░░ 22%
schedule change ████░░░░░░░░░░░░ 13%
Suggested intervention:
→ New project assignment (addresses top 2 factors)
→ 1:1 conversation re: growth trajectory
Privacy & Ethics
Privacy and ethics
This is about organizational health, not surveillance.
Attrition risk scoring exists to help managers have better conversations and make proactive decisions — not to monitor individuals.
Risk scores are visible only to direct managers and above.
Individual activity patterns stay private — only aggregated risk signals surface. Engineers can see their own profile and understand what data informs their score.
No keystroke monitoring. No screenshot capture. No individual hour tracking.
Gitrevio works from commit metadata, PR activity, and review patterns — artifacts the team already produces as part of normal work.
Early Warning
What you can do with early warning
01
Proactive 1:1 conversations
Address concerns before they calcify into a decision to leave
02
Project reassignment
Move someone to work that reignites their engagement
03
Mentorship pairing
Connect disengaging engineers with senior mentors
04
Career development
Create a growth path that makes staying the better option
05
Compensation review
When pay is the driver, act before a competing offer arrives
06
Team restructuring
Reorganize around people's strengths and interests