The Problem: You Find Out Too Late
By the time an engineer tells you they're leaving, they've been unhappy for months. The decision was made weeks ago. You're just the last to know.
The average engineer has been job hunting for 2-3 months before giving notice. Their engagement dropped, their standups got shorter, their enthusiasm faded—but nobody was tracking it.
Exit interviews reveal what you should have seen: sustained blockers nobody addressed, growth conversations that never happened, workload that crossed from "challenging" to "crushing."
Signals Vereda Detects
Sentiment Drop
Standup responses become shorter, more negative, or less engaged over time.
Example alert: "Sarah's responses have shifted from detailed updates to single sentences over the past 2 weeks."
Repeated Blockers
The same blocker appears in multiple standups without resolution.
Example alert: "API dependency mentioned as blocker 5 times in 10 days. No progress recorded."
Silence
Engineer stops responding to standups or check-ins.
Example alert: "James hasn't submitted a standup in 4 days. Previous participation was consistent."
Disengagement
Responses become formulaic, copy-paste, or lack substance.
Example alert: "Last 5 standups contain identical 'No blockers' response with minimal detail."
How AI Burnout Detection Works
Collects Signals
Vereda analyzes standups, check-in responses, goal progress, and activity patterns across your team.
Builds Baselines
Learning mode establishes what 'normal' looks like for each engineer before alerting on deviations.
Detects Patterns
AI identifies concerning patterns: sentiment shifts, repeated blockers, silence, or disengagement.
Alerts You
Get notified via Slack or dashboard when an engineer shows signs of burnout or flight risk.
Learning Mode: No False Positives
Every team is different. What's normal for one engineer might be a red flag for another.
Vereda runs in "learning mode" for your first 2-4 weeks, building baselines for each team member. It learns:
- Typical standup response length and tone
- Normal patterns of engagement
- Individual communication styles
- Team-specific norms and culture
Only after baselines are established does Vereda start alerting on deviations. This means fewer false alarms and more meaningful signals.
What You See in Your Dashboard
Team Health Overview
At-a-glance view of your team's engagement levels. See who's thriving and who might need attention.
Individual Risk Scores
Daily AI-generated assessment for each team member, with the factors contributing to their score.
Actionable Alerts
Slack notifications when someone needs attention, with suggested next steps and context.
Frequently Asked Questions
How accurate is the AI at detecting burnout?
Vereda's AI is designed for high precision with low false positives. It uses multiple signals in combination—a single bad day won't trigger an alert. We look for sustained patterns across 1-2 weeks.
Can engineers see their own risk scores?
No. Risk analysis is manager-facing only. Engineers see the helpful parts of Vera (chat assistant, micro check-ins) but not the diagnostic signals about themselves.
What should I do when I get an alert?
Alerts come with suggested actions. Usually, the right move is a casual 1:1 to understand what's happening. The goal is early intervention, not surveillance.
Is this just monitoring engineers?
It's about catching problems early so you can help. The same data that flags burnout also helps you recognize when someone deserves a shoutout or is ready for more responsibility.