06For People and HR
Objective data for fair performance reviews
Engineering performance shouldn't be a popularity contest. Get unbiased contribution data that supports fair, defensible reviews.
Early warning / load vs baseline
Sample data- Contributor 1142 percent of baseline, 9 after-hours commits, flagged
- Contributor 296 percent of baseline, 1 after-hours commits
- Contributor 3121 percent of baseline, 5 after-hours commits
- Contributor 478 percent of baseline, 0 after-hours commits
- Contributor 5109 percent of baseline, 2 after-hours commits
- Contributor 6133 percent of baseline, 7 after-hours commits, flagged
2 of 6 contributors above threshold
Names hidden in this sample. Signals start a conversation, they never score a person.
- Privacy-first
- Manager approved
- Developer trusted
- Contribution Score
- 87/100
- Above avg
- Consistency
- 94%
- Steady
- Collaboration
- High
- 45 reviews
02Signals
What slows you down today
Open signals
3 openSubjective Reviews
Engineering reviews often come down to who speaks up loudest or who the manager likes best. You want fairness, but you lack data.
Blind spotNo Visibility
You can't evaluate engineering contributions because the work is technical and opaque. You rely entirely on manager opinions.
Blind spotBias Concerns
Unconscious bias creeps into reviews. You want to support DEI initiatives but need objective data to back decisions.
Blind spot
03Capabilities
What you get on day one
Contribution Metrics
Objective data on commits, PRs, code reviews, and collaboration. No more relying solely on manager opinions.
Bias Reduction
Data-backed insights reduce unconscious bias. Support fair promotions and compensation decisions.
Trend Analysis
See contribution patterns over time. Identify consistent performers vs one-time spikes.
Collaboration Scores
Code reviews, mentoring, and helping teammates. Recognize the glue that holds teams together.
Review Reports
Pre-built reports for performance review cycles. Save hours of data gathering and consolidation.
Privacy Controls
Aggregated insights that respect individual privacy. No surveillance, no micromanagement, just fair data.
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04Playbook
How teams use DevLyTicks
- 01
An HR partnership on humane signals
Focus onboarding ramp time, collaboration patterns, and workload sustainability rather than surveillance metrics that erode trust.
Explore contributor insights - 02
Prove onboarding programs work
Track time from first commit to independent delivery so onboarding budgets map to measurable acceleration for every cohort.
- 03
Co-create an equitable review culture
Track review participation spread across teams so invisible glue work gets recognized and review load stays balanced.
Before DevLyTicks
After DevLyTicks
- Data-backed performance insights
- Clear contribution visibility
- Objective promotion criteria
- Hours gathering review data
- Quiet contributors overlooked
- Disputes about fairness
05Shift
From guesswork to evidence
06Start
Ready for fairer reviews?
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