Salary Recommendations
A salary recommendation that leaves gender out of the model.
Evenpay predicts what a role should pay from objective factors and your own pay philosophy. Gender is excluded by design, which makes the recommendation itself a fairness instrument.
45 minutes. See a recommendation built from your own data.
Numbers you can explain, in the places decisions happen.
Trained on your data, blind to gender
A regression model learns from your organisation's own data and pay factors to predict what a role should pay, using objective inputs such as experience, education, level and certifications. Gender is deliberately excluded from the model.
- Gender excluded from the model by design
- Objective inputs: experience, education, level, certifications
- Trained on your data, not a generic market formula
Grounded in how you say pay is decided
Pay factors are configurable, so the recommendation reflects your stated philosophy rather than someone else's formula. If your organisation values certifications heavily, the model does too.
- Configurable pay factors behind every recommendation
- The same factors used in your equity analysis
- A recommendation you can defend in a pay conversation
Any benchmark provider, mapped to your roles
Import benchmark datasets from any provider, map your internal levels and positions to external roles, and compare against P25, P50 and P75 for base and total compensation. You are not locked into one survey vendor.
- Bring your own benchmark data from any provider
- Map internal levels and positions to external roles
- Compare P25, P50 and P75 for base and total compensation
Generate bands, then see where everyone sits
Generate position level or grade level salary bands from your own data, with configurable buffer, midpoint progression, overlap and rounding, then apply them across grades in one step. Compa ratio shows where every employee sits against their midpoint.
- AI assisted band generation with parameters you control
- Batch apply across grades rather than one by one
- Compa ratio positioning and leveling recommendations
Where a recommendation comes from
And where it shows up, which matters just as much.
Your factors are configured
Pay philosophy settings define what legitimately drives pay.
The model learns your data
A regression trained on your organisation, with gender excluded.
Benchmarks add outside context
Your imported datasets, mapped to your levels and positions.
Recommendations meet decisions
In merit cycles, in the employee view and through Copilot.
Common questions about salary recommendations
Recommendations feed the decisions
Compensation Reviews & Cycles
Suggestions land in manager worksheets during a live cycle.
Intelligence
The same statistical engine, with its assumptions on display.
Job Architecture
Grades and bands the recommendations are positioned against.
Competence & Performance Management
Skills and certifications that feed the prediction as factors.