Insights
Reading for the people who answer the “why”.
Useful explanations of how people records, time inputs and payroll rules connect, and how to evaluate software honestly before you trust it with real data.
Payroll review
Why has my salary changed? The question payroll should answer
A salary query is simple to ask and often slow to answer. This is what a complete answer needs, and why the trail behind the number matters as much as the number.
Payroll review
How a payroll run moves from inputs to a locked result
Input review, calculation, approval, locking. What each stage of a payroll run is for, what a reviewer should be able to see, and what locking does not mean.
Payroll review
Effective-dated payroll rules, explained in plain English
A payroll rule is not just a value. It is a value from a date. Why that distinction matters, and why rule changes deserve a maker and a checker.
Records & history
Tamper-evident vs immutable: what an audit-history check can tell you
Two words that are often used as if they meant the same thing. What a tamper-evident history check actually tells you, and where its answer stops.
Evaluation
How to evaluate HR software with sample data, not real spreadsheets
You can learn most of what matters about HR software without sharing a single real salary. What to prepare, what to ask, and what to get in writing.
Time & leave
Leave and attendance: why time inputs need their own review path
Most salary queries start as a time question. Why leave, attendance and holiday calendars deserve a proper review path, and what both sides of a leave request need.
Comparison
Spreadsheets and message threads vs one connected HR record
Spreadsheets are not the enemy, and a connected system is not automatically better. An honest look at who each approach suits, and the signs it is time to change.
Perspective
Why we publish our limits next to our claims
A product built around explanation should explain itself honestly too. Why every claim on this site sits next to its current limit.
Next step
Have a question an article did not answer?
Bring it to a sample-data walkthrough. One anonymised example is enough to start.
Start with a question, not a commitment. Use sample or anonymised information.