Rules over spreadsheets
Named, testable rules make exceptions explainable and reduce dependence on fragile manual formulas.
Dashient Attendance
Overview
An internal attendance and payroll workflow built around the real shift rules used by hospitality and service teams.
- Role
- Product design · Business logic · Full-stack engineering
- Stack
- Flask · Python · PostgreSQL · GPS check-in · Multi-currency
01 / Challenge
The problem worth solving.
Paper shift records made worked hours difficult to verify and moved payroll disagreements downstream to the end of each month.
02 / System architecture
How the parts connect.
- 01
Photo and GPS capture
- 02
Timezone-aware shift records
- 03
Rule engine
- 04
Payroll calculation
- 05
Export-ready output
03 / Implementation
From ambiguity to a working system.
- 01
Modelled grace periods, breaks, weekend work and mid-month starts as explicit, testable rules.
- 02
Added photo and GPS context to live clock-in and clock-out records.
- 03
Anchored shifts to each employee’s timezone and calculated pro-rata pay from working days rather than calendar days.
- 04
Prepared payroll-ready outputs designed to fit an existing operational process.
04 / Engineering decisions
Working-day pro rata
Mid-month pay is based on actual working days, while weekend work remains an explicit overtime line.
Context at capture
Photo, location and timezone context travel with the attendance event instead of being reconstructed later.
05 / Outcomes and current status
Rule-basedshift engine
Liveclock in / out
Payrollexport ready
06 / Constraints and limitations
What the demonstration does not claim.
- Attendance records can be sensitive, so the public demonstration uses fictional staff and illustrative values.
- Location and photo capture must be communicated clearly and handled with appropriate consent.
- Payroll rules vary by organisation; the system makes those rules configurable rather than universal.
07 / Technology
FlaskPythonPostgreSQLGPS check-inMulti-currency