Timecard Not Valid
Sales or tips exist but the employee has no valid timecard — missing punches, job code, or shift mapping.
Gratuity Solutions adds an always-on watch layer to automated tip distribution, checking POS data, timecards, rules, and reconciliation totals before money moves.
Every tip distribution platform calculates. GS watches — with 6 standard alarms (always-on) and 12 custom alarms (configurable), 18 in total, across POS ingestion, clock-out, and daily reconciliation.
Sales or tips exist but the employee has no valid timecard — missing punches, job code, or shift mapping.
An employee clocked in under a job code not set up for tips has received tips.
Sales or tips run through a virtual job code with no real employee clocked in to receive them.
The POS shows zero minutes for an employee, yet tips are recorded — a timecard that didn't close cleanly.
Tips exist but can't route to a valid recipient — an unmapped position, or a pool with no eligible employee clocked in.
Sales and tips rung under a banquet/event job code need the real servers teamed up so tips split correctly.
A clock-in with no matching clock-out — the shift never closed in the POS.
Credit-card tips on a single check exceed a set percentage of the total (e.g. over 50%).
Credit-card tips on a check fall below a set percentage of the total (e.g. under 5%).
Credit-card tips on a check exceed a defined dollar amount (e.g. over $100).
A check's net sales drop below a set amount — often surfacing voids, comps, or miscategorized items.
Sales in a tracking group (Food, Alcohol, Wine…) fall below a set value. One alarm per group (dynamic).
Commission-eligible sales fall below a set value. One alarm per commission group (dynamic).
A check has sales but tips fall below a minimum — catching CC tips missed before a sync.
A non-tipped position (host, manager) generates net sales — usually checks rung under the wrong role.
The effective hourly tip rate between two job codes differs by more than 50%.
A clock-out flagged invalid or synthesized by the POS at end-of-day — the shift needs manual review.
A tipped employee's total compensation falls below the state minimum-wage threshold (e.g. $7.25/hr).
Two custom alarms — Category Sales Check and Commission Category — are dynamic: one alarm is auto-generated per enabled tracking or commission group, so a location's custom count can grow beyond 12.
Alarms validate the data flow at the moments where payout risk usually enters the process: POS import, clock-out, and final reconciliation.
GratSync validates incoming POS data for sync errors, missing records, stale files, and format issues.
Employee clock-out triggers checks for timecard accuracy, rule configuration, and timecard-to-tip mismatches.
End-of-day validation catches duplicate payouts, compliance issues, rounding gaps, and reconciliation mismatches.
Alarms reach the right people through their preferred channels.
Instant push alerts on mobile phone for critical alarms
Consolidated daily email summary of all alarms
Multi-location dashboard for area managers
Integrated into payroll processing with blockers for critical issues
These alerts protect the parts of the workflow where operators lose the most time, create the most payroll friction, and face the most compliance exposure.
Missing or misaligned timecards are a major source of tip distribution errors, saving payroll managers hours each week.
State tip law issues are surfaced before payroll runs, helping prevent wage claims and avoidable operational exposure.
Unusual tip amounts and typo patterns are caught before they become employee disputes or payroll corrections.
Every tip distribution platform calculates. But without proactive monitoring, errors are discovered AFTER payroll runs — when it's too late to correct without payroll reversal. Intelligent Alarms catch problems in real time, before they become wage claims, disputes, or compliance violations. With Intelligent Alarms, your operation is always one step ahead.
Claude AI agents add an additional monitoring layer on top of Intelligent Alarms.
AI agents monitor every distribution against 150+ algorithms and flagged patterns to catch edge cases humans would miss.
AI understands the context of each alarm — is this a one-time issue or a recurring pattern that needs escalation?
AI correlates multiple alarms to identify root causes and suggest fixes before the next distribution cycle.







































































































