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For contract security providers, maximizing workforce efficiency and controlling payroll costs are essential for profitability and client satisfaction. Large datasets—spanning hundreds of personnel, job sites, and shift records—can obscure opportunities for optimization. In today's competitive environment, contract security operations need agile, data-driven decision-making to manage labor variability, reduce unproductive shifts, and maintain regulatory compliance. This case study demonstrates how end-to-end, agentic AI from Scoop enabled a mid-sized security services provider to automate complex analysis, unlock new insights from dispersed time-tracking data, and rapidly translate findings into business results.
Scoop's automated analytics unveiled actionable workforce and payroll patterns hidden in the raw data. The ability to precisely segment work patterns by employee, job site, and shift type enabled management to rebalance staffing, investigate wage policy adherence, and address process gaps. Notable results included identification of peak demand periods, exposure of workload disparities, and diagnostics on validation inefficiencies. With Scoop surfacing insights that would traditionally require days of manual spreadsheet analysis or custom BI, the team accelerated operational refinement, improved compensation equity, and prioritized review of critical data quality issues.
Computed across all paid shifts, highlighting low baseline rates and raising questions about wage policies for different job tiers.
Period cost exposure, providing a clear view of payroll magnitude relative to operational priorities.
Period cost exposure, providing a clear view of payroll magnitude relative to operational priorities.
Revealed potential underutilization or data capture errors, with over half of entries lacking recorded hours.
Operational focus with more than half of staff activity at the leading location, guiding resource deployment decisions.
Contract security firms manage a transient, distributed workforce tasked with safeguarding diverse sites—ranging from event venues to commercial spaces. Operational oversight often suffers due to fragmented time-tracking systems, inconsistent payroll entries, and variable shift requirements across job categories. This fragmentation makes it tedious to surface workload inequalities, suboptimal scheduling, and pay rate anomalies. Legacy BI tools struggle to synthesize data at the required granularity, hampering real-time insights into resource allocation, payroll leakage, or compliance flags. Security agencies must answer: Where are our staffing pressures? Which shifts or roles yield the highest ROI? Are there hidden disparities or process breakdowns in workforce validation and compensation? Without transparent, actionable analytics, inefficiencies persist and margins erode.
The input dataset consisted of comprehensive time-tracking and payroll records from a contract security provider, capturing individual shifts worked by 22 personnel across a handful of prioritized locations over a multi-month period. The dataset included 489 shift entries, with attributes such as date/time of entry, job role, site, hours worked, pay rate, device type, and validation status. Totaling 1,695.8 hours and pay of 29,864.30 in local currency, the data offered multidimensional insight into operational activity.
Scoop’s fully agentic AI pipeline automatically executed the following steps:
The input dataset consisted of comprehensive time-tracking and payroll records from a contract security provider, capturing individual shifts worked by 22 personnel across a handful of prioritized locations over a multi-month period. The dataset included 489 shift entries, with attributes such as date/time of entry, job role, site, hours worked, pay rate, device type, and validation status. Totaling 1,695.8 hours and pay of 29,864.30 in local currency, the data offered multidimensional insight into operational activity.
Scoop’s fully agentic AI pipeline automatically executed the following steps:
Through its agentic modeling, Scoop uncovered work patterns and anomalies that would elude conventional dashboards. Pay rates were found to be strongly employee-dependent, with certain individuals commanding premium compensation regardless of experience or site. For example, specific personnel consistently received double the standard hourly rate under defined shift conditions (e.g., weekend or late-night assignments). Additionally, the combination of employee, site, and day-of-week drove nuanced pay and workload outcomes, such as higher rates on Sunday shifts at particular venues.
Shift classification exhibited high dependency on entry time: a 19:00 start almost always yielded a 12-hour extended shift, while 06:00 starts led to 8-hour standard shifts. Interestingly, 'No Hours' entries defaulted to 'VALID' in the timesheet approval process, while any actual hours triggered a 'NOT VALID' status—suggesting a two-stage workflow or underlying system flaw. Over 56% of shifts carried no recorded hours, reflecting either substantial data quality issues, high turnover of scheduled but uncompleted shifts, or inconsistencies in clocking processes.
Crucially, relationships surfaced among device types used for log-ins, employee roles, and error rates, allowing managers to trace root causes of validation failures and optimize data collection policies. These granular, ML-derived rules are impractical to surface in standard BI suites, as they emerge from hundreds of intersecting staff, site, and shift combinations.
Armed with these operationally relevant insights, management prioritized a review of validation protocols to address the high volume of unvalidated labor entries. Scheduling was rebalanced to reduce pronounced workload disparities among staff, particularly for those personnel consistently assigned to extended or premium shifts. The team investigated low or zero pay instances for compliance risk and potential payroll leakage. Next steps include implementing automated exception reporting for outlier shifts, refining onboarding to emphasize clock-in protocol adherence, and extending agentic modeling across future payroll cycles to continually monitor data quality and equity in shift allocations.