The Challenge
Despite a straightforward transaction model, the retail chain’s scale across hundreds of sites introduced complexity. Multiple POS systems with high switching costs, PDFs and CSVs requiring manual reconciliation in Excel, and utility data fragmented across municipalities all combined to slow financial closes, increase labor costs, and erode trust in reported numbers - making growth harder. Standard reconciliation software was not viable, as it assumed clean ERP feeds and could not handle the extremely fragmented and multi-system environment the client operated in.
"AI-driven reconciliation cut weeks of manual work down to hours, giving us speed, accuracy, and confidence to scale."
The Solution
Unframe deployed an AI-powered performance monitoring system that continuously scans transactional data from Quivio to detect meaningful shifts in business performance across car wash locations. Using a Dynamic Triggering System, the AI identifies anomalies - such as a 10% drop in recurring washes or an unexpected revenue spike - and performs Root Cause Analysis (RCA) by layering public data like weather, traffic, holidays, and local events to explain why changes occur. This enables operations teams to respond proactively to issues, optimize faster, and make every decision data-driven.
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