Tablecost
A freshness-aware P&L and operations cockpit for regional restaurant groups that normalizes location data, surfaces explainable variance cases, and routes supplier, inventory, sales, scheduling, payroll, and law-review evidence to accountable owners.
The supplied research confirms a single-restaurant financial tool connecting operations data and an established multi-location restaurant suite with batch P&L workflows. It found no product combining near-real-time cross-location anomalies with the proposed regional-group focus. Tablecost must reject the invention's theft-versus-supplier classifier. A location variance cannot establish theft, misconduct, or individual responsibility; menu mix, volume, promotions, waste, transfers, counting, yield, comps, refunds, timing, supplier price, invoice error, delivery, weather, events, accounting mappings, and data outages can all produce similar signals. The product normalizes periods and operating context, displays data freshness, creates an unexplained-variance case with source evidence and alternative hypotheses, and routes review to operations, finance, purchasing, inventory, payroll, and HR. Any state labor-law overlay is an applicability candidate for qualified review, not a compliance verdict. It never scores employees, changes schedules or pay, triggers discipline, files claims, or accuses a location. The original stage verified no production API; source-system access, semantics, writes, and readback remain gates.
The regional operations, finance, controller, purchasing, inventory, or people-operations leader at a restaurant group with roughly 20 to 100 locations.
A new single-location entrant and batch-oriented incumbent gap support current testing.
Regional operations leaders at 20-to-100-location groups are concrete, while budget and source stack need validation.
One cross-reference and no inbound connections provide limited convergence.
Confirmed single-location and multi-location categories, a clear regional operator, and no direct cross-location anomaly product found support the cockpit.
No production API was verified, cross-location normalization is hard, near-real-time claims depend on source freshness, employee and legal risks are high, causal classification is unsound, and incumbents can add anomalies.
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