CASE STUDY: REVENUE RECONCILIATION BREAKDOWN
Industry: Multi-location Hospitality Brand (3+ locations, high transaction volume)
The Situation: Reporting looked clean on the surface. Revenue was growing, and systems were in place across production, payments, and QuickBooks.
But the numbers didn't fully reconcile. Leadership was making pricing, purchasing, and expansion decisions on data that couldn't be validated end-to-end.
$400K+
in unattributed costs identified
SKU-Level Margin
made measurable across locations
3 Systems
unified into one validated flow
WHAT WAS BROKEN
• COGS not attributed at the transaction level
• Product costs estimated instead of sourced from Production data
• Production, payments, and accounting were disconnected across systems
• Revenue and margin reporting did not tie across systems
• Different teams were working from different versions of the numbers
WHAT GOT REBUILT
Rebuilt the reconciliation layer across production, payments, and QuickBooks.
Created a validated transaction-level model connecting cost, payment, and accounting data so every sale reconciled end-to-end across all systems.
Standardized reporting definitions and business logic across teams.
THE RESULT
• $400K+ in previously unattributed costs surfaced
• Margin became measurable and reliable across locations
• Production, payments, and accounting reconciled end-to-end
• Manual reconciliation work significantly reduced
WHAT CHANGED
Leadership could trust the numbers with confidence.
Pricing, purchasing, and expansion decisions shifted from estimates and partial data to validated transaction-level performance.
CASE STUDY: PAYMENT RECONCILIATION REBUILD
Industry: Enterprise Retail/E-Commerce
The Situation: A multi-location retail and e-commerce brand processed millions of transactions annually through Stripe, but payment and ERP data were never reliably reconciled.
Finance had no dependable way to verify that every payment was recorded correctly.
Monthly close relied on manual reconciliation across massive datasets, with discrepancies surfacing slowly and inconsistently.
20M+
rows of Stripe data reconciled
99%
payment matching accuracy
~20 hours
manual work eliminated monthly
WHAT WAS BROKEN
• No system-level reconciliation between Stripe and ERP
• Payment discrepancies went undetected across millions of rows
• No validation layer - exceptions discovered during or after close
• Manual matching created close delays and audit exposure
• No real-time visibility into reconciliation status
WHAT GOT REBUILT
Rebuilt the payment data and reconciliation infrastructure from the ground up.
Connected Stripe, ERP, and POS data into BigQuery and modeled transaction-level reconciliation logic to validate payment, ERP, and POS records at scale.
Built a SQL-based validation layer that reconciled every transaction across systems before it reached finance close.
Implemented reporting and exception-monitoring dashboards so finance could track reconciliation health in real time.
THE RESULT
• 20M+ transaction records reconciled across Stripe and ERP in BigQuery
• 99% payment matching accuracy achieved via SQL validation layer
• ~20 hours of monthly manual reconciliation work eliminated
• Exceptions surfaced in real time instead of discovered during close
WHAT CHANGED
Finance moved from fragmented source data and manual exception handling to a centralized, validated transaction model powering a repeatable close process.