Case Study: a US retail chain achieves faster, more accurate financial reconciliation with Unframe AI

US Retail Chain cuts manual reconciliation hours by 80% with Unframe

The US Retail Chain faced a significant challenge in automating its financial reconciliation across hundreds of locations. Despite a straightforward transaction model, its scale, multiple POS systems, and fragmented data in PDFs and CSVs required extensive manual work in Excel. This process slowed financial closes, increased labor costs, and eroded trust in reported numbers. Standard reconciliation software was not viable, leading them to seek a solution from Unframe.

Unframe deployed an AI-powered reconciliation solution that automated the extraction and abstraction of multi-system transaction data. This implementation resulted in a 97% reconciliation accuracy rate validated on over 100,000 transactions and an 80% reduction in manual reconciliation hours. For the US Retail Chain, the solution from Unframe delivered projected annual productivity gains of $500K.