Rima AI (formerly Garage)

Rima AI launched with a deliberately narrow MVP focused on a single reconciliation workflow—bank-to-ledger matching—rather than attempting to handle the full spectrum of accounting reconciliations. ​​‌‌‌‌‌‌‌​‌‌​​‌​​​​​​‌‌​‌‌‌​​​‌‌The team shipped their first version in eight weeks, prioritizing core AI accuracy over feature breadth. They deliberately excluded multi-entity reconciliation, custom rule engines, and ERP integrations, betting that solving one problem exceptionally well would validate demand faster than a feature-complete product. This constraint-driven approach paid immediate dividends. Early accountants using Rima reported completing reconciliations in 15 minutes instead of three hours, generating word-of-mouth validation within their target firms. The narrow scope also meant their AI model could be trained intensively on bank reconciliation data, achieving higher accuracy than competitors attempting broader solutions. Within three months, usage metrics showed 70% of invited users returning weekly, signaling strong product-market fit. This focused execution allowed Rima to raise their Series A while competitors were still building feature lists, demonstrating that ruthless prioritization often outpaces comprehensive ambition in B2B software.