Reap AI cross-matches CCTV camera data with POS transactions in real time to detect internal fraud, void manipulation, and revenue leakage — without changing how your team works. The problem it solves Internal revenue leakage — through void manipulation, receipt reprinting, table switching, unauthorized discounts, and cash handling gaps — costs the average restaurant 1–4% of monthly revenue. These losses don't appear as errors in your POS reports. They look like normal transactions, because someone inside the system made them look that way. Traditional audits catch roughly 5% of these incidents. Reap AI catches them in real time. How it works Reap AI runs a continuous three-way cross-match between: - Camera data — headcount per table, timestamps of customer arrivals and departures - POS data — transactions opened, voided, discounted, reprinted, or transferred - Operational rules — your own business logic for promotions, staff access, and service flow When the data doesn't match — a table closed in POS but camera shows customers still seated, a void executed moments after a customer paid cash, a QR code reused across two seatings — Reap AI flags it instantly and sends an alert to the auditor. What customers see One buffet chain reduced daily fraud incidents from 28 to 1 within 3 months of deployment. Net profit increased 17% in the same period. No new hardware was required — Reap AI connected to the cameras and POS already in place. Who it's for - F&B groups and restaurant chains (5+ branches) - Retail and convenience store operators - Manufacturing and warehouse operations - Corporate finance and compliance teams running internal audits Compliance The Leakage Audit framework is built on ACFE (Association of Certified Fraud Examiners) methodology. Data handling is fully compliant with Thailand's PDPA (Personal Data Protection Act B.E. 2562). Audit documentation can be used to support CAC (Thai Private Sector Collective Action Against Corruption) certification.