MissionCommerceAI by eVo.Ninjas

Retailers hold extensive sales, stock, and cash data, yet manage stores by product SKUs rather than customer missions. Operational leaks—such as cash variance, inventory shrinkage, and expired stock—are frequently discovered long after financial losses occur. Static mock data cannot simulate these dynamic store conditions. To solve this, we built **Sim.PesoWeb**, a synthetic retail world where simulated merchants register on our production PesoWeb POS/ERP, configure branches, hire staff, and process transactions from persistent virtual customer personas. The simulator operates in three modes: **Quick Sim** (scaled fast-forwarding), **Day-to-Day** (real API transactions), and **Scenario Sim** (targeted anomaly injections). On top of this world runs **MissionCommerce AI**. It identifies customer shopping missions, forecasts demand using statistical/ML models, recommends commercial actions, and investigates operational leaks. Every injected scenario is benchmarked across POS alerts, AI detection, or missed incidents. Both the PyTorch models and the vLLM investigator run on AMD Instinct accelerators via ROCm with live performance monitoring.