AI Retail Theft Solution Build

I’m launching an ultra-lean AI solution that helps medium-sized retail chains curb shrinkage without demanding a heavy upfront investment. The first priority is the core system itself: a computer-vision workflow that performs real-time monitoring, triggers instant alert notifications and feeds events into a lightweight analytics dashboard for daily and weekly loss-prevention reports.

I’d like you to leverage proven open-source frameworks—think YOLO, OpenCV, TensorFlow or similar—so the software can run on inexpensive IP cameras and modest edge devices. From there, I need the entire go-to-market package wrapped around it: a clear step-by-step marketing playbook that speaks to operations directors and loss-prevention managers at mid-size chains, an onboarding guide that shows them exactly how to wire up cameras, create store profiles and manage user roles, plus a concise explainer video (≈90 seconds) that visually walks prospects through detection, alerting and reporting.

Deliverables
• Working prototype with real-time detection, SMS/email alerting and a dashboard that summarizes incidents and basic data analytics
• Deployment manual covering single-store and multi-store rollouts, hardware specs and bandwidth requirements
• Marketing plan broken into pre-launch, pilot, and scale phases with channel tactics, messaging angles and KPIs
• Customer setup guide formatted as a quick-start PDF
• Short demo video (screen-capture + voice-over) showing the system in action
• Pricing breakdown: suggested monthly SaaS tiers and per-store cost model with margin notes

Acceptance criteria: prototype must flag predefined suspicious actions in under two seconds, send a verifiable alert, log the event to the dashboard, and export a CSV summary report.

If you’re confident you can craft both the tech and the commercial wrapper while keeping costs razor-thin through smart use of open-source tools, I’d love to see your approach and timeframe.