--- title: "Physical Operations Analytics | Fortune AI" description: "Fortune AI applies Physical AI and video analytics to existing camera feeds, producing shift-by-shift metrics for labor, flow, utilization, and throughput" canonical: https://www.fortuneai.app/ language: en --- # The application layer for physical operations Tell Fortune AI what to measure. It analyzes your existing cameras and returns the result every shift with supporting footage attached [Request a demo](https://www.fortuneai.app/book/) [How it works](#how-it-works) Live measurement Weekly forklift flow Running You ask Measure every loaded forklift trip this week - 01 Build Movement · Loaded or empty · Across camera views - 02 Run 12 existing camera feeds - 03 Deliver Report · API · MCP Camera events Live 10:42:18 Cam 03 Loaded trip 10:42:21 Cam 08 Same forklift matched Loaded trips this shift 184 Investors and ecosystem     From question to live analysis ## Ask once. Get the same result every shift Fortune AI learns what to count from sample clips your team approves, then runs the analysis on the cameras already in place A user defines a weekly forklift-flow measure, Fortune AI builds and validates the analysis, runs it on existing cameras, and delivers the result through Agent, report, API, or MCP - 01 Define Plain-language request - 02 Build What to track - 03 Validate Team-approved clips - 04 Run Existing cameras - 05 Deliver Report · API · MCP 10× ### faster to deploy Describe the metric, approve a few sample clips, and run it across existing camera feeds Measurement live 12 feeds processing First operating output ## See where paid time goes Compare work, waiting, walking, and cleaning time with the output from the same shift Fortune AI Agent Shift review · Line 2 Ready Illustrative answer ### Outbound staging caused the largest delay Waiting accounted for 1h 46m of paid time during Shift A - **Paid time:** 6h 42m - **Output:** 428 cases - **Productivity:** 64 cases / paid hour  Camera 03 · 10:42 12 footage moments attached Available every shift Agent · Report · API · MCP Full scene checked 90% ## fewer false alerts Fortune AI follows who is present, what the equipment is doing, and how the activity unfolds before notifying the operator Candidate event · Dock 3 Person near a moving forklift - 01 Detect Person and forklift - 02 Follow Forks align with the pallet - 03 Decide Expected loading sequence Context verified Normal loading activity No alert sent Where to start ## Start with repeated work and a result you already count [View all industries](https://www.fortuneai.app/industries/) [ Warehouse Warehouse and dock flow Optimize forklift utilization, reduce staging congestion, and slash dock dwell time](https://www.fortuneai.app/industries/warehouse-operations/) [ Manufacturing Manufacturing process flow Detect line cycle delays, accelerate bottleneck recovery, and capture repeat exceptions instantly](https://www.fortuneai.app/industries/manufacturing-operations/) [ Airport terminals Queue flow Queue growth, lane response, and passenger wait time](https://www.fortuneai.app/industries/airport-operations/) Founder story ## Why Fortune AI exists  Andrew Chen Founder & CEO Start with your operation ## See what your cameras can measure [Request a demo](https://www.fortuneai.app/book/)