/ Retail · Food & Beverage /
Real-Time Ingredient Detection — Missing Items Caught Before the Customer
100%
Orders Monitored in Real Time
Eliminated
Revenue Loss from Incorrect Orders
/ the problem /
What wasn't working
In a high-volume fast food operation, orders move fast. Assembly-line workers put together multiple items simultaneously, under time pressure. Missing ingredients are inevitable at scale — and costly:
- Customer receives the wrong order, complains, or returns the item
- The business refunds or replaces the order — direct revenue loss
- Reputational damage — social reviews, customer retention
- No way to catch it in real time — by the time the customer notices, the order is out of the kitchen
- Manual QC requires a dedicated person on the line, slows assembly, and still isn't 100% reliable
/ the solution /
What Tensorbot built
Tensorbot deployed a computer vision QC system on the assembly line — a camera positioned above the assembly station that monitors each order in real time as it is assembled.
Ingredient Detection
- The vision model is trained on images of correctly assembled orders — it learns what "complete" looks like for each menu item
- As each ingredient is added, the model confirms its presence and correct placement
- If an ingredient is missing when the order should be complete, the system flags it immediately
Real-Time Alert
- Alert fires before the packaging step — the problem is caught while the order can still be corrected
- Visual and/or audible alert — minimal disruption to the assembly workflow
- The team member adds the missing ingredient and the order is re-checked
Management Visibility
- Dashboard showing order accuracy rate, most common missing ingredients, and time-of-day patterns
- Identify which SKUs or shifts have the highest error rates
- Data drives training and process improvement, not just in-the-moment correction
/ stack /
Technologies used
Computer VisionReal-Time Object DetectionDeep Learning
/ results /
What changed
100%
Orders Monitored in Real Time
Eliminated
Revenue Loss from Incorrect Orders
- Errors caught on the line, before orders reach customers
- Order accuracy visible in real time and historically
- QC no longer depends on a dedicated human checker
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