

Bring real-time intelligence to every rack, bin, and floor location with vision AI for inventory cycle counting. Built for the operations where the count team walks the same aisles every week and still finds a pallet the WMS swore was in a different building, the year-end physical shuts down shipping for two days, and the variance report lands on the controller's desk with no way to tell a mis-pick from a mis-slot from a theft. Whether you're running a distribution center, a manufacturing stockroom, a retail backroom, or a third-party logistics site, Roboflow counts what is actually in each location from cameras on forklifts, drones, handhelds, and the rack itself, reconciles it against the system of record, and puts a photo behind every count and every variance.
Location Counts and Verification:
Capture Modes and Coverage:
Reconciliation, Variances, and Systems Integration:
Bring intelligence to every location today. Stop the lost pallet, the year-end shutdown, and the unexplained variance from becoming a write-off, an audit finding, or a customer order you could not fill from stock you actually had.
What is inventory cycle counting with Vision AI?
Inventory cycle counting with vision AI uses computer vision models on forklift-mounted cameras, indoor drones, handhelds, and fixed cameras to count what is physically in each rack, bin, and floor location, read location and pallet labels, verify SKU and quantity against the WMS or ERP, and flag variances with a photo attached. Every location gets counted as trucks and drones pass it, continuously, so cycle counts become a byproduct of normal operations, with a photo-backed history that supports SOX inventory controls, external audit observation, and ISO 9001 records.
Can Vision AI count cases on a pallet it can only see from the front?
The partial view is the hard case: a pallet in a rack shows one face and one side, cases are stacked in patterns that hide the back rows, shrink wrap adds glare, and the upper levels are seen from a forklift mast or a drone at an angle under uneven lighting. Deep-learning models trained on your actual products, pallet patterns, and capture angles learn what a full pallet of each SKU looks like from the front, infer the stack from the visible faces and the pallet pattern, read the license plate and location labels in the same frame, and report a count with a confidence. Where the view is not enough for an exact count, the model reports what it can verify, the location gets flagged for a closer capture, and the system of record is never overwritten by a guess. Your inventory control team sets the tolerance for when a count is accepted and when it goes to a person.
Does this satisfy our auditors and SOX inventory controls?
Yes. SOX (the internal control requirements for inventory as a material balance), the external audit standards that require auditors to observe physical counts, and ISO 9001 quality management each expect documented, repeatable counts with a traceable record. Vision-based counting produces exactly that: every count carries the image, location, timestamp, capture source, and the reconciliation result, and every variance shows what was seen and what the system said. Auditors can sample locations and see the evidence instead of watching a recount. Roboflow is the counting engine; your inventory control and finance teams own the count frequency, tolerances, and the variance investigation process that sits on top.
Can it integrate with our WMS, ERP, and count tools?
Yes. Roboflow Inference exposes a standard API and webhooks, so counts and variances flow into your existing systems: WMS platforms like Manhattan, Blue Yonder, SAP EWM, Oracle WMS, and Körber, ERP platforms like SAP, Oracle, NetSuite, and Microsoft Dynamics, and the forklift telematics, drone, and handheld systems that capture the images, through REST, webhooks, and direct database writes. A verified count posts as a confirmed cycle count, a variance becomes a task in the same queue your inventory control team already works, and every record carries location, SKU, quantity, capture source, timestamp, and the image, with a full audit trail behind every adjustment.