NAGARE AI keeps track of every stock unit on the rack and in your system records, so the two always tell the same number. No stockouts, no overstocking, no duplicate stock hiding in the wrong location — just accurate inventory that protects revenue and frees working capital.
Every unlogged movement widens the gap between what your system says and what’s actually on the rack. That gap surfaces at the worst moment, a picker at an empty location, a reorder for stock you already own, a line waiting on material the system swore was in stock.
Our closed-loop system, NAGARE™, uses real-time video analytics to count stock continuously, catch discrepancies as they happen, and keep your WMS and the warehouse floor telling the same story.
See it running on your site
Tell our engineers how you count today and they will walk you through implementation, timelines and cost for your site.
Learn More - Talk to our expertSee it running on your site
Tell our engineers how you manage locations and they will walk you through implementation, timelines and cost for your site.
Learn More - Talk to our expertInventory monitoring runs on the cameras you already have across your racks. No pilot line, no new hardware to install. We have mapped this deployment before, which is what makes it fast now.
We walk your storage areas, confirm camera coverage, and scope the rack and SKU list for record-level accuracy.
The model trains on your actual stock, racks, and movement patterns, not a generic library. Training uses 120 minutes of footage.
Counting runs alongside your existing process first, then is confirmed accurate before your records depend on it. Deployment takes 4 weeks, with ROI payback in 6–12 months.
Bring your worst count variance and your busiest aisle. We will scope a pilot on your site.
Comparison
When errors are caught
At the next count, or at the customer
Scheduled scans, fixed gates
As stock moves, in real time
What it sees
Whatever the checker notices
Barcodes, fixed positions only
Stock and operator actions
New site or SKU setup
Recounting, weeks
Reprogramming, weeks
New SKUs configured in minutes
Hardware
Count sheets and handheld scanners
Drones, robots, proprietary rigs
Runs on your existing cameras
Traceability
Paper logs, if kept
Scan log per pass
Video-backed record for every movement
Record trust
Hope
As of the last scan
Continuous, every shift
Discover how industry leaders run on accurate, trusted stock records with Jidoka’s AI vision systems.
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Inventory record accuracy is the degree to which the stock recorded in your system matches what is physically on the rack, measured at SKU-location level. It is calculated as (accurate records ÷ records counted) × 100. Records drift through mis-putaways, unlogged movements, shrinkage, and data-entry errors, and most operations only measure the damage during periodic counts. Vision AI measures it continuously.
World-class operations target 95 to 99%+ inventory record accuracy at SKU-location level, yet audits routinely find reality far below it; Auburn University’s RFID Lab measured average retail inventory accuracy at just 63%. Continuous camera-based counting holds accuracy at the top of the range because discrepancies are corrected the day they occur, not discovered at the next scheduled count.
Yes. Camera-based AI counts stock continuously from live video, replacing scheduled manual cycle counts, and Gartner predicts that by 2027, half of all companies with warehouse operations will use AI-enabled vision systems to do exactly this. Every stock movement updates the record in real time, so the count is always current and the floor never stops for counting.
A WMS records the transactions people tell it about. NAGARE verifies what physically happened, catching the movements that never got logged, then syncs verified counts back to your SAP, WMS, or ERP so the system of record finally matches the rack.
No. NAGARE runs on the CCTV cameras you already have. There is no drone fleet to manage, no robot traffic in your aisles, and no new capture hardware to buy or maintain. Once trained, the same system scales across stores, plants, and sites.
Deployment starts with a site audit and camera mapping, then the model trains on your actual SKUs and racks. Counting runs alongside your existing process until verified, typically weeks rather than a six-month pilot, and accuracy compounds from day one because discrepancies stop accumulating.
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