Unilever · Google Cloud
15-35% increase — retailer sales
Independently reportedOther · Other · Classical ML · Scaled
Other
JKK Pack · Lumana
Vendor-reportedManufacturingIn productionComputer vision
Vendor-reported. The customer is named and the numbers are quoted from the source page, but the account comes from the vendor. No independent confirmation.
Reported by lumana.ai (vendor self-report). Checked against the source page on 2026-09-06. 3 of 3 figures below appear on that page word for word.
The problem
Incident reviews were slow and manual, undermining safety and uptime. The legacy system didn’t detect production-floor accidents or safety violations.
What was deployed
- Vendor
- Lumana
- Products
- Lumana
- Technique
- Computer vision
- Build or buy
- Bought off the shelf
- Deployment
- SaaS
- Data used
- video data from existing cameras
- Scale
- expansive camera network
What changed
Each row is quoted from the source. Figures we could not find on the page in those words are marked — they are kept, not deleted, so you can judge them.
investigation speed
“90% faster investigations”
Quoted word for word from the source · round percentage, no baseline given
<1 secalert response time
“<1 sec alert response time”
Quoted word for word from the source
1 monthtime to achieve ROI
“1 month to achieve ROI”
Quoted word for word from the source
None (existing cameras reused)Camera replacements needed
“Use existing cameras without costly replacement”
Quoted word for word from the source
ROI as stated: positive ROI within the first month alone
“Lumana has become an essential part of how we run our business. By bringing intelligence to our video systems, we can ensure a safer workplace for our people and a more reliable operation for our customers. It gives us the confidence that our facility is protected and performing at its best, every day.”
Difficulties and limits
- Legacy CCTV/NVR was difficult to use and offered no real intelligence
- System didn’t detect production-floor accidents or safety violations
- Investigations were slow and manual, requiring teams to scrub hours of footage
Sources
- lumana.ai — vendor self-report
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