Reducing contamination risk events by 63% with AI Vision
Customer background
The Leadership Team at a Fortune 500 Food and Beverage Manufacturer supplying major retail brands wanted to accelerate digital transformation across its production and logistics operations.
Despite modern equipment and experienced teams, the organization lacked real-time visibility into what was happening on the shop floor. This was especially across safety risks, operational inefficiencies, and quality deviations. With high staff turnover, seasonal labor, and strict retailer requirements, maintaining consistent standards across shifts and sites was becoming increasingly difficult.
The company partnered with viso.ai to introduce AI Vision as a scalable, privacy-first layer of operational intelligence, crucually without replacing existing systems or disrupting production.
The business need
The leadership team was looking for practical starting points, not a full digital overhaul.
They needed a solution that could:
- Deliver quick wins to prove value
- Address safety, efficiency, and quality together
- Scale easily once proven
- Remain cost-efficient, privacy-preserving, and fully under their data control
The approach was to start small, validate outcomes, and then expand.
Challenges
Impact
Reactive safety management instead of prevention
Missed near-misses involving forklifts and vehicles
Inefficient inbound and outbound logistics processes
Quality rejections due to late detection of print or packaging errors
High investigation and rework costs
Resolution
The solution
Phase 1: Getting started (two use cases)
The pilot focused on two areas with immediate impact:
Safety: Vehicle and forklift monitoring
- Detection of speeding forklifts and vehicles
- Near-miss monitoring in loading docks, waste areas, and blind corners
- This helped address safety risks in environments with frequent new and temporary staff.
Efficiency: Packaging-line flow monitoring
- Detection of line stoppages, jams, and idle states
- Early insight into bottlenecks impacting throughput
- These use cases delivered fast, visible results, building internal confidence.
Phase 2: Scaling value (three additional use cases)
Following early success, three more use cases were layered on using the same platform and cameras:
Safety: LOTO compliance
- Monitoring Lock Out / Tag Out procedures during maintenance
- Reducing high-severity risk from unsafe equipment access
Operational efficiency: Logistics automation
- Vision-based scanning for inbound logistics
- Validation of the right pallet on the right truck after pick and pack
- Reduced returns, stockouts, and perishable waste
Quality: Automated quality checks
- Detection of print, label, and packaging defects
- Early identification of issues that could cause retailer rejections, especially critical for international shipments
“Starting with just two use cases helped us prove value quickly. From there, scaling was effortless. We used the same cameras, the same platform, and everything remained fully anonymized. The combination of ROI, scalability, and data control made AI Vision an obvious long-term investment for us.”
Director of QA & Operations, Global F&B Manufacturer
Business results
Scalable expansion and cost efficiency
By running all use cases on a single AI Vision platform, the manufacturer was able to:
- Scale from two to five use cases without additional infrastructure
- Avoid fragmented point solutions
- Reuse configurations and models across sites
- Keep operational overhead low
- Maintain GDPR compliance and worker trust
- Achieve strong ROI through risk reduction, efficiency gains, and avoided rework
Scaling and future phases
With a proven foundation in place, AI Vision is now being rolled out to additional facilities in under six weeks per site.
Planned next phases include:
- Predictive maintenance (e.g. conveyor belt wear, bottle breakage risk)
- Advanced quality inspection
- Broader warehouse and yard automation