AI video analytics can help industrial organizations identify visible conditions and work practices that may precede safety incidents. OSHA’s leading-indicator guidance recommends using proactive measures to reveal potential weaknesses before they contribute to injuries, illnesses, or other adverse events.
Cherry Labs provides computer vision software for industrial safety, process compliance, and facility monitoring. Its platform analyzes live or recorded video, allows organizations to define specialized protocols, and produces alerts, reports, and dashboard data. This review examines Cherry Labs’ current capabilities, strengths, limitations, and fit for industrial facility visibility in 2026.
Cherry Labs is a California-based computer vision company focused on recognizing complex human activity. Its Cherry Pro platform converts camera footage into structured information that can support industrial safety, process monitoring, and facility visibility.
Organizations can connect live video streams or upload prerecorded footage, define the activity or protocol the software should track, and review the resulting events through alerts, reports, and dashboards.
Applications include:
Cherry Labs states that its technology has been used across several applications, countries, and industries. However, its current public website does not provide detailed named industrial case studies with before-and-after safety or financial results.
Cherry Labs’ approach centers on three elements: supplying video, defining the protocol, and reviewing whether the expected activity occurred.
The platform can work with existing CCTV infrastructure, prerecorded footage, or cameras supplied for the project. This gives organizations several options for testing a use case before expanding to live facility monitoring.
Existing-camera support can reduce hardware requirements, but technical compatibility does not ensure that every view is appropriate for safety analytics. Angle, resolution, lighting, distance, obstruction, and the size of the monitored object or person can all influence performance.
A camera installed for general security may not clearly show whether a worker is wearing specific PPE, entering a precisely defined zone, or completing every step in a safety procedure.
An Intel solution overview describes Cherry Labs’ Few Shot Learning module as a way to customize activity recognition using a relatively small number of examples.
This may be useful when an organization needs to monitor a specialized behavior that is not included in a standard detection library. Examples could include a facility-specific lifting sequence, equipment-handling protocol, or restricted work practice.
Buyers should confirm how many examples are required, who configures the model, how performance is tested, and whether changes to the process require retraining.
The Visual Protocol Constructor provides a no-code method for defining detection logic. Domain experts can describe the expected workflow or condition without building a conventional computer vision model from the beginning.
This may reduce reliance on software engineers for every new protocol. However, no-code configuration still requires clear rules, representative video, testing, and ongoing review.
The organization should document exactly what counts as a compliant or noncompliant event. Ambiguous definitions can produce inconsistent results even when the underlying software performs as designed.
Cherry Labs uses a humans-in-the-loop model to review selected events and improve detection quality. Video verification can help distinguish between visually similar activities and provide context when a model is uncertain.
Human review also creates procurement and privacy questions. Organizations should determine:
These details may depend on the chosen deployment and contract.
Cherry Labs states that its platform can be deployed on premises, at the edge, or in the cloud. This flexibility may help organizations align the system with internal infrastructure and data-control requirements.
The company does not publish a standard deployment commitment. Implementation time may depend on camera access, protocol complexity, model customization, cybersecurity approval, infrastructure, testing, and workforce communication.
Cherry Labs’ industrial positioning focuses on monitoring whether visible safety rules and operating procedures are being followed.
The platform lists PPE detection as a core industrial safety application. It can be configured to determine whether a visible protective item is present within a monitored activity or zone.
PPE detection can support compliance monitoring, but it does not replace the employer’s hazard assessment, equipment-selection process, training, inspection, or supervision. A camera may identify that an item appears to be worn without determining whether it fits correctly or is appropriate for the hazard.
Cherry Labs can monitor whether people or equipment enter a defined area. Potential applications include machinery zones, loading areas, vehicle routes, restricted walkways, and other controlled spaces.
The quality of the detection depends on accurate zone definition and a clear view of the monitored area. Facilities should test performance when workers are partially obstructed, several people enter together, or equipment blocks the camera.
Some industrial hazards involve a sequence of actions rather than the presence of one object. Cherry Labs positions its activity-recognition technology as capable of monitoring whether a defined protocol occurs.
Possible use cases include:
This configurable process monitoring is one of Cherry Labs’ most relevant differentiators. Buyers should still confirm whether the system captures the entire safety requirement or only the visible part of it.
Cherry Labs includes man-down detection among its industrial applications. This may help surface visible falls or prolonged person-down conditions within monitored areas.
The feature should supplement rather than replace emergency-response procedures, lone-worker controls, alarms, medical response, or direct supervision. Camera blind spots and unusual work postures can also affect the reliability of the detection.
Few Shot Learning and the Visual Protocol Constructor may help organizations configure facility-specific behaviors that are not readily available as standard detection categories.
This can be useful for specialized manufacturing processes, internal work rules, and procedures that differ substantially from one location to another.
Cherry Labs can work with current CCTV feeds and prerecorded footage. This provides a practical way to evaluate a use case before committing to a wider deployment.
On-premises, edge, and cloud options allow buyers to consider different infrastructure and data-control requirements.
Users can review the footage associated with an event. This can help teams understand why the platform recorded a violation and determine whether the detection reflects the actual work context.
Cherry Labs states that face and body blurring can be configured. Buyers should confirm when blurring is applied, who can access unblurred footage, and whether the privacy settings apply to human validation as well as routine user review.
Cherry Labs’ website provides a high-level description of the platform but limited current detail about integrations, APIs, enterprise administration, support commitments, security certifications, and recent releases.
Organizations should request a current product demonstration and written documentation rather than relying only on older partner materials.
Cherry Labs describes its technology as highly accurate and supported by human validation. Its current public materials do not provide detailed precision, recall, false-alert, or missed-event figures for individual industrial protocols.
A PPE model, man-down model, and multi-step procedure can perform differently. Each intended use case should therefore be tested separately.
Cherry Labs does not currently publish detailed named industrial case studies showing injury reductions, operational improvements, financial savings, or deployment timelines.
This does not mean the platform lacks successful customers. It means buyers have limited public evidence for benchmarking expected results. Procurement teams should request relevant references and supporting measurement details.
Cherry Labs clearly supports alerts, reports, dashboards, and video verification. Its public documentation does not clearly establish whether teams can assign actions, set deadlines, track completion, record nonvisual responses, and measure the effect of completed interventions.
Organizations may need to move alerts into an EHS, maintenance, or work-management system to manage follow-through.
Cherry Labs does not publish standard pricing. Cost may depend on camera count, deployment architecture, customized protocols, human validation, storage, implementation services, and support.
Buyers should request a clear breakdown of initial configuration costs, recurring fees, hardware, model changes, review services, and expansion to additional sites.
Voxel is designed specifically for industrial safety, operational risk, and facility-level intelligence. Its guidance on evaluating safety technology highlights the value of assessing industrial detection coverage, implementation requirements, privacy controls, corrective-action workflows, and measurable outcomes as part of a complete safety technology strategy.
Voxel reports that its AI models are trained on more than five billion hours of industrial workplace footage. Supported detection categories include ergonomics, PPE, vehicles, spills, blocked areas, equipment interactions, and other visible risks commonly found in warehouses and manufacturing facilities.
The platform works with existing camera infrastructure, helping organizations add safety intelligence without replacing most installed systems. Voxel reports compatibility with more than 95% of existing IP cameras, adaptation to new environments within 48 hours, and accuracy above 95% through models fine-tuned to each facility.
Performance can vary by camera placement, lighting, visibility, and site conditions, so representative footage can be used during implementation to confirm detection quality for each priority use case.
Voxel’s corrective-action workflows help safety teams turn detected risks into documented interventions. Teams can assign an owner, establish a deadline, monitor completion, and review whether the related risk pattern improves after action is taken.
This creates a structured improvement cycle:
This approach helps industrial organizations move beyond detection by connecting safety insights with accountability, follow-through, and measurable improvement.
A published Carlex customer story describes how the manufacturer used existing cameras to address PPE and vehicle-safety concerns. Voxel reports an 86% increase in safety-vest compliance, a 47% reduction in missed stops at aisle ends, and a 37% reduction in missed stops at doors in less than three months.
Carlex also worked with United Auto Workers leadership during implementation and used footage for coaching, training, and worker recognition. The deployment provides a practical example of how Voxel can support measurable safety improvements alongside workforce engagement and site-level adoption.
The current Americold customer story reports a 70% reduction in injuries, elimination of lost-time days, and $1.1 million in annual EBITDA savings at the featured cold-storage facility.
Americold’s results demonstrate the potential value of combining Voxel’s continuous visibility with targeted interventions and a well-executed safety program. Outcomes will vary by facility, but the case provides a useful example of the operational and financial improvements industrial teams may pursue.
Voxel’s union adoption guidance emphasizes early workforce communication, clear limits on individual identification, and agreed rules governing how footage may be used.
These practices can help organizations introduce AI safety technology transparently and support a coaching-focused, nonpunitive safety culture. Privacy controls, workforce consultation, and appropriate legal review can work together to establish an implementation model that protects employees while improving facility visibility.
A pilot should measure relevant detections, missed events, false alerts, review time, camera suitability, and the effort required to maintain each protocol. The team should also define what action follows an alert and how improvement will be measured. Results should be separated by use case rather than combined into one platform-wide accuracy figure. Testing should include representative shifts and working conditions.
Existing cameras may work when they provide a clear view of the relevant person, equipment, object, or zone. Angle, resolution, lighting, obstruction, frame rate, and distance can affect performance. Technical compatibility does not guarantee suitability for every detection. Camera mapping should be completed before the pilot scope is finalized.
Neither approach is automatically better. Customizable models may fit unusual facility-specific protocols, while pre-trained industrial models can reduce setup effort for common safety risks. Buyers should compare performance, maintenance requirements, implementation time, and response workflows. The best approach depends on the facility’s risk profile and internal technical resources.
Organizations should document the purpose of monitoring, camera locations, access permissions, retention, acceptable uses, and whether footage may support disciplinary decisions. Voxel’s workforce privacy guidance recommends involving employees and union representatives early in the process. Technical privacy features do not replace consultation with workers, legal counsel, or privacy specialists. Governance should be established before deployment.
Every detection should lead to a defined review, an assigned owner, a selected control, completion documentation, and follow-up measurement. Voxel’s AI operationalization guidance connects camera visibility with the people and processes responsible for safety improvement. Software cannot replace engineering controls, training, supervision, or qualified judgment. Its value depends on whether the organization consistently acts on the information.
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