Real-World Impact of SeeWise AI on Manufacturing Operations
Explore how industrial teams use SeeWise AI to improve safety, compliance, and operational reliability across real shopfloor environments.
Machine Zone Safety
Tool & Component Identification
Truck Loading Zone Safety
Collaborative Robot Safety
Machine Zone Safety with AI-Driven PLC Interlock
100% real-time machine safety automation
Zero machine operation during human presence
Full audit trail for safety and compliance events
A leading beverage manufacturing facility needed a reliable way to prevent human entry into active machine zones during automated operations. High-risk areas such as palletization and stretch-wrapper zones required immediate intervention to eliminate accidents caused by unintended machine motion.
The Challenge
Automated packaging machines continue operating unless explicitly stopped, creating serious safety risks when operators enter machine zones. Existing safety processes relied heavily on manual lockout–tagout methods, which were prone to delays and human error.
Key challenges included:
- No real-time detection of human presence
- Risk of injury from unintended machine motion
- Manual safety protocols frequently bypassed
- Poor coordination between operator movement and machine logic
The SeeWise AI Solution
SeeWise deployed an AI-driven safety system using existing CCTV cameras and direct PLC integration to enforce real-time human–machine interlocks.
The solution enabled:
- AI-based detection of human presence in restricted zones
- Continuous monitoring of machine key signals
- Instant PLC signal writes to halt machine operation
- Automatic machine resume once the area is verified safe
- All events were centrally logged and monitored through the SeeWise platform.
How It Works
- SeeWise AI processes camera feeds covering machine entry points and operator zones
- The AI system detects human presence in active machine areas in real time
- A stop signal is sent directly to the PLC when a safety breach is detected
- Machine motion halts immediately to prevent hazardous operation
- Operations resume only after the zone is verified as clear
Impact
- Prevented machine operation during human presence
- Enforced strict human–machine interlock compliance
- Created a complete audit trail of entry and stop events
- Achieved 100% real-time safety automation on the shopfloor
Business Value
- Reduced near-miss incidents and injury risk
- Improved SOP adherence and safety compliance
- Strengthened safety culture across shifts
- Minimized downtime caused by safety violations
Case Study Snapshot
- Category:
Machine Zone Safety - Industry:
FMCG - Impact:
100% real-time machine safety automation
Industrial OCR for Metallic Tools & Components
High-accuracy OCR on reflective metal surfaces
Automated part identification from camera streams
Eliminated manual logging and tracking errors
In heavy manufacturing and tool management environments, identifying tools and components often depends on engraved or embossed text on metal surfaces. Glare, scratches, and lighting variations make manual reading and conventional OCR unreliable. A robust, automated OCR solution was required to ensure accurate identification and traceability of metallic parts.
The Challenge
Conventional OCR systems are not designed for reflective or irregular metal surfaces commonly found on shopfloors, leading to errors and operational delays.
Key challenges included:
- Low OCR accuracy due to glare and surface texture irregularities
- Manual identification causing delays and human errors
- Limited integration with MES / ERP systems
- High cost of alternate hardware solutions such as RFID or laser scanners
- Quality and maintenance issues caused by mis-tracking of tools
The SeeWise AI Solution
SeeWise deployed a custom AI-driven OCR system purpose-built for industrial metal surfaces. The solution combined surface-aware vision intelligence with a robust OCR pipeline to reliably read engraved and embossed characters under real shopfloor conditions.
The solution enabled:
- AI models optimized for reflective and textured metal surfaces
- Adaptive illumination correction and contrast enhancement
- Accurate recognition of faint, distorted, or partially damaged engravings
- Structured data output ready for production systems
How It Works
- SeeWise AI processes camera feeds capturing metal tools and components
- AI preprocessing removes glare, noise, and enhances contrast on reflective surfaces
- The OCR engine interprets engraved or embossed alphanumeric characters
- Validation logic improves recognition accuracy
- Structured data is pushed directly to MES / ERP systems or databases
Impact
- Automated part identification directly from camera feeds
- Improved traceability and tracking accuracy
- Eliminated manual logging and data entry errors
- Reduced inspection time and operational downtime
Business Value
- Lower operating cost compared to RFID or laser-based systems
- Stronger quality control and digital traceability
- Reduced operator dependency
- Improved productivity through automated ID capture
Case Study Snapshot
- Category:
Tool & Component Identification - Industry:
Automotive Components Manufacturing - Impact:
High-accuracy automated part identification
Safety Belt Compliance Monitoring for Truck Dispatch
Automated safety belt verification before truck dispatch
Real-time detection of non-compliance
Eliminated manual inspection errors
In logistics and loading yard environments, ensuring that all cargo is securely fastened before truck dispatch is critical. Improperly fastened safety belts can cause load displacement during transit, leading to product damage, driver risk, and safety incidents. A real-time automated system was required to verify belt fastening and eliminate inconsistencies caused by manual inspections.
The Challenge
Manual safety belt verification during outbound loading was slow, inconsistent, and heavily dependent on human vigilance, creating gaps in compliance and accountability.
Key challenges included:
- Manual inspections prone to delays and oversight
- No real-time alerting for missing or loose belts
- Inconsistent fastening practices across operators and shifts
- No centralized system to track violations or audit evidence
The SeeWise AI Solution
SeeWise deployed an AI-based vision system to automatically verify safety belt fastening before truck dispatch. Using camera-based detection and real-time analytics, the system ensured compliance checks were enforced without slowing down operations.
The solution enabled:
- AI models trained to detect belt presence and fastening status
- Strategic camera placement at loading lane exit points
- Real-time alerts when non-compliance was detected
- Centralized logging of compliance events with visual evidence
How It Works
- SeeWise AI analyzes camera feeds at loading exits to monitor cargo and belt fastening
- The AI system detects the presence and fastening status of safety belts
- Edge-based processing enables real-time inference
- Instant alerts are triggered when belts are missing or improperly fastened
- Compliance logs are stored with timestamps and visual evidence
Impact
- Automated safety belt compliance checks before dispatch
- Eliminated manual inspection errors
- Reduced risk of cargo shifting during transit
- Improved dispatch quality and safety standards
Business Value
- Faster, more consistent outbound operations
- Stronger enforcement of safety SOPs
- Improved accountability across shifts
- Complete audit trail for compliance and investigations
Case Study Snapshot
- Category:
Truck Loading Zone Safety - Industry:
Automotive Components Manufacturing - Impact:
Automated belt verification before dispatch
AI-Powered Safety Control for Collaborative Robots
Real-time human detection around cobots
Instant robot halt using edge-based safety relays
Safer human–robot collaboration on the shopfloor
Collaborative robots operate in close proximity to humans in modern manufacturing environments. While designed for shared workspaces, high-speed operations in areas such as palletization still pose safety risks when humans enter active robot zones. An intelligent, real-time system was required to detect human presence and immediately trigger robot control actions without relying solely on manual intervention.
The Challenge
Despite being collaborative by design, cobots operating at production speeds require additional safeguards to prevent contact injuries during manual intervention.
Key challenges included:
- Accidental entry into active robot movement paths
- Lack of direct signal-level interlock between AI detection and control systems
- Delays in stopping robot motion during manual intervention
- Dependence on emergency stops and physical sensors alone
The SeeWise AI Solution
SeeWise deployed an edge-based AI safety system to monitor cobot workspaces and enforce real-time control through safety relays. The solution enabled immediate robot response when human presence was detected, without introducing operational latency.
The solution enabled:
- AI-based human detection within cobot operating zones
- Local edge processing for low-latency decision making
- Real-time RS-485 output to trigger safety relays
- Automatic robot halt and safe resume once the area is clear
How It Works
- SeeWise AI processes camera feeds covering entry paths and cobot workspaces
- The AI system detects human presence within the active robot operating zone
- Inference is processed locally on the edge device for low-latency response
- An RS-485 signal is sent to the safety relay
- Robot motion halts instantly and resumes only when the area is safe
Impact
- Enabled safe human–robot collaboration
- Immediate response to human presence in robot zones
- Eliminated risk of contact injuries during palletization
- Fully automated safety control via edge device and safety relay
Business Value
- Reduced injury risk in collaborative operations
- Improved compliance with industrial robot safety standards
- Faster reaction times through real-time signal intelligence
- Avoided downtime and liability from safety incidents
- Cost-efficient integration with existing safety relay systems
Case Study Snapshot
- Category:
Collaborative Robot Safety - Industry:
Automotive Components Manufacturing - Impact:
Real-time robot halt on human presence