World-renowned port operator managing multiple high-traffic terminals at key trade hubs
Container terminal operations handling millions of containers annually across gates, yards, and vessel operations
High-volume container movements requiring damage inspection at gate lanes, yard crossings, and rail interfaces
Replace time-consuming manual inspections with automated damage detection that maintains throughput, ensures consistency, and provides defensible digital records for all container movements
Physical walkdowns stopped containers, created queues, and delayed truck turnarounds
Large inspector workforce needed across multiple shifts, terminals, and peak seasons
Different inspectors applied different standards, creating friction with shipping lines and insurers
Volumes rising faster than manual inspection could scale without sacrificing speed or quality
Disputed damage led to prolonged back-and-forth without clear visual evidence or standardized records
High-speed imaging systems capture multiple angles as containers pass at operating speed, making inspection invisible to operations
Custom AI models trained on thousands of real-world examples recognize dents, cracks, bent corners, door issues, and roof deformation consistently
API layer connects inspection data directly into terminal operating systems, eliminating duplicate entry
Every container gets time-stamped, image-backed inspection linked to its ID for defensible documentation
Operations, yard, and claims teams access images and results on devices they already use
System identifies damage trends by route, cargo type, or handling practice for targeted improvement
Capture sharp multi-angle images as containers pass at operating speed
Trained on container-specific damage patterns, recognizing issues with consistent criteria across sites
Handles image analysis and storage at scale with near real-time results
Provides quick access to images and inspection results across terminal teams
Connects inspection outcomes directly into existing terminal systems for unified data flow
Containers inspected at full operating speed without stopping or slowing traffic
AI-driven criteria eliminated subjective judgments across shifts and terminals
Automated process freed inspectors for higher-value tasks
Time-stamped, image-backed records reduced disputes from weeks to days
Pattern recognition identified damage-prone routes and handling practices
Eliminated inspection bottlenecks, improved gate flow, and increased volume capacity without adding lanes
Consistent assessments regardless of shift, terminal, or location with standardized visual documentation
Lower direct labor costs and fewer extended claims, legal escalations, and damage-related disputes
Pattern insights directed maintenance and operational changes to root causes instead of symptoms
Clear, high-quality images from arrival/departure resolved disputes in days instead of weeks
You’re one step away from building great software. This case study will help you learn more about how Simform helps successful companies extend their tech teams.
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You’re one step away from building great software. This case study will help you learn more about how Simform helps successful companies extend their tech teams.
