From site assessment and proof of concept to system integration and rollout, supporting complex industrial sites, ISV partnerships, and hardware AI integration.
Diagnosis First
Start by defining recognition targets, available samples, existing systems, and rollout goals. Proceed to a proof of concept once recognition feasibility, training data, and integration requirements are understood.
Custom Route
01
Site Assessment
Recognition targets, available data, camera locations, and acceptable false positives
02
Solution Design
Training and perception platforms, edge devices, and AI modules
03
Pilot Project
Validate recognition and system integration at real locations
04
Rollout and Improvement
Document models, rules, configurations, and delivery procedures
Entry Paths
Adapt existing GSmart algorithms, platforms, edge hardware, and delivery practices to the specific requirements of your site.
Adapt recognition to specific objects, defects, behaviors, and operating conditions beyond the scope of standard mining, port, transport, and factory solutions.
Add vision AI to existing industry applications and customer systems, with a solution package that can be deployed across sites.
Integrate embedded vision AI into cameras, vehicle terminals, edge devices, and industrial equipment.
Diagnosis Matrix
Before starting a proof of concept, assess technical feasibility, sample availability, system constraints, and rollout goals to keep the pilot focused and measurable.
Capability Modules
Combine training and perception platforms, edge devices, and hardware modules to suit enterprise procurement, project delivery, and partner integration.
Manage samples, annotation, training, evaluation, version releases, and ongoing model improvements.
Connect video feeds, configure algorithms, manage alerts, review responses, and integrate business systems.
Run low-latency inference and local alerts on site, including weak-network and cloud-edge deployments.
Embed vision AI capabilities in cameras, vehicle terminals, and industrial devices.
Delivery Route
Use staged validation to turn site requirements into delivery steps with defined inputs, outputs, and acceptance criteria.
Define recognition targets, business actions, deployment scope, and acceptance criteria.
Assess sample quality, camera locations, lighting, occlusion, and additional collection needs.
Test algorithm feasibility, false positives, missed detections, and site integration.
Train models, deploy edge inference, connect the platform, and configure response logic.
Validate recognition, alert delivery, and operational response at real locations.
Document models, rules, configurations, and delivery procedures for rollout across locations.
Deliverables
Each engagement defines its scope, process, and deliverables so customers and partners can plan implementation and acceptance.
Identify feasible tasks, limitations, additional data needs, and an estimated pilot timeline.
Specify recognition targets, model versions, performance metrics, and an improvement plan.
Define cameras, edge devices, platforms, business systems, and integration interfaces.
Set pilot scope, acceptance criteria, deliverables, and a plan for wider deployment.
Before Consultation
These materials help assess readiness for a proof of concept and estimate the scope of integration and wider deployment.
Site images and video samples
Recognition targets and anomaly definitions
Existing camera and system inventory
Deployment environment and network conditions
Business systems to integrate
Expected acceptance criteria
We assess your recognition targets, samples, existing systems, and deployment environment to propose a testable proof of concept and a suitable solution.
