Layer 01
Field signal capture
Acoustics, ultrasound, vibration, and operational logs captured to hold up on site.
Industrial AI
Industrial AI that runs on site
We analyze acoustics, vibration, ultrasonics, and operational data with AI. The field-ready dashboard is part of what we ship, not an add-on.
Acoustic, vibration & environmental signal analysisAI analysis and diagnosticsField operation softwareEdge/on-device and on-prem deployment
System scope
We bring sensing, inference, operator software, and deployment rules together as one system that can run in real operations.
Project flow
The same team stays involved from problem framing through rollout and handoff.
Layer 02
Edge inference
Models built to run in real time on equipment and local gateways, not just in the lab.
Approach
Beyond the AI model
Who uses it, where, and for what — designed alongside the model itself.
01
Collect the signals
Select the signals that matter on site and build the capture pipeline.
- Signal criteria tuned for real anomalies
- Data capture that holds up in the field
02
Build the model and management system together
Inference, gateways, dashboards, and alerts ship together.
- Edge/on-device and on-prem inference
- PLC, sensor, dashboard, and alert integration
03
Factor in operational standards
Maintenance history, permissions, reporting, and operational systems are designed together.
- Operator-facing UI and reporting flow
- Maintenance history and standards
Featured solutions
Systems that fit right into the field
Every site has different signals and constraints. The bar is the same: systems that hold up in real operations.
Predictive maintenanceHigh-frequency audio and vibration signatures
Sound-based motor condition monitoring
Acoustic and vibration analysis for motors and gearboxes. Catches anomalies before they cause downtime.
Deployment
Field PLC/SCADA equipment, control dashboard, and alerting
- Earlier detection of anomalies
- Better-timed maintenance
- Fewer unplanned stoppages
Safety systemWire tension and appearance
Crane wire monitoring
Inspects hoisting equipment wires to detect fatigue accumulation and break risk.
Deployment
Crane and hoist safety monitoring
- Pre-incident warning detection
- Real-time inspection data
- Clear signals for field decisions
Robotics AI
AI experiences applied to robot platforms
We analyze sensor data and manipulation logs from robots with AI to improve autonomous decision-making and teleoperation precision.
Sensor fusion & state estimation
Autonomous motion planning
We analyze sensor data and motion logs from robot platforms to improve autonomous behavior and task execution precision.
- Sensor-fusion-based real-time state estimation
- Environment-adaptive motion planning
- Autonomous behavior under safety constraints
Demonstration-based skill acquisition
Manipulation learning
We learn from robot manipulation data to enable autonomous complex task execution and improve operational efficiency.
- Demonstration-based manipulation learning pipeline
- General-purpose manipulation across different task environments
- Real-time obstacle-avoidance path planning
Remote operation data pipelines
Teleoperation & transfer learning
We use manipulation data collected from teleoperation systems to accelerate robot learning and transfer across tasks.
- Teleoperation-data-based behavior cloning
- Bimanual coordinated task learning
- Sim-to-real transfer learning
VLA foundation models
Vision-Language-Action model fine-tuning
We fine-tune pretrained VLA foundation models on domain-specific demonstration data to adapt them to new tasks and sites quickly.
- Adapter/LoRA fine-tuning on open VLA models (OpenVLA, π0, RT-2)
- Aligned data pipeline for site cameras, language instructions, and trajectories
- Domain adaptation for zero- and few-shot generalization to new tasks
Sensor-to-action policy learning
End-to-end robotics
Instead of separating perception, planning, and control, we learn a single policy from sensor input to motor commands, removing error accumulation between modules.
- Unified policies over image, proprioception, and language inputs
- Diffusion-policy and transformer-based action generation
- Distilled, lightweight pipelines for on-device inference
Case studies
Programs validated against real operating environments
Each program is documented with the problem it scoped and how the system fit real operations.
Environment & Forestry
Korea Forest Research Institute
Ultrasonic non-destructive diagnostics for earlier pine wilt disease detection
Approach
We built ultrasonic NDT equipment and a field reading interface that tracks changes in ultrasonic propagation inside trunks. Data is captured and read on site, without an external network.
Ultrasonic NDT scan
Diagnostic method
On-site interpretation
Data processing
Project flow
From problem definition through operational handover
The same team carries the work from first framing into day-to-day operations.
Diagnose
Start by checking the site
Equipment layout, operating procedures, data quality, and site constraints — checked first.
Build
Build analysis and operations systems together
AI models, dashboards, alerts, and integrations built together. We build systems that blend into real workflows.
Hand off
Own the work past the PoC
Installation, deployment environment, handover, and follow-on improvements are documented.