Artificial Intelligence & Machine Learning
We apply computer vision, predictive analytics and machine learning to real industrial problems — quality inspection, equipment health, yield and autonomy — and deploy them where they run, from cloud to edge.
Artificial intelligence is only useful when it touches a real decision. Karna Dynamics builds AI and machine-learning systems aimed squarely at industrial and agricultural outcomes: catching the defect a human eye misses, predicting the bearing failure before it stops the line, forecasting yield, and giving robots the perception they need to act on their own.
Our work spans the full pipeline — collecting and labelling the right data, training and validating models, and deploying them where they actually need to run. That often means the edge: a vision model running on the machine itself for instant inspection, or a perception network on a robot in the field, with no round-trip to the cloud and no latency.
Crucially, AI here doesn't stand alone. It plugs into the same robots, automation lines and XR systems we build, so a model's prediction triggers a real action: reject the part, flag the asset, re-route the robot. Intelligence and machinery are designed to work as one closed loop.
Machine vision inspection
Deep-learning models spot scratches, misassembly and defects in real time, more consistently than manual checks.
Predictive maintenance
Models learn each machine's healthy signature and warn before vibration, heat or current signals a coming failure.
Edge deployment
Run models directly on machines and robots for instant, offline inference with no cloud latency.
Robot perception
Detection, segmentation and pose estimation that let our robots recognize and handle objects autonomously.
Forecasting & optimization
Predict yield, demand and energy use, and optimize schedules and setpoints from real production data.
Custom model pipelines
End-to-end: data collection, labelling, training, validation and lifecycle monitoring for drift.
| Domains | Vision, time-series, forecasting, control |
|---|---|
| Vision tasks | Defect detection, segmentation, OCR, pose |
| Predictive | Anomaly detection, RUL, failure forecasting |
| Deployment | Cloud, on-prem and edge inference |
| Edge hardware | GPU/NPU modules on machines & robots |
| Pipeline | Data → label → train → validate → monitor |
| Integration | Triggers actions in automation & robotics |
| Monitoring | Drift detection & model retraining |
- Automated visual quality control on the line
- Predictive maintenance for critical machines
- Crop, pest and yield analysis in agriculture
- Autonomous perception for robots and vehicles
- Production forecasting and process optimization
Frame
We pin down the decision the model must improve and the data available to support it.
Build
Data is collected and labelled; models are trained and validated against your real-world acceptance criteria.
Deploy
The model ships to cloud, on-prem or edge — running on the machine or robot where the decision happens.
Close the loop
Predictions trigger real actions in your automation and robotics, and models are monitored and retrained over time.
Put AI & ML to work in your operation.
Send us the problem, the constraints and the numbers — we'll come back with an approach.