Collect
Capture images, sensor streams, logs, production records, and operational context.
From raw industrial data to deployed model loops.
We handle the pipeline around the model: data collection, labeling, training, evaluation, deployment, monitoring, and iteration.
Annotate
Build labeling guidelines, QA passes, review queues, and usable dataset structure.
Train
Train custom models for computer vision, anomaly detection, time series, or classification.
Evaluate
Measure model behavior against real costs, edge cases, and production thresholds.
Deploy
Package the model into an API, edge service, dashboard, or human review workflow.
Monitor
Watch model drift, review failures, collect new labels, and improve the next version.
Inspection and classification.
Defect detection, image classification, object localization, video review, and QA tooling.
Signals over time.
Anomaly detection, forecasting, sensor behavior, operating windows, and event detection.
Labels that can be trusted.
Annotation workflows, guidelines, reviewer tools, consensus checks, and dataset versioning.