One platform for industrial AI production.

Shared assets, QA, workflows, and deployment in one platform.

Computer Vision Book a platform demo

Platform architecture

workbench

annotation

qa

workflow

training

runtime

capture

Shared asset model

Assets, labels, training, and inference logs stay connected.

episode

›

frame

›

edgeTelemetry

›

action

›

label

›

qaReport

›

trainingRun

›

deployment

›

inferenceLog

All objects stay connected to the same production object graph.

Integrated modules

One platform for every production module.

12.4M

98.4%

142

Data Workbench

Operational module description.

12.4M

98.4%

142

Annotation

Operational module description.

12.4M

98.4%

142

QA & Reliability

Operational module description.

12.4M

98.4%

142

Workflow Engine

Operational module description.

12.4M

98.4%

142

Training Center

Operational module description.

12.4M

98.4%

142

Edge Runtime

Operational module description.

Production convergence

Make CV and edge AI work in the same loop.

Shared asset graph

Source assets connect to training and edge events.

CV VideoPipelineEdge Event

QA rules

QA rules are reused across workflows.

min_bbox_area > 100pxlabel_consistency > 0.8

Closed loop

Production feedback flows into retraining.

DeployInferRetrain

The platform stopped manual handoffs between teams.

Alex Morgan

Industrial AI Platform Lead

Deployment modes

Adapt to cloud, private, and edge environments.

Cloud

Deploy based on governance, latency, and control needs.

SecureScalable

Private

Deploy based on governance, latency, and control needs.

SecureScalable

Edge

Deploy based on governance, latency, and control needs.

SecureScalable

Platform FAQ

Why use one platform?

One platform preserves context and approval lineage.

Does it support private deployment?

It can run in cloud, private, or hybrid configurations.