Who should use the AI Segmentation workflow?
Teams or solo builders working on creativity tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
4-step pipeline — follow each step in order
1Setup · Step 1 of 4
Open task pageAutomatic Detection
Prepare inputs and settings through Automatic Detection before running ai segmentation.
Why this matters
Automatic Detection sets up the foundation for ai segmentation; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
2Core · Step 2 of 4
Open task pageAI Segmentation
Execute ai segmentation with AI Segmentation to produce the primary final deliverable.
Why this matters
This is the core step where ai segmentation actually happens, so it determines baseline quality for everything after it.
What you get
A first-pass final deliverable is generated and ready for refinement in the next steps.
More options
3Refine · Step 3 of 4
Open task pageObject Erasure
Refine and validate ai segmentation output using Object Erasure before final delivery.
Why this matters
Object Erasure adds quality control so issues are caught before the workflow is finalized.
What you get
The final deliverable is improved, validated, and prepared for final delivery.
4Deliver · Step 4 of 4
Open task pageAI Object Eraser
Package and ship the output through AI Object Eraser so ai segmentation reaches end users.
Why this matters
AI Object Eraser is what turns intermediate output into a usable, publishable result for real users.
What you get
A finalized final deliverable is ready for publishing, handoff, or integration.