For the complete documentation index, see llms.txt. This page is also available as Markdown.
Model stages
Integrate model inference as an observable, reviewable workflow responsibility.
A Model stage makes inference part of the same state machine as human work. It must have a versioned contract, mapped outputs, visible failure behavior, and a human-controlled acceptance route.
Before you make the change
Define model owner, version, endpoint or runtime, supported modality, input, output, and ontology mapping.
Prepare a calibration cohort and expected failure cases.
Define timeout, retry, empty-output, malformed-output, and low-confidence routes.
Unitlab AI Models catalog
Treat every public or private model as a versioned production dependency whose mapping and failure behavior are part of workflow design.
Understand the product behavior
Model → Annotate → Review: a model proposes labels, an annotator corrects them, and a reviewer verifies the result.
Annotate with local assistance → Review: Magic Touch, prompt detection, Find Similar, or tracking helps the annotator within the task.
Model → Review with rejection to annotation: high-confidence output moves directly to review; rejected work returns to a human correction stage.
Annotate → Review → specialist Review: ambiguous or high-risk cases move to a second Review stage configured for specialists.
Workflow save and change impact
Workflows are reusable workspace definitions that can be bound to multiple projects. Saving an active graph is therefore not a cosmetic edit. Before applying a change, Unitlab validates reachability, required edges, terminal stages, and the effect on in-flight items.
Important UX rules:
exactly one Project and one Complete stage;
Project has no incoming edge and must lead to an entry stage;
Review has one Approve and one Reject route;
terminal stages have no outgoing routes;
every stage is reachable from Project;
duplicate outgoing action names are rejected;
a stage holding active items cannot be silently deleted;
sensitive automated-stage configuration cannot change while occupied without resolving the impact.
When an active workflow change would strand work, Unitlab returns an apply-impact conflict instead of silently moving or losing items.
Model-stage user experience
Entering a Model stage automatically dispatches the configured model. The item shows Processing while inference runs. Predictions are saved as normal annotation history, then the item follows the Model stage’s default edge to another Model stage, Annotate, Review, or a terminal stage. A failure moves the item to Error so it can be diagnosed rather than disappearing from the workflow.
Add a model to production routing
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1. Approve the contract
Document inputs, outputs, classes, geometry, confidence, version, and ownership.
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2. Map to ontology
Verify every model output has an intentional destination or rejection rule.
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3. Place the stage
Connect model input, success, human review, failure, and escalation routes.
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4. Exercise failure modes
Test timeouts, empty output, malformed output, partial success, and unavailable runtime.
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5. Review corrected output
Measure human correction by source, class, and model version.
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6. Version change safely
Re-run calibration and workflow impact review for material model or mapping updates.
Decisions that affect production
Decision
Production guidance
Pre-label vs autonomous route
Choose according to risk and the required human acceptance gate.
Confidence
Use for prioritization or routing only when calibrated on the relevant domain.
Retry
Inspect remote task state before repeating a mutation.
Ownership
A failed model task must land in a visible queue with a named owner.
