Add AI decision points to any workflow and let the platform improve over time.
Orqit's AI Workflows turn traditional orchestration into a self-optimizing engine, where AI continuously learns from execution data, suggests new branches, and predicts outcomes, while preserving full governance.
Users design a workflow in the visual builder and add AI Decision Nodes that evaluate real-time data (e.g., incident severity, asset health). The AI model predicts the best path, auto-adjusts thresholds, and surfaces confidence scores. Over time, the system refines predictions based on actual outcomes, feeding back into the model.
Self-optimizing orchestration for process engineers, IT leaders embedding AI decision points, and developers integrating custom services.
Embed probabilistic logic with confidence levels at any step.
Models retrain on execution outcomes—nightly by default.
See why the AI chose a path with feature importance traces.
Enforce compliance before AI-driven actions execute.
Immutable history of flow changes with diff view.
AI assesses impact metrics, auto-assigns priority, and selects the routing path.
AI predicts change risk from historical success and suggests rollback steps.
AI evaluates usage trends and recommends bulk vs. per-seat licensing.
AI decides whether to auto-quarantine a host or trigger manual review.
Reduce manual decision latency with predictive routing.
Workflows complete more often on the first optimal path.
Explainability improves trust and auditability for stakeholders.
Execution time improves as the model learns from real runs.
The core AI model evaluates real-time context, predicts outcomes, and suggests optimal branches, learning from each execution.
Dashboards show decision accuracy, confidence distribution, and impact on SLA compliance.
Jira
ServiceNow
Yes – the Explainability Dashboard provides feature importance and decision trace.
Retraining occurs nightly on accumulated execution data.
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