AI Workflows
Intelligent flows that learn and adapt
Add AI decision points to any workflow and let the platform improve over time.
- AI Decision Nodes
- Continuous Learning Loop
- Explainability Dashboard
- Policy Guardrails
Overview
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.
Audience
Who this is for
- Process engineers designing complex service flows
- IT leaders seeking to embed AI decision points
- Developers integrating custom services into orchestration
Challenges
Problems we address
-
Static workflows Flows struggle to adapt when conditions change in production.
-
Manual branch tuning Hand-edited logic leads to errors and slow iteration cycles.
-
Limited performance insight Bottlenecks stay hidden without execution intelligence.
-
Custom prediction code Embedding predictive decisions requires bespoke development.
Context
How Orqit approaches this
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.
Capabilities
Platform capabilities
Self-optimizing orchestration for process engineers, IT leaders embedding AI decision points, and developers integrating custom services.
AI Decision Nodes
Embed probabilistic logic with confidence levels at any step.
Continuous Learning Loop
Models retrain on execution outcomes—nightly by default.
Explainability Dashboard
See why the AI chose a path with feature importance traces.
Policy Guardrails
Enforce compliance before AI-driven actions execute.
Versioned Workflows
Immutable history of flow changes with diff view.
Workflow
How it works in practice
-
1
Incident Prioritization
AI assesses impact metrics, auto-assigns priority, and selects the routing path.
-
2
Change Management
AI predicts change risk from historical success and suggests rollback steps.
-
3
License Procurement
AI evaluates usage trends and recommends bulk vs. per-seat licensing.
-
4
Security Response
AI decides whether to auto-quarantine a host or trigger manual review.
Outcomes
Results teams care about
Faster decisions
Reduce manual decision latency with predictive routing.
Higher success rates
Workflows complete more often on the first optimal path.
Transparent AI
Explainability improves trust and auditability for stakeholders.
Continuous optimization
Execution time improves as the model learns from real runs.
Governance & security
Built for audit and least-privilege
- AI decisions are logged with input data and confidence scores.
- Approval gates can require human sign-off for high-risk paths.
- All model inference occurs within the tenant VPC; data never leaves.
AI usage
Co-pilot within your guardrails
The core AI model evaluates real-time context, predicts outcomes, and suggests optimal branches, learning from each execution.
Analytics
Operational visibility
Dashboards show decision accuracy, confidence distribution, and impact on SLA compliance.
Integrations
Connects to your stack
ITSM incidents
Asset Management
Jira
ServiceNow
Custom API actions
FAQ
Common questions
Can I audit the AI's reasoning?
Yes – the Explainability Dashboard provides feature importance and decision trace.
How often does the model retrain?
Retraining occurs nightly on accumulated execution data.
Next step
Ready to modernize IT operations?
Launch a pilot workspace or book a walkthrough — with estimated seat savings versus legacy suites, plus 24/7 support.
Entity graph