Built to understand the operation, improve the decision and learn from the result.
ConstraintFlow connects manufacturing systems and operational knowledge into a decision-ready model. Optimization and AI services evaluate feasible actions, people remain in control, and actual execution and outcomes improve future decisions.
Connect → Model → Decide → Approve → Execute → Measure → Improve
One decision-ready model. Every capability connected.
Read top to bottom: ConstraintFlow brings the operation together, maintains a living model, applies decision intelligence, coordinates work through agents, keeps humans in control, and closes the loop with measurement and governed improvement.
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Manufacturing systems and knowledge
ConstraintFlow does not require these systems to be replaced. It uses their information as inputs to a broader operational decision model.
ERPMESWMSAPSPLC and machine dataQuality systemsMaintenance systemsPlanning filesSpreadsheetsDocumentsOperational rulesHuman knowledge - 2
Zahir Data Plane
Creates a governed and reusable foundation for manufacturing intelligence. Zahir translates fragmented operational records into consistent manufacturing entities and relationships that can be reused across intelligence applications.
Data ingestionCanonical manufacturing modelCurrent operational stateHistorical production contextSemantic layerData qualityLineageGovernanceFeature preparation - 3
Living Operational Model
ConstraintFlow Operational Digital Twin. The operational twin represents what exists, what is currently happening, what is feasible and what each decision may affect. It is not a 3D factory model — it is a decision-ready representation of manufacturing operations.
PlantsOrdersCustomersMachinesMachine groupsCapabilitiesMaterialsInventoryToolingLaborRoutesSchedulesConstraintsBusiness rulesProduction eventsOutcomes - 4
Decision Intelligence
Deterministic optimization, business rules, machine learning and AI reasoning are combined according to the decision being made. Not every decision is made by a language model.
Constraint evaluationCandidate generationPredictionOptimizationSimulationRisk detectionImpact analysisRecommendation rankingExplainability - 5
Gurita Agent Plane
Coordinates specialized intelligence, tools, workflows and human handoffs. Agents do not replace the underlying optimization, operational data or governance — they coordinate the work required to turn intelligence into action.
Planning agentsInventory agentsDemand agentsProcurement agentsKnowledge agentsWorkflow agentsExecutive agentsSupport agentsTool useWorkflow orchestrationKnowledge retrieval / RAGApprovalsEscalationCase creationNotificationsAuditability - 6
Human-in-the-loop decisions
High-impact manufacturing decisions remain governed by configured approval and accountability rules.
RecommendationSupporting evidenceOptions consideredTrade-offsExpected impactConfidenceApproveAdjustRejectReason capture - 7
Closed decision loop
ConstraintFlow does not stop at generating a recommendation. It tracks whether the decision was trusted, implemented and effective.
Recommendation issuedPlanner responseApproved actionExecution observedVariance detectedOutcome measuredImprovement case createdData, rule, capability, workflow or product correction - 8
Operational Intelligence Control Tower
The shared workspace for planners, operations leaders and executives to understand risk, review recommendations, manage exceptions, approve actions and measure outcomes — not merely a dashboard.
PlannerPlant managerNetwork operationsInventory leaderDemand plannerProcurement leaderExecutive - 9
ConstraintFlow Control Plane
Provides enterprise-wide governance, licensing, deployment visibility and operational oversight across installed ConstraintFlow components.
Customer and plant configurationLicensingCapability accessDeployment registrationConnectivity healthUsage metricsOperational telemetryVersion visibilitySecurity policyPack management
What the closed loop records
Enterprise security and operations
Capabilities are labeled by availability. ConstraintFlow does not imply compliance certifications it has not achieved.
- Identity and SSOAvailable today
- Role-based accessAvailable today
- Encryption in transit and at restAvailable today
- Customer isolationCustomer-configurable
- Secrets managementAvailable today
- Audit logsAvailable today
- ObservabilityAvailable today
- Backup and recoveryCustomer-configurable
- Deployment governanceCustomer-configurable
- Human approvalsAvailable today
- LicensingAvailable today
Why this architecture matters to the operation
One source of operating context
Every intelligence module works from the same orders, capabilities, constraints and current state.
No new decision silos
Scheduling, inventory, procurement and delivery decisions remain connected.
Faster expansion
New use cases reuse the operational model, security, governance and learning loop already established.
Measurable accountability
Every important recommendation can be followed through approval, execution and outcome.
Controlled improvement
Operational learning is governed, explainable and reviewable rather than uncontrolled.
From recommendation to controlled execution
Write-back is governed, audited, reversible where possible, permission-controlled and customer-configured.
Advisory
ConstraintFlow recommends. The user implements the action manually.
Approved workflow
ConstraintFlow generates an action that requires human approval before being sent to another system.
Controlled automation
Pre-approved, lower-risk actions may be executed automatically within configured policies.
Designed for the customer's operating environment
Where source data remains, where operational models are maintained, how systems communicate, where optimization runs and how write-back is controlled are all deployment decisions made with the customer.
Customer cloud
ConstraintFlow runs inside the customer's own cloud account. Source data and operational models stay within the customer environment.
Private cloud
Deployment into a dedicated private cloud, with connectivity to source systems controlled by the customer.
On-premises
For operations that require data and processing to remain on-site, ConstraintFlow can be deployed on-premises. (Planned / evaluated per engagement.)
Hybrid
Data preparation and models remain close to source systems while selected services run elsewhere, with controlled communication between them.
ConstraintFlow-managed
ConstraintFlow operates the environment on the customer's behalf under agreed isolation, security and data-ownership terms.
The same platform, described for technical evaluators
These are technology categories, not a mandatory stack. ConstraintFlow is designed to run in the customer's environment with their preferred components where practical.
- 1Source Systems
- 2Integration and Ingestion
- 3Canonical Operational Data
- 4Analytical and Historical Data
- 5Semantic and Feature Layer
- 6Operational Digital Twin
- 7Optimization and AI Services
- 8Agent and Workflow Runtime
- 9Application and Control Tower
- 10Control Plane
- 11Security and Observability
Example deployment technologies
An example implementation may use technologies such as those below. This is illustrative — none of these are mandatory, and equivalents can be used.
See the architecture mapped to your operation.
Bring your systems and one difficult operating decision. We will show how ConstraintFlow would connect, model, decide, execute and measure it.