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DATAMETADATAMETADATA ORCHESTRATIONMETADATA FABRICCAPABILITIESHARVESTCONTEXTUALIZEINFERCONVERGEVALIDATEGOVERNANCEOPERATIONSAIDECISION-MAKING

Platform

Operate with living context.
Activate metadata into decisions.

Coretex is a metadata intelligence platform: a living Metadata Fabric powered by GraphMind—built to make governance operational and AI trustworthy in regulated environments.

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Stop searching for data. Start operating on context: relevance, lineage, policy, ownership, and quality—unified into one decision surface.

Coretex relevance trace dashboard

WHAT YOU GET

A platform with three tightly coupled layers.

Coretex operationalizes metadata as a living system—centered on business use cases, not static asset inventories.

Interactive use case viewsthat expose the most relevantentities, evidence, andconstraints—ready foroperations.A unified graph of lineage,semantics, policy, identity,ownership, andquality—continuously updated.Layered inference that scoresrelevance, tracescontributions, and returnsrationale—human-readable andauditable.GraphMindExplainable ReasoningMetadata FabricLiving Context LayerCoretexDecision Surface

HOW IT WORKS

From fragmented metadata
to operational intelligence.

01
Connect
Ingest metadata from catalogs, lakes, warehouses, BI, IAM, and APIs.
02
Unify
Normalize entities, semantics, lineage, and governance signals into one fabric.
03
Reason
Score relevance to a use case and explain why—down to contributions and constraints.
04
Activate
Serve context into teams, governance workflows, copilots, and operational systems.
STARTMETADATA ACQUISITIONINFERENCE & RELATIONSHIPGENERATIONHUMAN-IN-THE-LOOPVALIDATIONMETADATA FABRIC UPDATEBUSINESS INSIGHTSDONE
See it in action

WHY IT’S DIFFERENT

Not a catalog.
An intelligence layer.

Traditional catalogs document assets. Coretex continuously links metadata to business use cases with relevance scoring, governance signals, and explainable reasoning.

Static discovery
Operational decision support
Manual tagging
Algorithmic relevance scoring
Uniform governance
Governance focused by business impact
Opaque AI context
Explainable rationale + constraints
AspectTraditional Data CatalogMetadata Intelligence Platform (Coretex)
Core principleData-first documentationUse case–centric intelligence
Role of metadataStatic reference informationLiving, evolving system
Link to business valueIndirect and manualDirect, automated linkage to use cases
Relevance determinationManual tagging or keyword matchingAlgorithmic relevance scoring (GraphMind)
Governance approachApplied uniformly across all dataFocused governance based on business impact
Metadata maintenanceHigh manual effortAutonomous orchestration with feedback loops
Context awarenessLimited, mostly technicalSemantic and business-aware reasoning
Adaptability over timeDegrades as data changesContinuously adapts as metadata evolves
Human involvementManual curation and upkeepHuman-in-the-loop validation for trust and refinement
AI readinessLimited support for AI useFoundation for agentic and context-aware AI
Outcome for stakeholdersData discoveryActionable insight and decision support

EMERGING TRENDS

Two trends are converging—
and forcing a new layer.

Metadata is shifting from passive documentation into an automated, operational service. At the same time, agentic AI needs grounded, context-aware data to act responsibly. The intersection creates a clear imperative: move beyond static catalogs toward an intelligent metadata fabric.

MAAS · OPERATIONAL METADATA · AGENTIC AI
METADATA-AS-A-SERVICE (MAAS)
Metadata becomes the backbone
Enterprises are treating metadata as continuously updated infrastructure for scalability, governance, and trustworthy decisions—not a static catalog.
  • Dynamic + automated metadata
  • Governance signals at runtime
  • Trustworthy decision support
OPERATIONAL METADATA
Metadata moves into operations
Metadata is increasingly driving workflows, monitoring, and predictive interventions—enabling real-time detection and continuous optimization.
  • Workflow automation
  • Realtime issue detection
  • Predictive interventions
AGENTIC AI
Agents require living context
AI agents need orchestrated meaning, constraints, and relationships to act autonomously—making metadata the fuel for responsible, adaptive systems.
  • Context-aware actions
  • Reasoning on meaning + links
  • Responsible evolution
Implication: a living metadata fabric is required to support operations today— and the next generation of AI tomorrow.
Accuracy. Accountability. Trust.AutomatedInferenceHumanValidationFeedbackIntegrationModelRefinementHumanValidation

TRUST BY DESIGN

Human-in-the-loop.
Audit-ready outputs.

GraphMind can automate inference, but trust is established through validation. Experts confirm relevance and decisions become explainable evidence—captured back into the system.

Explainable rationale
What signals contributed, what relationships were followed, and why the score is what it is.
Accountability
Ownership, stewardship, and change traces are first-class citizens of the fabric.
Continuous refinement
Feedback becomes learning signals—without breaking operations.
EXPLAIN · VALIDATE · LEARN · SCALE

SECURITY · GOVERNANCE · COMPLIANCE

Governance that enables speed—
without losing control.

Policies, lineage, identity, classification, and quality move with the context—so every decision has constraints, an origin, and an explanation.

Auditability
Trace decisions across assets, products, reports, and models—what changed, when, and why.
Policy enforcement
Purpose + classification + access intent govern usage across workflows and AI.
Actionable lineage
End-to-end lineage with impact analysis for changes, incidents, and approvals.
Explainable AI context
Rationale + confidence + constraints—built into the answer, not bolted on.

DEPLOYMENT

Designed to fit your
enterprise reality.

Azure-native by default, compatible with hybrid patterns, and adoptable incrementally—without forcing migrations or rewrites.

Azure-native by default
Runs securely on Azure using native identity and enterprise patterns.
Hybrid & on-prem compatible
Deploy alongside existing platforms—data stays where it belongs.
Incremental adoption
Start with one domain/use case. Expand as value is proven.
No forced rewrites
Connect catalogs, lakes, warehouses, and tools as they exist today.

OUTCOMES

Decisions you can defend.
Intelligence you can scale.

Relevance scoring narrows scope without losing transparency—so governance is applied where it delivers real business impact.

Reduce risk
Know what changed, what breaks, and who is affected—before it ships.
Increase AI accuracy
Ground copilots and agents in consistent semantics + trust signals.
Move faster with governance
Policies enable safe automation instead of slowing delivery.
RELIABILITY
Impact & change intelligence
Know exactly what a change affects.
  • Lineage-aware rollouts and audit trails
  • Fewer broken dashboards and pipelines
  • Clear ownership and accountability
ENTERPRISE AI
AI readiness & trust
Turn metadata into model context.
  • Governed semantic layer for copilots/agents
  • Policy + quality signals baked into prompts
  • Explainable decision paths (why/why-not)

Ready to activate metadata-driven intelligence?

See Coretex on your use case—scored, explained, and governed.

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