Product · PatternLab Cortex

One operational semantic layer. Multiple operational workspaces.

PatternLab Cortex connects the meaning in your ERP fields, production records and documents. PatternLab helps configure governed workspaces on that shared foundation, so each operational team gets purpose-built work without creating another copy of identities, relationships, rules or evidence.

Product: PatternLab Cortex. Foundation: the operational semantic layer. Experience: multiple governed operational workspaces.
Architecture

From source records to multiple governed workspaces.

Each layer has a distinct job. Sources remain evidence; mapping creates identity and meaning; the semantic layer connects the operation; rules, solvers and AI act on it; multiple workspaces reuse that shared foundation for different operational outcomes.

Industry model
Ready-made manufacturing meaning

Product, process, resource, material, order, inventory, constraint and industry-specific relations and rules.

Sources
ERP, MES, spreadsheets, records and documents

Original fields and facts remain systems of record and retain their evidence.

Mapping + identity
AI-assisted matching with review

Fields and records are mapped to stable operational identities; duplicates and conflicts are surfaced, not hidden.

Customer layer
Your operational semantic layer

Hierarchy, relations, time, versions, effective dates and lineage connect product and process definitions to execution facts.

Decision services
Rules · solvers · AI

Rules derive and validate; solvers test feasibility and optimize; AI proposes mappings, explanations and actions.

Workspaces
Multiple operational workspaces

Each workspace reuses shared identities, relationships, rules, evidence and governance. Supply Chain is live; Plan, Inventory and Sales are views within it.

PatternLab-assisted activation

Configure workspaces with your team. Do not rebuild meaning.

PatternLab works with your team to map existing fields, records and documents into the shared operational model, then configure the workspace, views, roles and controls around the required outcome. AI assists mapping and explanation; every mapping is reviewable, traceable and versioned. This is a guided implementation, not a self-serve builder.

Map

Connect your sources

PatternLab-assisted mapping turns source records into stable operational identities and relationships while retaining evidence.

Configure

Shape the operational work

Together we define the workspace views, decisions, validation, roles and approval paths.

Reuse

Extend the shared layer

Additional workspaces can use the same identities, rules and governance without creating isolated models.

Semantic depth

A factory is more than a list of tables.

Hierarchy

Plant to operational detail

Enterprise → site → area → line → resource; product family → SKU → specification; order → operation → material requirement.

Definition vs execution

Product/process and actual event

The designed route is distinct from the work order that executes it. A standard yield is distinct from the yield observed on a particular batch.

Time

Version and effective dates

Know what was true, when it became valid, which plan used it and whether a later correction should affect history or only future decisions.

Governance

Lineage, roles and approvals

Every derived fact can explain its sources and rule. Roles define who reviews mappings, publishes plans and approves high-impact changes.

Commercial language

Derive. Validate. Explain. Solve.

Derive what operations already knows.

Use mapped attributes and industry rules to derive routes, eligibility, transitions, dependencies and constraints instead of typing giant matrices.

Inputs
Mapped facts · industry model · documents · actuals
Derived
Operational objects, relations and consequences

Prove consistency before action.

Rules test identity, completeness and operational validity. Solvers test whether a proposed world is feasible.

Checks
Schema · identity · rule conflicts · temporal validity · feasibility
Returns
Accepted, or refused with the reason

Trace every conclusion.

Show the source evidence, mapping version, rule path and affected objects behind a plan, shortage or promise.

Why
Source → mapping → fact → rule → consequence
Control
Review, correct, approve or reject

Choose among feasible futures.

Constraint solvers calculate a plan, inventory position or delivery date that respects the shared model and ranked objectives.

Govern
AI proposes. Rules and solvers validate. People approve high-impact decisions.
Outcome
Better plan · lower inventory · more reliable promise
Workspace layer

One shared foundation. Different operational outcomes.

The live reference implementation is the Supply Chain workspace, with Plan, Inventory and Sales as connected views. Further workspaces are configured with PatternLab against the same semantic layer.

Live

Supply Chain

Connected planning, inventory and order-promising decisions in one governed workspace.

Illustrative · not live

Quality Risk Registry

A potential workspace could connect materials, batches, specifications, deviations and evidence to governed quality-risk review.

Illustrative · not live

Maintenance operations

A potential workspace could connect assets, work history, production dependencies and evidence to maintenance prioritization.

Operating boundary

One model on top of systems of record.

Not an ERP replacementTransactions, masters and ledgers remain in their source systems.
Operational meaningCortex connects those records to decisions and retains evidence.
Not copied contextEach workspace does not recreate identities, relationships, rules or evidence.
Shared semantic layerMultiple workspaces reuse the same governed operational meaning.
Not autonomous authorityAI does not silently publish high-impact operational changes.
Governed assistanceRules and solvers validate; people retain approval authority.