THE MERIKH PLATFORM

The Factory OS that turns industrial data into coordinated action.

Merikh connects machines, systems, and operational teams in one shared factory model. It turns raw signals into trusted context, role-specific decisions, and actions whose impact can be measured.

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Your factory already has data. What it needs is shared operational context.

Machines report states. Production systems hold orders and targets. Operators understand what happened on the floor. Maintenance teams know the history behind recurring failures. Yet these signals often remain separated—across screens, spreadsheets, messages, and people.

Merikh brings those sources into one operational model, so teams can work from the same version of factory reality and move from seeing a problem to owning, resolving, and verifying it.

Isolated machine signals

To: Machine data connected to products, shifts, orders, and operating conditions

KPIs with conflicting definitions

To: Trusted calculations with visible sources, rules, and context

Alerts without ownership

To: Role-based actions with owners, approvals, due dates, and follow-through

Improvement activity without proof

To: A closed loop that measures whether an action changed the outcome

From	To
Isolated machine signals	Machine data connected to products, shifts, orders, and operating conditions
KPIs with conflicting definitions	Trusted calculations with visible sources, rules, and context
Alerts without ownership	Role-based actions with owners, approvals, due dates, and follow-through
Improvement activity without proof	A closed loop that measures whether an action changed the outcome

One operating loop—from signal to verified improvement.

Connect

Website copy: Bring machine signals, industrial systems, cameras, sensors, and operator input into a reliable data foundation.

Understand

Website copy: Add the production context that explains what the data means: asset, product, order, shift, process, and operating condition.

Prioritize

Website copy: Identify the losses and abnormalities that matter most to safety, quality, capacity, cost, and delivery.

Coordinate

Website copy: Send the right evidence, recommendation, and workflow to the people responsible for deciding and acting.

Measure

Website copy: Compare the result with the baseline and make the impact visible.

Learn

Website copy: Keep the evidence, action history, and outcome connected so the next decision starts with better context.

Step	Website copy
Connect	Bring machine signals, industrial systems, cameras, sensors, and operator input into a reliable data foundation.
Understand	Add the production context that explains what the data means: asset, product, order, shift, process, and operating condition.
Prioritize	Identify the losses and abnormalities that matter most to safety, quality, capacity, cost, and delivery.
Coordinate	Send the right evidence, recommendation, and workflow to the people responsible for deciding and acting.
Measure	Compare the result with the baseline and make the impact visible.
Learn	Keep the evidence, action history, and outcome connected so the next decision starts with better context.

Five layers. One shared factory reality.

Each layer has a distinct job. Together, they create the path from physical operations to trusted intelligence and coordinated action.

Connectivity Layer

Publish-ready description: Connect supported PLCs, drives, sensors, cameras, industrial systems, and edge devices through appropriate integration paths.

Unified Data Model

Publish-ready description: Organize time-series data, assets, production context, events, and operational relationships in one shared model.

Context & Visualization Layer

Publish-ready description: Make factory state visible through live views, histories, schedules, events, camera context, and connected business information.

Experience Layer

Publish-ready description: Give managers, production teams, maintenance, quality, and operators experiences designed around their responsibilities.

Intelligence Layer

Publish-ready description: Use governed AI to explain, recommend, coordinate, and—where explicitly permitted—support operational execution.

Layer	Publish-ready description
Connectivity Layer	Connect supported PLCs, drives, sensors, cameras, industrial systems, and edge devices through appropriate integration paths.
Unified Data Model	Organize time-series data, assets, production context, events, and operational relationships in one shared model.
Context & Visualization Layer	Make factory state visible through live views, histories, schedules, events, camera context, and connected business information.
Experience Layer	Give managers, production teams, maintenance, quality, and operators experiences designed around their responsibilities.
Intelligence Layer	Use governed AI to explain, recommend, coordinate, and—where explicitly permitted—support operational execution.

One factory. A relevant experience for every role.

Factory leadership

What the experience should help them do: See where performance, capacity, quality, and reliability need attention—and whether improvement work is delivering results.

Production managers

What the experience should help them do: Understand plan versus actual, line constraints, recurring losses, and the actions affecting today’s output.

Maintenance teams

What the experience should help them do: Prioritize work with asset history, event evidence, production impact, and clear ownership.

Quality teams

What the experience should help them do: Connect defects and waste to products, batches, equipment conditions, process events, and corrective actions.

Operators

What the experience should help them do: Report context, receive relevant instructions, and contribute the human evidence that machine data alone cannot provide.

IT/OT and engineering

What the experience should help them do: Manage connectivity, data quality, asset structure, integrations, access, and the technical health of the platform.

Role	What the experience should help them do
Factory leadership	See where performance, capacity, quality, and reliability need attention—and whether improvement work is delivering results.
Production managers	Understand plan versus actual, line constraints, recurring losses, and the actions affecting today’s output.
Maintenance teams	Prioritize work with asset history, event evidence, production impact, and clear ownership.
Quality teams	Connect defects and waste to products, batches, equipment conditions, process events, and corrective actions.
Operators	Report context, receive relevant instructions, and contribute the human evidence that machine data alone cannot provide.
IT/OT and engineering	Manage connectivity, data quality, asset structure, integrations, access, and the technical health of the platform.

Industrial AI should be useful, bounded, and accountable.

Merikh is being built around a simple principle: understanding comes before automation. AI should reason with factory context, show the evidence behind its recommendation, operate within a defined role and asset scope, and request human approval whenever the action requires it.

Industrial KPIs remain deterministic. AI helps people investigate, explain, prioritize, and coordinate; it does not replace trusted calculation logic or silently take control of physical equipment.

Evidence linked to the recommendation

Confidence and uncertainty made visible

Role, asset, and action scope defined

Human approval where required

Decision and action history available for review

Start with one measurable problem. Build on a reusable foundation.

Merikh does not require a factory to solve everything at once. A deployment can begin with a limited line, machine group, or operational loss—while the underlying model is designed to support broader factory coordination over time.

Assess

Copy: Understand the production environment, available data, existing systems, and the decision that needs to improve.

Connect

Copy: Select the appropriate integration path for the approved pilot scope.

Validate

Copy: Check signal meaning, freshness, completeness, and production context before trusting the output.

Baseline

Copy: Establish a transparent starting point for the agreed KPIs and losses.

Act

Copy: Put insights into a role-based workflow with clear ownership.

Verify

Copy: Measure what changed, what did not, and what should happen next.

Deployment step	Copy
Assess	Understand the production environment, available data, existing systems, and the decision that needs to improve.
Connect	Select the appropriate integration path for the approved pilot scope.
Validate	Check signal meaning, freshness, completeness, and production context before trusting the output.
Baseline	Establish a transparent starting point for the agreed KPIs and losses.
Act	Put insights into a role-based workflow with clear ownership.
Verify	Measure what changed, what did not, and what should happen next.

Built around the systems you already operate.

Merikh is designed to work across mixed industrial environments. Depending on the factory and approved scope, integration may involve PLCs, drives, sensors, cameras, edge gateways, operator input, and supported ERP, MES, SCADA, or maintenance-system interfaces. Compatibility is confirmed during technical assessment—not assumed from a generic claim.

Trust is part of the operating model.

Reliable factory intelligence depends on controlled access, traceable data, visible calculation logic, and accountable action. Review how Merikh approaches data quality, role-based access, auditability, and AI governance.

See Merikh in the context of your factory.

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