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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.

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.

FromTo
Isolated machine signalsMachine data connected to products, shifts, orders, and operating conditions
KPIs with conflicting definitionsTrusted calculations with visible sources, rules, and context
Alerts without ownershipRole-based actions with owners, approvals, due dates, and follow-through
Improvement activity without proofA closed loop that measures whether an action changed the outcome

One operating loop—from signal to verified improvement.

StepWebsite copy
ConnectBring machine signals, industrial systems, cameras, sensors, and operator input into a reliable data foundation.
UnderstandAdd the production context that explains what the data means: asset, product, order, shift, process, and operating condition.
PrioritizeIdentify the losses and abnormalities that matter most to safety, quality, capacity, cost, and delivery.
CoordinateSend the right evidence, recommendation, and workflow to the people responsible for deciding and acting.
MeasureCompare the result with the baseline and make the impact visible.
LearnKeep 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.

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

One factory. A relevant experience for every role.

RoleWhat the experience should help them do
Factory leadershipSee where performance, capacity, quality, and reliability need attention—and whether improvement work is delivering results.
Production managersUnderstand plan versus actual, line constraints, recurring losses, and the actions affecting today’s output.
Maintenance teamsPrioritize work with asset history, event evidence, production impact, and clear ownership.
Quality teamsConnect defects and waste to products, batches, equipment conditions, process events, and corrective actions.
OperatorsReport context, receive relevant instructions, and contribute the human evidence that machine data alone cannot provide.
IT/OT and engineeringManage 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.

Deployment stepCopy
AssessUnderstand the production environment, available data, existing systems, and the decision that needs to improve.
ConnectSelect the appropriate integration path for the approved pilot scope.
ValidateCheck signal meaning, freshness, completeness, and production context before trusting the output.
BaselineEstablish a transparent starting point for the agreed KPIs and losses.
ActPut insights into a role-based workflow with clear ownership.
VerifyMeasure 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.