SUPERVISORY ENGINEERING LAYER

AINNA NeuralOpsControl & Instrumentation

AI-assisted supervisory intelligence for semiconductor equipment, industrial instrumentation and electronic control environments.

Connect sensors, PLCs, DAQ systems and engineering data without replacing existing safety or control infrastructure.

Launch Control Demo Explore System Architecture

Request Engineering Pilot · Does not bypass PLC, safety relay or E-stop

Existing controllers retainedIndependent validationEngineer approval required

NEURALOPS PROCESSING DIESIMULATED ROUTING

PHYSICALPressureTemperatureFlowParticleVibrationESD

ACQUISITIONPLCNI DAQMultiplexerModbusOPC UAIndustrial PC

Signal Parser

Smart Routing

Rules Engine

Detached System

Anomaly Detection

Time-Series

AI Agent

Independent Validation

Safety Gate

OPERATIONALDashboardAlarm CentreDaily ReportMaintenanceTechnician AlertEngineer Approval

Signal StableData Quality VerifiedRoute Detached SystemAI Required NoInterlock PreservedApproval Required

NeuralOps IC Architecture

NeuralOps Intelligence Processor

A custom intelligence-processing architecture that selects the correct processing layer before a task is executed.

Use advanced AI only when advanced intelligence is genuinely required.

The correct processing layer is selected before the task is executed.

Routine engineering workloads run through parsers, rules and Detached Systems - not unnecessary AI inference.

AI recommendations are independently validated and never approve themselves.

Lower computation supports lower cost, lower energy demand and stronger operational governance.

DIE NOP-CI/01CONTROLLED PROCESSING FABRIC

INPUT I/OSensor I/OPLC InterfaceSCADA / HMI / DAQ

PARSER ARRAYParser-01Parser-02Parser-03

CLASSIFICATIONTask ClassifierRisk Classifier

ROUTING CORESmart RouterFormat / risk / accuracy

CORE-01Rule EngineDeterministic logic

CORE-02Detached CoreRepeatable workflow

CORE-03SLM CoreBounded reasoning

CORE-04LLM GatewayAdvanced reasoning

GOVERNANCEValidation CoreIndependent checks

MEMORYAudit MemoryRoute and decision log

CONTROL GATEAuthorised WorkflowSafety-critical actions only

OUTPUT BUSOperational OutputNo direct machine movement

Input - Parser - Rules / Detached Core - Validation - Output Generative AI used: No. Routine processing remains deterministic and repeatable.

Input Interface Layer

Sensor signals, PLC events, SCADA and HMI data, DAQ readings, equipment alarms and engineering documents enter through controlled interfaces.

Multiple Specialised Parsers

Structured machine data is parsed and normalised. Specialised outputs can be compared to identify malformed, incomplete or conflicting information before unnecessary AI usage.

Smart Routing Core

Tasks are classified by type, complexity, format, risk and required accuracy, then routed to rules, parsers, Detached Systems, SLM, LLM or a human engineer.

Detached Systems

Repetitive and predictable workloads continue without continuous AI calls, reducing token consumption, compute demand and dependency on external models.

Rules and Deterministic Engine

Threshold validation, state verification, equipment logic, alarm classification, trend rules and safety policies provide repeatable processing.

AI Intelligence Core

Advanced reasoning is reserved for complex troubleshooting, multi-signal interpretation, engineering document analysis, root-cause assistance and technical summarisation.

Independent Validation Core

Validation remains separate from the generating model and checks rules, history, safety policy, parser results and engineering limits. Inconsistent outputs are rejected, rerouted or flagged.

Human Approval Gate

Qualified engineers retain authority for safety-critical actions. AI cannot approve its own recommendation and direct critical machine control remains blocked.

Compute Discipline

Less Compute. Lower Energy. More Control.

NeuralOps reduces unnecessary AI computation by processing structured and predictable workloads through parsers, rules and Detached Systems.

Conventional AI ArchitectureEVERY TASK - LARGE AI MODEL - OUTPUT

Every TaskLarge AI ModelOutput

High token usage
High compute demand
Higher infrastructure load
Higher power consumption
Increased cooling requirement
Higher operational cost
Greater hallucination exposure

NeuralOps ArchitectureCLASSIFY - CORRECT LAYER - VALIDATE

Task ClassificationCorrect LayerValidation

Very low AI usage for routine workloads
Lower token consumption
Lower compute demand
Reduced power and cooling demand
More predictable infrastructure cost
Higher repeatability
Lower hallucination exposure

Architecture Evolution

Token Reduction Through Processing Segmentation

Internal AINNA operational architecture case study.

Initial AI-Centric Architecture32-34 billion tokens per month

Smart Routing + Detached SystemsApproximately 1.5-2.0 billion tokens per month

Further Segmentation + RefactorApproximately 0.75 billion tokens per month

Internal case study only. Figures are illustrative of AINNA operational architecture segmentation. Actual token use depends on workload mix, model selection, routing configuration and deployment environment. Not a guaranteed production outcome.

Architectural Safeguards

Hallucination Risk Is Controlled by Architecture

Near-zero hallucination exposure for deterministic and parser-based workloads. AI-generated outputs remain subject to independent validation and human approval.

Deterministic Route

SignalParserRule EngineValidationVerified Output

Generative AI used: No
Hallucination exposure: Near zero
Repeatability: High

AI-Assisted Route

Complex EventSmart RoutingAI CoreValidationEngineer

Generative AI used: Yes
Independent validation: Required
Human approval: Required
Direct machine action: Blocked

Structured work avoids generative AISpecialised parsers reconcile outputsSmart Routing prevents unnecessary callsAI never validates itself

ESG Architecture

Sustainable Intelligence by Design

NeuralOps improves ESG performance by reducing unnecessary computation while preserving human accountability and organisational control.

Environmental

Reduced unnecessary AI inference
Lower token, compute and power demand
Lower cooling and water demand
Longer life for existing control infrastructure
Support for local, appropriately sized models

Social

Engineers remain in control
Safer decision-support workflows
Reduced repetitive clerical workload
Improved access to engineering intelligence
Local technical capability development

Governance

Independent validation and human approval
Audit trail and role-based access
Data sovereignty and controlled deployment
Clear processing accountability
No model self-approval

Live ESG and Processing Demo

Select an Engineering Task

Task SIMULATED ENGINEERING DATA - FOR DEMONSTRATION ONLY

SELECTED PROCESSING ROUTEVERY LOW COMPUTE

Processing Logic

How NeuralOps Thinks

A governed signal path from acquisition to qualified human review.

01Acquire

Read sensor, PLC, controller and DAQ signals.

02Parse

Standardize protocols, formats and machine events.

03Route

Select rules, parser, detached system, SLM or LLM according to complexity and risk.

04Detect

Identify anomalies, drift and abnormal operational patterns.

05Validate

Verify recommendations independently from the generating model.

06Approve

Require qualified human review for critical action.

View retained monitoring capabilities

Reads Sensor Data

Collects sensor and machine data through configured polling, event, streaming or historian interfaces from compatible PLCs, controllers and instruments.

Monitors PLC Signals

Monitors PLC and controller signals to ensure proper operation and detect signal anomalies.

Detects Abnormal Readings

AI-assisted anomaly detection identifies unusual patterns for engineer review before they cause failures.

Logs Alarms & Events

AI-assisted alarm correlation, failure-pattern analysis and root-cause hypothesis generation for fab equipment incidents, with compliance support and engineer-led confirmation.

Generates Technician Alerts

Immediate notifications to technicians via multiple channels when issues arise.

Creates Daily Reports

Automated daily machine health reports with trends and performance insights.

Predictive Maintenance

Condition-based monitoring and predictive-maintenance indicators can provide early warning of developing equipment degradation when sufficient validated historical and sensor data are available.

Connects Supported Systems

Integrates compatible databases, APIs, dashboards, controllers, historians and AI services through deployment-specific interfaces.

Builds Detached Systems

Supports deployment of independent supervisory monitoring modules alongside existing machines, subject to site assessment, interface validation, cybersecurity controls and planned installation procedures.

Interactive Demonstration

NeuralOps Engineering Control Demonstration

Explore how NeuralOps observes signals, detects abnormalities and routes engineering decisions without bypassing existing control systems.

Demo only NeuralOps never bypasses PLC, safety relay, interlock or emergency stop. Critical actions stay under qualified engineer approval, and approved corrective actions must remain within validated operating limits.

SIMULATED ENGINEERING DATA FOR DEMONSTRATION ONLY

LIVE SIGNAL PANELVacuum Pump

Sensor

PLC / DAQ

Parser

Smart Routing

Detached System

Validation

Engineer

NEURALOPS DECISIONNORMAL

Condition-based monitoring and predictive-maintenance indicators that can provide early warning of developing equipment degradation when sufficient validated data are available.

Monitoring

Browse the component library or use filters

Layered Schematic

System Architecture

NeuralOps operates as a supervisory and decision-support layer. It does not replace deterministic machine control, certified safety functions, emergency shutdown systems or machine interlocks.

Physical EquipmentSensors, instruments, pumps, motors, semiconductor equipment and production machinery remain the physical source of process signals.

Capabilities shown are deployment patterns, not universal guarantees. Final functionality depends on equipment compatibility, sensor coverage, protocol support, data quality, cybersecurity design, validation scope, operating procedures and customer approval.

Fail-Safe Operation

AINNA NeuralOps provides monitoring, anomaly detection and decision support. Safety-critical commands and production changes require authorised human approval before execution. Routine non-safety actions may be automatically executed only when they are explicitly pre-authorised, bounded by validated operating limits, protected by existing PLC, DCS, safety relay and interlock logic, and supported by a documented fallback procedure.

Deployment-Validated Latency

Critical alert and monitoring-loop performance is validated for each deployment.

Scalable Architecture

Designed to scale from single equipment to distributed systems after capacity validation.

Configurable Security

Supports encryption, role-based access and audit trails when configured for the deployment.

Applications

Engineering Scenarios

Each scenario shows the observed signals, processing route, output and required human decision point.

01Pressure Calibration Monitoring

Engineering problemPressure reference and device-under-test comparison

Signals observedPressure values, stability and calibration events

Processing routeDeterministic parser + trend rules

Output generatedCalibration verification report

Human decisionEngineer accepts calibration outcome

Expected benefitImproved traceability and earlier drift visibility

02Air Pump Pressure Stability

Engineering problemUnstable pneumatic pressure affects test consistency

Signals observedPressure, cycle timing and regulator state

Processing routeTime-series trend analysis

Output generatedStability alert and inspection recommendation

Human decisionTechnician verifies regulator and pump

Expected benefitMore consistent monitoring and planned intervention

03NI Multiplexer Automation

Engineering problemManual channel switching creates slow, fragmented tests

Signals observedChannel state, measurement and sequence events

Processing routeParser + detached sequence workflow

Output generatedStructured test record and exception alert

Human decisionEngineer approves sequence changes

Expected benefitRepeatable measurement workflow

04Semiconductor Alarm Analysis

Engineering problemFab alarm sequences are difficult to correlate

Signals observedAlarm codes, chamber state and event timing

Processing routeRules first; AI only for complex context

Output generatedPrioritised event summary and possible causes

Human decisionEquipment engineer reviews recommendation

Expected benefitFaster engineering triage

05SMT Production Downtime Analysis

Engineering problemDowntime events lack consistent classification

Signals observedLine state, stop reason and event duration

Processing routeEvent parser + deterministic classification

Output generatedDowntime report, loss categorisation and OEE analysis support

Human decisionProduction engineer validates category

Expected benefitClearer operational reporting

06Cleanroom Monitoring

Engineering problemEnvironmental excursions require timely review

Signals observedHumidity, temperature and particle count

Processing routeThreshold rules + trend validation

Output generatedAlert, event log and escalation record

Human decisionQualified staff assess compliance response

Expected benefitImproved environmental visibility

07Predictive Maintenance

Engineering problemDegradation patterns can be missed between inspections

Signals observedVibration, current, temperature and runtime

Processing routeCondition-based monitoring and predictive-maintenance indicators

Output generatedMaintenance recommendation

Human decisionMaintenance engineer schedules action

Expected benefitEarlier evidence for maintenance planning when validated data are available

08Sensor Drift Detection

Engineering problemMeasurement drift can affect process interpretation

Signals observedSensor value, reference trend and calibration history

Processing routeSensor-history monitoring and potential drift detection

Output generatedCalibration verification recommendation

Human decisionInstrumentation engineer approves adjustment

Expected benefitBetter measurement confidence after reference confirmation

09Production Yield and Reject Analysis

Engineering problemQuality patterns are fragmented across process data

Signals observedYield, reject reason and equipment event context

Processing routeStatistical monitoring + contextual review

Output generatedTrend summary and investigation queue

Human decisionProcess engineer determines corrective action

Expected benefitBetter structured engineering review

Semiconductor Operations

From Wafer Process Signals to Engineering Decisions

A simulated control-room view of process context, anomaly routing and human review.

SIMULATED ENGINEERING DATA FOR DEMONSTRATION ONLY

WAFER PROCESS MAPLOT DEMO-04

ETCH-01Vacuum trend under reviewEngineer approval required for corrective adjustment

VACUUM TREND-72.4 kPaSimulated · stable band

TEMPERATURE ZONES3 / 3Simulated · monitored

PARTICLE MONITORNormalLabelled status

ALARM TIMELINE1 reviewNo machine command

YIELD TRENDContext onlyNo production claim

REVIEW QUEUE2 pendingSimulated approvals

SELECTED EVENTINDEPENDENT VALIDATION

AcquireChamber pressure and valve-state context received
DetectSlow deviation from configured demonstration baseline
RouteDeterministic trend analysis selected
RecommendVerify calibration and inspect seal condition
ControlDirect movement blocked; engineer review required

Central Control System

Supervisory Command Centre

DEMO ONLINEISOLATED SIMULATION

Plant OverviewNormalDemonstration state

Active AlarmsAttention1 review event

Signal IntegrityVerifiedLocal dataset

Data AcquisitionNormalSimulated packets

Parser StatusReadyDeterministic route

Detached SystemsMaintenanceDemonstration label

AI Analysis QueueAttention1 contextual task

Engineer ApprovalsWarning2 pending review

Critical PathCriticalHuman gate enforced

External Control LinkOfflineDemo remains isolated

Safety & Governance

NeuralOps observes, analyses and recommends.

Existing industrial controllers and safety systems remain authoritative. AINNA NeuralOps operates as a supervisory and decision-support layer. It does not replace deterministic machine control, certified safety functions, emergency shutdown systems or machine interlocks.

Systems NeuralOps Works With

PLC retained as primary controller
SCADA and HMI retained as primary supervisory monitoring and operator-interface systems
DCS retained as primary control system
DAQ and calibration systems remain operational

Systems NeuralOps Never Bypasses

Safety relay
Machine interlock
Emergency stop
Qualified engineer approval
Regulatory control procedures

DetectionRecommendationIndependent ValidationEngineer ReviewApproved Workflow

AI RecommendationDirect Machine MovementBLOCKED HUMAN APPROVAL REQUIRED

Critical Machine Movements & Safety Interlocks

All critical machine movement, safety interlock, calibration approval and production adjustment operations require human engineer approval. Routine non-safety actions may be automatically executed only when they are explicitly pre-authorised, bounded by validated operating limits, protected by existing PLC, DCS, safety relay and interlock logic, and supported by a documented fallback procedure. Human approval does not replace engineering controls. Approved actions must still remain within validated operating limits and pass applicable controller, interlock and safety checks.

Deployment Assurance

Performance & Security Controls

Deployment-specific engineering controls without absolute performance or security claims. Capabilities shown are deployment patterns, not universal guarantees. Security depends on correct configuration, credential management, patching, network segmentation and operational monitoring.

Controlled Deployment

Supports on-premise, private cloud, controlled VPS and hybrid deployment patterns.

VPN-Protected Inference

Inference traffic is designed to be protected through deployment-specific VPN, encryption, access control, network allowlisting and audit logging.

Private Inference Boundary

Production inference endpoints can be deployed behind customer-controlled VPN and network allowlists. No inference endpoint is exposed publicly when the private deployment configuration is correctly implemented.

Role-Based Access

Engineering roles, review permissions and escalation paths can be configured per deployment.

Audit Logging

Signal events, recommendations, validation outcomes and approvals can be recorded.

Independent Validation

Recommendations can be checked separately from the model or route that generated them.

Validated Performance

Monitoring-loop latency and system capacity are tested against each deployment requirement.

Deployment Scalability

Capacity can be validated from a single equipment workflow to distributed supervisory systems.

Engineering Intake

Start an Engineering Pilot

Share the initial engineering context, then continue through AINNA's existing secure lead workflow.