Artificial Intelligence

Artificial Intelligence Solutions That Deliver Measurable Field Results

At M&L Technology, we use artificial intelligence not only for theoretical analysis or standalone software but also to produce measurable results in real business operations.

We analyze information collected from production lines, cameras, machinery, sensors, ERP systems and IoT devices to develop solutions for production counting, quality inspection, object detection, fault analysis, production forecasting and process optimization.

Each project is designed according to the company's workflow, data sources, machinery and operational objectives. AI models are trained with real field data, tested under actual working conditions and improved continuously.

Customized Artificial Intelligence Development

Every organization has different production methods, products, camera conditions, sensor infrastructure and decision processes.

Instead of relying on one-size-fits-all models, we develop systems customized for the actual requirements of the business.

The initial stage defines the problem, available data sources and measurable success criteria. Data is then collected, prepared and used to train the AI model.

The model can operate with web, desktop and server-based applications and can integrate with PLCs, NetRelay IoT devices, sensors, ERP systems and field equipment.

AI-Powered Computer Vision

Camera feeds can be analyzed in real time through artificial intelligence algorithms.

People, vehicles, products, packages, pallets, production components, protective equipment and business-specific objects can be detected, classified and tracked automatically.

The system can measure object position, direction, speed, time spent within a defined area and relationship with other objects.

This capability can support production counting, quality inspection, area monitoring, vehicle tracking, occupational safety and automated event management.

Real-Time Object Detection

AI models can detect objects in live camera feeds.

In addition to standard classes, custom models can be trained for products, components, packages and equipment unique to the business.

Each detected object can receive a unique tracking identity, preventing duplicate counts.

The system can create automated events when an object enters a defined area, moves in a selected direction or remains at the same location for a specified duration.

Automated Production Counting

Products moving through conveyors and manufacturing lines can be detected and counted automatically through cameras.

Products moving close together, in different directions or with different shapes can be handled through project-specific tracking algorithms.

Production quantities can be stored by product, machine, line, shift and date.

Actual production can be compared with targets, while automatic notifications can be generated when production speed falls or no product passes for a defined period.

AI-Powered Quality Inspection

Quality problems occurring during production can be detected through camera images.

Custom models can identify missing components, incorrect assembly, deformation, surface defects, color differences, incorrect labels, open packaging and unsuitable product orientation.

When a defective product is found, its image can be stored with the defect category, date, time and production-line information.

The result can be transferred to a PLC, NetRelay device or automation system to remove the product, stop the line or alert an operator.

Production Forecasting

Historical production, shift information, machine operating times, downtime, order volume and environmental conditions can be analyzed to estimate future output.

Forecasting models can support daily, weekly and product-based planning.

Businesses can evaluate whether available capacity can meet planned orders.

Production forecasts can support workforce planning, raw-material purchasing, delivery dates and machine-capacity decisions.

Demand and Inventory Forecasting

Sales history, seasonal changes, campaigns, customer behavior and inventory levels can be analyzed to estimate future demand.

Demand forecasts may reduce the risk of both stock shortages and excessive inventory.

Separate models can be created by product, category, customer group and sales channel.

Forecast results can be transferred to ERP, inventory and purchasing systems.

Process Optimization

Production speed, machine downtime, waiting periods, quality results and workflow information can be analyzed together to identify inefficient areas.

Artificial intelligence can reveal which production stages create the greatest delay or under which conditions defect rates increase.

These insights can be used to reorganize production sequences, improve resource use and reduce operational bottlenecks.

The system can support managers by predicting risks instead of only reporting historical information.

Machine Downtime and Fault Analysis

Information collected from PLCs, sensors, NetRelay digital inputs and production software can be analyzed to classify machine downtime.

Fault codes, maintenance records, vibration, temperature, current and operating times can be evaluated together.

When patterns repeatedly appear before a failure, the system can provide an early warning to the maintenance team.

This approach can help reduce unplanned downtime and improve maintenance planning.

Predictive Maintenance

Predictive maintenance systems analyze the current condition and historical performance of machinery to estimate possible failures.

Vibration, temperature, energy consumption, pressure, cycle time and fault records can be used as model inputs.

The AI model learns normal machine behavior and identifies abnormal changes.

When risk increases, a maintenance task can be created, technical personnel can be notified or a controlled shutdown can be initiated.

Anomaly Detection

Unusual changes in production, energy, network, sensor and device data can be identified automatically.

Unexpected production drops, unusually long cycle times, sudden energy increases and abnormal sensor measurements can be marked as anomalies.

Not every possible condition must be defined in advance. The system can learn normal behavior and identify deviations.

Events can be displayed in the management dashboard and delivered to relevant teams according to priority.

Energy Consumption Analysis

Energy information collected by machine, production line and facility can be analyzed through AI.

Energy consumption per product, shift-based usage and consumption during inactive periods can be calculated.

Machines consuming more energy than expected under similar conditions can be identified.

Consumption forecasting and anomaly detection can reveal opportunities for improving energy efficiency.

Occupational Health and Safety

AI-powered cameras can verify whether employees use the required personal protective equipment.

Safety helmets, reflective vests, protective glasses and business-specific equipment can be detected.

Entry into hazardous areas, unsafe proximity between forklifts and people and waiting in restricted zones can be identified automatically.

Following an event, warning lights or sirens can be activated through NetRelay and image-based notifications can be sent to safety teams.

Fire, Smoke and Abnormal-Event Detection

Smoke, flame and unusual visual changes in camera feeds can be analyzed through artificial intelligence.

The system can provide an additional early-warning capability in factories, warehouses, agricultural areas and outdoor locations.

When smoke or flame is detected, the event image can be stored, notifications can be sent and field alarms can be triggered.

This capability does not replace certified fire-detection systems and should be used as an additional monitoring layer.

Thermal Image Analysis

Thermal camera feeds can be analyzed to detect abnormal temperature changes in machinery, electrical panels and products.

Automatic alarms can be generated when defined temperature limits are exceeded.

Thermal data can be combined with historical production and failure records to analyze relationships between overheating and equipment faults.

Fans, ventilation or safe-shutdown systems can be activated through NetRelay when required.

Text and Document Analysis

Artificial intelligence can also be used to process business documents and text records.

Service records, production notes, customer requests, fault descriptions and technical reports can be classified.

Recurring customer problems, common fault descriptions and issues related to specific products can be grouped automatically.

Document information extraction, summarization and routing to relevant departments can be implemented.

Intelligent Reporting and Decision Support

Production, quality, maintenance, sales and IoT information can be combined to create management decision-support dashboards.

AI systems can interpret deviations and risks rather than only displaying current values.

The system can identify which line is behind target, why defect rates are rising or whether an order is at risk of delay.

Reports can be displayed in web dashboards or delivered automatically in Excel and PDF formats.

ERP and Enterprise Software Integration

AI solutions can integrate with Logo Tiger and similar ERP, inventory, order, production and customer-management platforms.

Product, order, sales, inventory, customer and historical transaction information can be used for analysis.

Forecast, quality, production and risk results can be transferred back to the ERP platform.

Integrations can be developed through REST APIs, SQL Server, PostgreSQL, MySQL, Oracle, XML, JSON, CSV and Excel.

NetRelay IoT and AI Integration

The NetRelay IoT platform connects AI software with sensors and physical field equipment.

Digital inputs can collect machine status, production pulses, door conditions, alarms and fault signals.

Analog inputs can collect temperature, humidity, light, pressure and similar measurements.

The AI system can analyze this information and use NetRelay relays to control fans, pumps, sirens, warning lights, barriers and product-separation equipment.

HTTP GET/POST, MQTT, WebSocket and TCP can provide real-time communication between AI software and NetRelay.

Automated Field Actions

When an AI system detects a problem, it can do more than create a notification. It can also control field equipment automatically.

A separation mechanism can remove defective products. A siren can activate when someone enters a dangerous area. A fan can start when machinery overheats.

Every automated action can be logged and reported to authorized users.

Safety conditions and manual override options are considered separately in critical control scenarios.

Hotspot and Network Data Analysis

In IDNet-based hotspot and network projects, user density, session duration, access time and connection behavior can be analyzed.

Usage patterns can be evaluated by time and location in hotels, schools, dormitories, businesses and shared areas.

Unusual sessions or high-demand areas can be identified to support network-management decisions.

Processing of connection and user information should be restricted according to applicable regulations, security rules and access permissions.

Data Sources and Integrations

AI models can work with information obtained from cameras, sensors, machinery, PLCs, ERP platforms, databases, APIs and files.

SQL Server, PostgreSQL, MySQL and Oracle databases can be connected.

JSON, XML, CSV, Excel and plain-text files can be processed.

Real-time data can be collected through HTTP, MQTT, WebSocket and TCP.

Information from different sources can be matched by time, device, product, order and production-order identifiers.

Web-Based AI Management Dashboard

AI results can be monitored through a centralized web dashboard.

Live production counts, detected objects, quality defects, forecast results, sensor values and alarm records can be displayed together.

Users can filter data by date, production line, product, camera, machine and defect type.

Separate interfaces and permissions can be created for managers, operators, quality teams, maintenance personnel and technical support.

On-Premise and Cloud Operation

AI systems can operate on local servers, cloud platforms or hybrid infrastructures according to security and performance requirements.

In projects where camera images should not leave the facility, analysis can be performed entirely on local servers.

Summary information and reports can be transferred to cloud systems.

Local analysis and data recording can continue during internet outages, followed by synchronization when communication is restored.

Model Training and Data Preparation

The success of an AI project depends on data quality and how accurately it represents real working conditions.

Camera images, production records, sensor measurements and historical fault information are collected and prepared according to the project objective.

Objects and defect categories are labeled for computer-vision projects. Missing, incorrect and duplicate records are cleaned for forecasting projects.

Training and test datasets are separated to evaluate performance on previously unseen examples.

Field Testing and Model Validation

A model that performs well in a laboratory must also be tested under actual field conditions.

Lighting changes, camera vibration, product speed, background, sensor differences and machine conditions can affect performance.

Accuracy, false alarms, missed detections, processing speed and system stability are measured during the pilot phase.

The model can be retrained or adjusted using new field data.

Measurable Success Criteria

AI projects should not be evaluated only through model accuracy.

Measurable indicators showing the contribution to the business are defined before development begins.

• Reduced manual counting errors
• Shorter quality-inspection time
• Earlier detection of defective products
• Reduced unplanned downtime
• Earlier identification of equipment faults
• Improved production-forecast accuracy
• Reduced energy consumption
• Reduced manual reporting time
• Faster response to safety events
• Improved production-target achievement

Example Artificial Intelligence Project

On a packaging line, a camera system detects and counts products moving along the conveyor in real time.

The AI model checks the product cover, label position and packaging integrity. When a defective product is identified, its image is stored.

A pneumatic separator is activated through a NetRelay relay. When the defect count exceeds the defined threshold, the quality manager is notified.

Operating and downtime information collected from PLCs and sensors is combined with production data. The AI system identifies production-speed drops and unusual downtime.

At the end of the day, accepted products, defective products, defect types, production speed and downtime are reported automatically.

Example Predictive-Maintenance Project

Temperature, vibration, current and operating-time information is collected from a production machine.

The AI model learns normal operating behavior and marks abnormal measurements as risk indicators.

When risk increases, the maintenance team receives a notification and an inspection task is created.

Maintenance results and actual fault information can be fed back into the model to improve future predictions.

Applications of Our Artificial Intelligence Solutions

• Automated production counting
• Object detection and tracking
• Visual quality inspection
• Missing-component and assembly-error detection
• Production and capacity forecasting
• Demand and inventory forecasting
• Machine downtime and fault analysis
• Predictive maintenance
• Energy-consumption analysis
• Anomaly detection
• Occupational safety and area monitoring
• Fire, smoke and thermal-image analysis
• Intelligent reporting and decision support
• IoT sensor-data analysis
• ERP and production-software integration

Business Benefits

Artificial intelligence transforms disconnected field information into meaningful and usable business insights.

Production, quality, maintenance, energy and safety information can be evaluated together to create a complete operational view.

Managers can evaluate not only what happened in the past but also potential future risks and outcomes.

Manual inspection and reporting workloads can be reduced while response to critical events becomes faster.

Scalable Artificial Intelligence Infrastructure

A project can begin with one production line, camera, machine or analytical model.

After a successful pilot, additional cameras, sensors, machines and AI models can be added.

Data from different facilities and branches can be combined within one central platform.

The modular architecture allows new features and integrations without replacing the complete system.

Our Artificial Intelligence Project Process

1. Requirements analysis: The field problem and project objectives are defined.
2. Data assessment: Cameras, sensors, machinery, ERP platforms and existing records are reviewed.
3. Pilot design: Devices, software infrastructure and success criteria are selected.
4. Data collection: Suitable examples are obtained from the real working environment.
5. Model development: The AI model is trained and tested.
6. Integration: The model is connected with web, desktop, ERP, PLC and IoT systems.
7. Field testing: The system is validated under actual operating conditions.
8. Deployment: The solution is activated and measurable results are monitored.
9. Continuous improvement: The model and software can be improved with new information.

Transform Artificial Intelligence into Real Business Results

At M&L Technology, we do not approach AI projects as isolated model-development exercises. We create end-to-end solutions that connect cameras, sensors, IoT devices, machinery and enterprise software.

Project performance is measured through production increases, defect reduction, downtime, energy consumption, response time and reporting efficiency.

For artificial intelligence, computer vision, production and custom-software solutions, visit M&L Technology. For sensor, digital-input and relay-based field control, visit NetRelay. For hotspot, guest-network and network-management solutions, visit IDNet.