Explore Neural Designer use cases by application area. Each area highlights representative problems and links to the detailed use case page.
Engineering and Technology
Engineering teams use predictive models to understand complex systems, optimize performance, and make decisions from process, sensor, simulation, and operational data.
Performance optimization
Model process behavior and identify the operating conditions that improve efficiency, output, or energy use. Neural Designer can evaluate many scenarios and help select the best control settings.
Predictive maintenance
Predict equipment condition before failures occur by learning from operating data, sensors, and historical behavior. This supports maintenance planning and reduces unexpected downtime.
Product quality improvement
Relate product features, process variables, and inspection data to final quality outcomes. The model helps teams understand quality drivers and improve production decisions.
Virtual sensing
Estimate measurements that are expensive, delayed, or difficult to collect directly. A virtual sensor turns available variables into real-time predictions for monitoring and control.
Natural and Life Sciences
Scientific applications often combine experimental observations, laboratory measurements, environmental records, and domain knowledge to predict properties or detect patterns.
QSAR and chemical modelling
Relate molecular descriptors or chemical structures to biological, chemical, or environmental behavior. These models support compound screening and rational design decisions.
Gas emissions reduction
Model emissions from operating conditions and identify settings that reduce environmental impact. This helps teams balance performance, compliance, and sustainability targets.
Virtual sensing
Estimate physical or chemical measurements from related variables when direct sensing is costly or unavailable. This is useful in laboratory, environmental, and process monitoring workflows.
Microarray data analysis
Analyze high-dimensional biological data to find patterns in genes, samples, or disease states. Machine learning helps turn many variables into interpretable predictive models.
Medicine and Health Sciences
Medical and health applications use clinical records, laboratory tests, biomedical measurements, and population data to support diagnosis, prognosis, and treatment decisions.
Medical diagnosis
Analyze patient variables to detect disease patterns and support earlier diagnostic decisions. Models can combine clinical, physiological, and laboratory data.
Medical prognosis
Predict future patient outcomes, disease progression, relapse risk, or mortality risk from available data. This helps prioritize follow-up and treatment planning.
Medical treatment
Estimate treatment response and support the selection of suitable interventions. Predictive models can help compare patient profiles and expected outcomes.
Human activity recognition
Use sensor data to recognize activity patterns for health, monitoring, and biomedical applications. These models transform time-dependent signals into meaningful categories.
Business and Economic Sciences
Business applications transform customer, transaction, market, operations, and employee data into models that predict behavior, risk, demand, and future outcomes.
Fraud detection
Identify suspicious transactions, claims, or payments from historical patterns. Predictive models help teams detect risk earlier and reduce financial losses.
Customer churn prediction
Predict which customers are likely to leave and identify the variables behind churn. This supports retention actions before the customer relationship is lost.
Customer segmentation
Group customers according to behavior, preferences, or response patterns. Segmentation models help make campaigns, offers, and communication more relevant.
Sales forecasting
Forecast future sales or demand from historical data and related variables. This supports planning, budgeting, inventory, and resource allocation.
Customer risk assessment
Estimate the probability of default, loss, or another defined adverse outcome from customer and transaction variables. Risk models should be validated against the cost of false approvals and false rejections.