Artificial intelligence (AI) is a layer in Smart RDM that analyzes, suggests, predicts, and supports users in making decisions – in real time and based on the full context of IT/OT data, energy, quality, environment, or, for example, maintenance.
This allows organizations to operate faster, more accurately, and more stably, without having to build their own AI infrastructure.
AI that really works. Securely. Scalably. On your data.
AI that understands industrial processes
Smart RDM analyzes data from:
- machines and devices,
- sensors,
- business systems,
- energy storage facilities, renewable energy sources, and technological systems,
- failure history, alarms, reports, and documentation,
- company process knowledge base.
The data is sent to a single Central Data Repository (CDR), where it is automatically “cleaned,” organized, and enriched with context so that it is ready for immediate use by Machine Learning (ML) models.
As a result, Smart RDM takes into account the full picture of the process, and the recommendations generated are reliable and consistent with the actual production conditions.
Advanced ML models – descriptive predictive and prescriptive analytics
Modern industrial analytics goes far beyond dashboards and historical reports.
Smart RDM combines descriptive, predictive, and prescriptive analytics to transform raw operational data into actionable decisions:
1. Descriptive analytics
Answers what is happening and why by analyzing current and historical data, correlations, trends, and anomalies.
2. Predictive analytics
Answers what is likely to happen next by forecasting failures, energy consumption, quality deviations, or process behavior.
3. Prescriptive analytics
Answers what should be done by recommending optimal actions, scenarios, and priorities based on predicted outcomes and operational constraints.
Together, these layers allow Smart RDM to move from monitoring and prediction to decision support and operational optimization.
Smart RDM uses various types of AI/ML algorithms, including:
- predictive models (failures, energy, quality),
- prescriptive models (best possible actions),
- anomaly detection, correlations, and trend analysis,
- RUL and process deviation prediction,
- “what if” scenario analysis,
- automatic generation of operational recommendations.
- classification and regression models for assessing technical condition and forecasting process parameters,
- hybrid learning combining AI with expert knowledge (physics-informed models / expert-augmented ML),
- filters and time models for signal smoothing, reconstruction, and prediction,
- decision tree algorithms and ensemble models for event classification and action prioritization,
- clustering for grouping devices, operating modes, and unusual behaviors,
- unsupervised learning to identify new, previously unseen failure patterns,
- signal and frequency spectrum analysis in vibration diagnostics,
- energy load prediction models for media consumption optimization.
Each predictive model goes through a full life cycle – from training and validation, through implementation, to automatic updating of key parameters based on new data, including information provided by operators during their daily work.
This ensures that the models remain resilient to process variability while being adapted to actual operating conditions and enriched with the knowledge and experience of production teams gathered over many years.
Smart Chat – an intelligent assistant available wherever you need it
Smart Chat is an AI widget available across the entire Smart RDM platform.
It works based on language models, knowledge retrieval (RAG), and full data context.
What can it do?
1. It answers questions about processes and data
Based on:
- documentation,
- event history,
- process parameters,
- reports, and data maps.
2. Creates automatic reports
Generates, among other things:
- machine performance summaries,
- energy reports,
- quality reports,
- production analyses,
- ESG reports.
3. Acts as a corporate knowledge assistant
Helps operators, technicians, and managers:
- find instructions,
- understand alerts,
- walk through procedures,
- perform tasks in accordance with best practices.
4. Supports voice commands
Ideal for:
- in high noise levels,
- when the operator’s hands are busy,
- in environments requiring quick responses.
Smart Chat allows you to work faster, more confidently, and without having to search through multiple systems.
Secure and ethical AI, working entirely within your environment
Smart RDM uses models:
- corporate (Azure OpenAI, Gemini),
- open-source (LLaMA, Mistral, Falcon, etc.),
- on-premise,
- in the cloud, or in hybrid mode.
When using AI models, it was crucial for us to maintain maximum security in line with international corporate standards.
Therefore:
- data does not leave the customer’s environment,
- full encryption and access auditing,
- compliance with industry requirements,
- support for data and retention policies,
- ethical operation without the risk of “knowledge leakage.”
AI in Smart RDM works where the process, industry, and regulations require it.
AI that supports every critical decision:
Production
Optimal settings, parameter stabilization, deviation detection.
Energy
Dynamic source selection prompts, RES/gas/DSR optimization, cost prediction.
Quality
Identification of causes of quality changes, setting recommendations.
Maintenance
Failure prediction, RUL, RCM/CBM, repair recommendations.
Sustainable development
Automatic data consolidation, reporting, environmental indicators.
AI works in all modules, creating a single, coherent layer of intelligence.
Why AI in Smart RDM is different
from standard systems:
- it works natively on IT/OT data in real time,
- covers the full production, energy, and environmental context,
- it has Smart Chat with RAG and voice support,
- supports automatic reporting and analytics,
- it can run locally or in the cloud,
- constantly learns and improves,
- is secure and compliant with standards,
- delivers real results from the very first weeks.
It is intelligence that increases the digital maturity of an organization
and supports fast, accurate, data-driven decisions.