AI-powered digital assistant

A platform that allows developing, deploying, and launching an industrial enterprise's digital twin in just 4 weeks.
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Significant reduction of human factor influence on processes
Reduced maintenance and repair costs and accurate prediction of breakdowns
Accurate prediction of the volume and quality of finished products
Optimization of expensive material usage
Ensuring required market growth in the production volume of the product
Elimination of bottlenecks that reduce overall productivity

Create dashboards on the fly to suit your needs

By 2035, artificial intelligence-based technologies could increase labor productivity by up to 40%.

Digital assistants for all industries

Food production
Mining and extraction
Metallurgy
Chemical industry

Food production

Enterprises in the food industry are adopting AI technologies to optimize production processes, enhance product quality, improve efficiency, and even create innovative types of food products.
Optimization of production processes
AI enables the analysis of large volumes of data on production operations, such as temperature, pressure, processing time, and many other parameters. Based on this analysis, AI systems can optimize production parameters to achieve maximum efficiency and minimize losses.
Quality control
Computer vision and AI systems can automatically scan products on the production line to detect defects such as cracks, spots, or irregularities. This helps reduce the amount of defective products and ensures higher product quality.
Demand forecasting
By analyzing sales data, customer preferences, weather conditions, and other factors, AI systems can more accurately forecast demand for products. This helps businesses plan production, reduce excess inventory, and meet customer needs more effectively.
Product personalization
AI enables the creation of products tailored to individual customer needs, such as developing dietary products or products for individuals with specific allergies.
Development of new products and recipes
AI can analyze vast amounts of data on consumer taste preferences, as well as the chemical properties of ingredients. This enables the development of products and optimization of recipes in accordance with preferences.
Inventory management, and logistics
AI systems can monitor ingredient and finished product inventory levels, as well as optimize logistical processes by predicting optimal time intervals for orders and deliveries.

Cases

Cost forecasting
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Production and logistics optimization
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Mining and extraction

The application of artificial intelligence in mining and beneficiation plants allows for the optimization of extraction, enrichment, and production operations, ultimately leading to increased industry efficiency, reduced environmental impact, and ensuring personnel safety. The complexity and diversity of processes underscore the importance of employing innovative approaches.
Optimization of mining processes
AI is capable of analyzing data on the composition of rock formations, geological characteristics, and historical data to optimize mining strategies, increase the yield of valuable minerals, and reduce the environmental impact.
Forecasting and control of beneficiation processes
The use of AI enables the prediction of parameters for mineral enrichment and automates management processes, thereby enhancing the quality of the final product.
Equipment condition monitoring
AI systems can monitor equipment in real-time, identify malfunctions, and predict the time until the next maintenance, contributing to reduced downtime and increased reliability.
Safety management
"AI can analyze safety data in the workplace, identify potential risks and emergency situations, and take measures to prevent incidents.
Energy consumption management
The application of AI allows for analyzing energy consumption in various production operations and identifying opportunities for optimizing energy consumption.
Production planning, and optimization
AI is capable of analyzing data on resources, execution times, and other parameters to create optimal production plans and resource allocation

Cases

Flotation
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Milling
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Metallurgy

The application of artificial intelligence in the metallurgical industry allows for the refinement of traditional production methods, improvement of product quality, and enhancement of process efficiency, which is a crucial factor for successful competition in the global market.
Optimization of mining processes
AI provides analysis and classification of defects in metallic materials based on visual data processing. This enables early detection of flaws and enhances product quality, which is particularly crucial for the use of metals in critical industries.
Forecasting and management of beneficiation processes
AI helps optimize resource allocation, minimize time delays, and reduce logistics costs, contributing to effective supply chain management in metallurgy
Equipment condition monitoring
Utilizing machine learning methods, AI assists in the development of new materials with specified characteristics, optimizing alloy compositions and material properties for specific applications.
Safety management
AI systems can monitor the condition of equipment, predict potential failures, and recommend optimal times for preventive maintenance, contributing to reduced downtime and increased equipment reliability
Energy consumption management
The use of AI allows for the analysis of large volumes of data on production parameters, energy consumption, raw material quality, and final product quality. This contributes to more accurate forecasting and optimization of production cycles, reducing energy costs and enhancing overall process efficiency

Cases

Coke production
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Optimization of ferroalloy consumption
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Chemical industry

The chemical industry plays a significant role in producing materials and products essential for various sectors. However, the complexity of manufacturing processes, the need to adhere to high safety and environmental standards, and the instability of market conditions make the use of artificial intelligence (AI) crucial for optimizing and improving operations in this field.
Modeling and optimization of chemical processes
AI enables the creation of precise models for chemical reactions and processes, contributing to the optimization of parameters, increased production output, and reduced waste in the industry.
Safety and risk analysis
AI helps monitor the parameters of production processes in real-time, identifying potential hazards and preventing emergency situations, thereby enhancing the safety of personnel and equipment.
Optimization of energy consumption
AI is used to analyze data on energy consumption in various production processes, identifying opportunities for reducing energy costs.
Demand and price forecasting
By utilizing data on market trends and forecasts, AI helps predict demand for chemical products and optimize production plans.
Quality control of production
AI systems are capable of analyzing data on the chemical composition of products and identifying even the smallest defects, thereby improving the overall quality of the products.
Development of new materials and products
AI helps identify optimal combinations of chemical components for the development of new materials with desired properties.

Cases

Virtual sensors
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Optimization of operations using the rectification column as an example
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A platform that allows you to develop, deploy, and launch an industrial plant's digital twin in just 4 weeks.
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A platform that allows you to develop, deploy, and put into operation a digital twin of an industrial enterprise within 4 weeks.
Access to the demo

How does Ditwinx integrate into the process?

Publications

Analytics and Forecasting Based on AI
Softline Digital proposed to develop a service based on the Ditwinx platform, allowing the client to build forecasts interactively. This service was created for the rapid development, deployment, and operation of artificial intelligence-based solutions, helping clients optimize technological processes.
May 22, 2022
Digitization of Mining and Mineral Processing Industry
The implementation of digital solutions based on machine learning and computer vision in mining and ore processing production can significantly enhance process efficiency and reduce metal losses. Such advanced systems have great potential for optimizing and automating various stages of production.
April 19, 2022
Digital Twins and Industrial Safety
A digital twin is a software representation of a real process, simulating its functioning under conditions of interference and the surrounding environment.
December 23, 2021
All Publications

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