Launch is a unified AI platform designed to solve real business challenges. It analyzes video, audio, and documents, detects deviations, and transforms raw data into clear, actionable business insights. The platform eliminates manual monitoring, reduces operational risks, and enables fact-based decision-making.

Technology

The Launch architecture is designed as a scalable, high-load system that supports real-time data streaming, flexible ML model integration, and configurable business logic.

The platform includes built-in capabilities for:

• connecting any data sources (video, audio, documents, images);
• running and orchestrating ML models;
• generating alerts, notifications, and events;
• building analytical reports and dashboards;
• integrating with external systems via APIs, webhooks, and the USEBUS integration bus.

Launch is engineered for high throughput and large data volumes, supports horizontal scaling, and delivers high availability and fault tolerance

With Launch, companies can:

• monitor safety, quality, and regulatory compliance;
• detect process issues and deviations in real time;
• reduce losses, downtime, and reliance on the human factor;
• deploy AI solutions faster without complex IT projects;
• scale proven use cases across the entire organization.

Business Impact

• Fast time to value: pilot deployment in as little as 48 hours
• Reduced operational losses and error rates
• Increased process transparency
• A unified, company-wide approach to AI adoption

<p>The post Launch first appeared on Usetech.</p>

USEBUS supports a wide range of installation options, including on-premises installation and cloud deployment, ensuring data protection from unauthorized access

• Real-time Data Processing. Allows continuous data flow and processing with minimal delays.
• AI/NO/Low-Code Tools. Empowers non-developers to create integrations through visual, user-friendly interfaces.
• AI and Automation. Uses AI for smart workflows, decision-making, and automating repetitive tasks.
• Strong Security. Incorporates encryption, compliance, and secure data handling mechanisms.
• Business Efficiency. Dramatically decreases time to market for your integrations and reduces integration costs at average 30%-50%.

Gain a competitive advantage

• Fast time-to-market integrations. Allows you to create and change integrations in a few clicks, significantly reducing the cost of developing and maintaining automations.
• Different integration tools in one. Includes all popular ways to build data streams, from possible extensions to the rare and unique.
• From single stand to hybrid infrastructure. Installed in “a couple of clicks” – both on local servers and clouds, forming a unified architecture of the distributed IT landscape of organizations.
• AI “suggests” – human “decides”. The application architecture enables the use of public LLMs and local AI to create integrations, manage data, and improve data quality.
• Data protection against unauthorized access. Built-in role-based access model for data protection, as well as encryption and security auditing mechanisms, including detection of anomalous behavior.

USEBUS is multiple products in one

• USEBUS is a high-performance ESB.
• USEBUS is an industrial data processing conveyor.
• USEBUS is a high performance batch ETL.

The next stage of evolution is integration platform USEBUS

The longer you use USEBUS, the cheaper it becomes by reusing established routes and additional components.

Benefits

• Includes all the pros of ESB and negates the cons.
• Multiple products in one interface for different integration interactions.
• No vendor lock, USEBUS is a modular product where interface acts as a superstructure for open-source technology: IT systems data handlers, adapters and connectors can be supplemented without any restrictions.
• Ability to connect external components in any languages as a Low-code integration element.
• Low entry threshold allows the company to quickly expand the integration team by creating domain subteams in different areas.
• It is possible to separate cluster integrations accordingto business processes criticality, so that some of themdo not interfere with others.
• Ability to create truly complex integration routes.
• Almost any logic can be placed.
• The documentation process is built into the interface.

<p>The post USEBUS AI-Code first appeared on Usetech.</p>

OCTOPUS is an automatic data center resource optimizer / balancer that:

• Continuously keeps your server infrastructure in an optimal state.
• Can manage different types of hypervisors and orchestrators.
• Allows for semi-automatic balancing (with operator confirmation) if needed.

The solution brings the operation of western server hypervisors, which are the de facto standard (VMWARE, IBM, Oracle), as well as domestic ones, to the optimal mode of consumption of system resource of the data center, thus freeing up the system resource reserved in a non-purposeful way as a reserve for business-critical operation of the production resource of the data center.

OCTOPUS is created to solve the following customer challenges:

• Keeps your server infrastructure in an optimal state at all times.
• Can manage different types of hypervisors and orchestrators.
• If necessary, it allows you to perform balancing in semi-automatic mode (with operator confirmation).

Advantages:

• Support for semi-automatic and automatic modes: Octopus supports both semi-automatic (with administrator control) and automatic modes, providing additional customization and installation options to meet the unique needs of each customer.
• Assured resourcing: Octopus ensures that your applications and systems, including mission-critical and strategically important ones, are resourced. This increases the reliability of software applications used in your critical information infrastructure.
• Improved server hardware performance and resource savings: Octopus improves server hardware performance and saves data center resources by at least 30%.
• Reduce performance spikes and improve quality of service: Octopus intelligently manages resources to reduce performance spikes, resulting in better quality of service and more stable system performance.
• Reduce resource scarcity: With Octopus, you can reduce server hardware resource scarcity at the enterprise, industry, or national level by 30% or more.
• Increase hardware performance: real-time monitoring of CPU, HDD, RAM, LAN status and utilization allows you to quickly identify hardware problems and reduce the risk of failures and malfunctions.
• Guaranteed supply of resources for applications and systems, including business-critical and strategically important ones, increased reliability of software applications used in CII (FZ187).
• Improved reliability and availability of IT infrastructure: predictive analysis of potential application failures caused by lack of resources allows operators to take measures to prevent them. Informing the operator of hardware failures allows for quick response and remediation.
• Improved resource planning: Tracking unused reserved capacity, reserving IT capacity for current and future projects, and forecasting demand for IT capacity improves resource planning and reduces the cost of purchasing new capacity.
• Reduce energy costs: Capacity balancing optimizes resource utilization, reducing energy costs and lowering energy bills.
• Improved service delivery: By utilizing resources more efficiently, increasing IT performance, reducing downtime and improving IT reliability, manufacturing facilities can provide better service to their customers.

Benefits of OCTOPUS

• 24/7 automatic resources monitoring and improvement
• up to 10% decrease in routine tasks
• up to 0 decrease in time for manual resources distribution
• 30 minutes to install basic version into your network
• up to 30% release of computing capacities
• 3–4 times increase in data processing speed

We also offer piloting our solution in your environment and free training of your experts using our educational network.

<p>The post Octopus first appeared on Usetech.</p>

By integrating real-time data, digital twins provide a comprehensive overview of risk factors, failure dependencies, and the potential impacts of failures, thereby significantly improving safety and reliability in system operations.

Digital risk twin is a digital twin, or a digital representation of an intended or actual physical product that contains the necessary information in terms of functions, flows, components and other criteria that allows risk simulation to be undertaken, identifying the risks (failures) of a system during operation and their path of propagation through the system.

Possibilities

• Conducting thorough risk analysis and assessing the probability of their occurrence.
• Developing sophisticated threat models to anticipate potential issues.
• Identifying the most vulnerable areas within the production system.
• Selecting and implementing effective measures to avert emergency situations.

The DRT is used to automate

• It streamlines coordination among participants in the risk management process, ensuring that all actions align with established procedures and protocols. This promotes consistency and reduces the likelihood of errors.
• The digital risk twin can perform complex calculations involving vast amounts of data, essential for accurate risk assessment and decision-making. By leveraging advanced algorithms, it can analyze potential risks and their impacts more effectively than traditional methods.
• It acts as a dynamic risk register, where it stores not only risk forecasts but also historical events, allowing organizations to learn from past occurrences and improve future risk strategies.

Problems solved by the DRT

At the planning stage

• Collecting information on the basic production safety hazards and barriers.
• Registering changes (removing/improving current barriers and introducing new ones and/or eliminating risks).
• Changes effectiveness forecast involves risk identification, risk assessment (possible scenarios simulation), and calculating safety indicators established by the board of directors.

At the operational control stage (execution and control)

• Collecting information on the current production safety hazards and barriers.
• Online forecast of safety indicators established by the board of directors.
• Online analysis of the safety level and trends in case of deviation from planned performances:
• Operator’s monitor safety indicators exceeding acceptable limits – based on the online forecast.
• Reason (prerequisites) for increasing risk level – the failure of specific basic barriers (specific production operations).
• Possible risk consequences – affected areas, trends recommendations on the prompt introduction of temporary barriers to achieve production safety goals and performance assessment.
• On-site video monitoring of any risk event.

At the motivation stage (continuous improvement)

• Objective assessment of achieving the safety goals in operation process.
• Objective assessment of the barriers effectiveness.
• Objective assessment of the work of persons responsible for the barriers.
• Objective assessment of the work of other roles in risk management.

Results

• A marked reduction in the frequency of risk events, leading to a more stable operational environment.
• Decreased response times for prevention, allowing quicker adjustments and interventions.
• Optimization of production and business processes, enhancing overall efficiency.
• Improved safety measures at hazardous facilities, safeguarding both employees and assets.
• Enhanced production performance, which can lead to lower operational costs and increased profitability.

<p>The post Risk Digital Twin first appeared on Usetech.</p>

<p>The post Teal HR first appeared on Usetech.</p>

Advantages of technology

• Our measures and analyzes complex vibrations and determine precisely how they arise in nature 
(i.e. measurement approach corresponds with physical nature of vibration)
• The technology is based on measuring the three-dimensional vibration at any point on the surface
of an object and therefore takes into account spacial movement of vibrating point
• Building a more complete and informative trajectory image
• Objects’ total energy numerical evaluation (too high energy level leads to breakage/accident, shows 
state of the equilibrium, etc.)
• Always kept time information between axes (4D = 3D + time / synchronization)

Problematics:

Brief Description of the 4D Method
of Dynamic Balancing

• For balancing all three components X, Y and Z of the vector of disbalance are used.
• The vector of disbalance is considered the real vector of vibration witn maximum length wich is built
on rotational speed.
• For balancing new rotor with two balancing e it is necessary to make three runs: 
empty run
run with known weight on the left balancing plane (trial)
run with known weight on the right balancing plane (trial)
• After this the software returns: mass of the weight and its position (angle) on the left plane; mass
of the weight and its position (angle) on the right plane.
• After 4D balancing the total energy of the system was decreased by 2.5– 7 times comparing
to the conventional balancing of the same rotor.
• The method can be easily adapted to multy-plane balancing.

Vibromonitoring: the way to identify defects

Three-dimensional vibration measurement

Our method is based on three-dimensional vibration measurement at a surface point. This innovative method allows us to draw a detailed three-dimensional phase pattern, which would be substantially changed by any defect.

Three-dimensional vibration measurement

Reducing the number of iterations leads to a decrease in balancing weights and the total weight of the system to be balanced. The initial consideration of vector imbalance of components makes it possible to perform balancing with no additional adjustments.

Real-time vector measurement

Through real-time vibration vector measurement at a particular material surface point, our method ensures more informative data representation as compared to traditional techniques. This is due to the lack of phase dispersion among measurement channels or axes, which increases the informative value.

System energy assessment

Three-dimensional vector measurement allows us to assess the vibrational system energy in order to reduce total energy after one-dimensional balancing. This includes solving the problem of real three-dimensional vector imbalance and axial vibration.

Three-dimensional vibration measurement

Our method is versatile, it is suitable for dynamic balancing of all rotor types and allows us to perform balancing of a flexible rotor with 1–3 modes using the same number of iterations as for a rigid one. Such efficiency is achieved due to precise assessment and less non-matching vibrations on X and Y axes.

Theoretical Background

<p>The post Vibromonitoring Balancing Technology first appeared on Usetech.</p>

Purpose and Objectives

• Hydrocarbon Detection: the primary purpose of this technology is to directly detect the presence of hydrocarbons (oil and gas) under the surface.
• Reservoir Characterization: beyond mere detection, the technology aims to characterize the identified reservoirs.
• Risk Reduction in Exploration: by providing direct indicators of hydrocarbon presence, the technology reduces the risk associated with drilling dry wells.
• Cost Efficiency: the method is designed to be more cost-effective than traditional seismic exploration techniques.

Fundamental Principles

• Natural Low-Frequency Seismic Emissions: Hydrocarbon Resonance, Passive Seismic Method.
• Vector Analysis of 3D Microseismic Fields: Three-Component Geophones, Full Wavefield Capture.
• Low-Frequency Focus: Concentrating on the 2–5 Hz.
• Advanced Data Processing: Signal Processing Algorithms, Anomaly Detection and Mapping.

Typical Procedure for Conducting a PoC

Advantages Over Traditional Methods

• High Cost Effectiveness: Direct Detection, Reduced, Dry Wells, Reduced Survey Time.
• Minimal Environmental Impact: Minimal Disturbance, Regulatory Compliance.

Results

The developed 3D model determines hydrocarbon accumulation, allows the user to assess reserves, and provides recommendations for new wells location:

• Improved field development efficiency.
• Reduced drilling of empty wells and drilling cost savings.
• Integrated modeling and operational asset management.
• Improved accuracy of results and reduced probability of interpretation errors.
• Predict geologic structures and identify the most promising locations for hydrocarbon exploration.
• Minimize human error in seismic acquisition.
• Up to 7 km depth of hydrocarbon vibration detection.
• More than 85% direct prediction accuracy.

<p>The post Hydrocarbon Exploration Vectorial Analysis first appeared on Usetech.</p>

The introduction of Artificial Intelligence (AI) and Machine Learning (ML) offers a transformative solution. AI-powered energy-centered predictive maintenance systems leverage advanced algorithms to analyze real-time data streams from various sources, providing a holistic view of energy consumption patterns. These systems don’t just track energy use; they predict potential anomalies, equipment failures, and inefficiencies before they occur. This predictive capability enables proactive maintenance, minimizing downtime and preventing costly energy waste associated with inefficient operation. Furthermore, AI can identify optimal operating parameters for individual machines and processes, leading to significant energy savings. For example, in oil refining, AI can optimize the cracking process by analyzing real-time data on feedstock quality, temperature, pressure, and catalyst activity, dynamically adjusting parameters to maximize yield while minimizing energy consumption.

AI surpasses traditional methods by optimizing processes through sophisticated algorithms that analyze vast datasets encompassing historical production data, real-time sensor readings, market demand forecasts, and even external factors like:

• supply chain disruptions;
• efficiently allocate resources;
• predict potential failures;
• proactively adjust production schedules.

The integration of AI and ML into industrial energy management is a necessity.

Statistical Modeling of Oil Cracking Installations

AI can create sophisticated statistical models that accurately predict energy consumption based on various process parameters, allowing for optimized control strategies and proactive mitigation of energy-intensive anomalies.

Optimal Energy Consumption in Fractionation

AI can develop dynamic models for optimizing energy use in gasoline, diesel, kerosene, and fuel oil fractionation. This involves considering feedstock variability, product specifications, and energy costs, leading to significant reductions in overall energy intensity.

Fuel Consumption Forecasting

Accurate forecasting of fuel consumption is vital for efficient planning and resource allocation. AI-based models can integrate planned operational modes, historical data, and external factors to provide highly accurate predictions, enabling proactive adjustments to energy supply and reducing operational costs.

Identifying Best Practices

AI can sift through vast amounts of historical data to identify best practices in energy consumption, correlating them with specific production modes and operational parameters. This allows for the systematic replication of best practices across different units and facilities, driving consistent energy efficiency gains.

Addressing Measurement Anomalies

AI algorithms can detect and correct anomalies in data from measurement instruments, improving the accuracy and reliability of energy consumption assessments. This includes:

• identifying outliers;
• handling missing data;
• detecting sensor failures.

Virtual Metering for Raw Material Quality

AI can create virtual meters that estimate raw material quality and consumption, even in situations where direct measurement is impractical or unavailable. This allows for better control over the input materials and their impact on energy efficiency. These virtual meters leverage relationships between measurable parameters and raw material properties to generate accurate estimates.

Resource Consumption Model

As part of the project, a research analysis of data was carried out, parameters affecting energy consumption and quality characteristics of the energy resource were determined.

ML Model

1. Based on the input quality of raw materials, create a model that, given fixed output qualities, determines how much gas needs to be burned (for example, how much gas needs to be burned to get these petroleum products). In this way, it is possible to predict how much gas is needed to process N quantities of oil.
2. Recognition (detection) of the operating mode of an oil cracking unit ML model allowing to analyze oil cracking settings.
3. Decision-making system support and decision-making system that allows you to reduce fuel consumption per day and improve the quality of production.

Optimal Furnace Control System Development

To develop a system for furnace control, in order to:

• increase furnace equipment efficiency;
• decrease furnace gas consumption.

Process of heating of oil, which passes through the furnace.

The increase in furnace efficiency due to depression control.

Based on elaboration of relations among various data:

• the thermomechanical calculation is performed;
• the machine learning model to predict furnace efficiency is implemented;
• the probability of potential for optimization of depression values in furnace sections is calculated.

ML Model:

• furnace section efficiency for various modeling depression values.

Result:

• The economic capacity of optimal furnace control system implementation is 1.41%.

<p>The post Energy Consumption in Oil and Gas Industry first appeared on Usetech.</p>