This solution is particularly useful in organizations that possess large amounts of data but do not fully utilize them in decision-making processes. It enables translating data into specific insights and decisions that have a direct impact on business results.
An approach based on data processing and modeling that enables the identification of dependencies, patterns, and factors influencing the results of processes, research, and operational activities. Our solution allows you to organize structural data (such as sales, operational, financial, or logistics data) and identify the most important dependencies and determine which features or parameters affect the outcome. In practice, this means both identifying the causes of process efficiency decline and assessing whether a given case (e.g., product, order, or event) meets requirements or requires further observation and qualification as non-compliant. When a company wants to understand what influences process results, quality, costs, sales, or operational efficiency. When a team needs to analyze dependencies between different areas of operations, process stages, or the results of successive measurements, studies, and experiments. The organization makes decisions based on historical and current data, both in operational areas as well as quality or laboratory areas. Employees need to detect inefficiencies, deviations, and areas requiring optimization or further observation more quickly. The company wants to organize data and prepare it for further use in analysis, prediction, classification, or optimization. Detection of causes of performance declines, sales drops, or cost increases. Understanding what actually influences results and where action should be taken. Elimination of inefficiencies and better utilization of available data. Data preparation and process understanding as a basis for prediction, optimization, and automation. Prediction of oil condition in industrial samples using machine learning methods and development of an AI model decision explainability system. Industry / Maintenance / Machine Diagnostics Assessment of lubricating oil condition in engines is a complex and time-consuming process, requiring analysis of several dozen physicochemical parameters of the sample. The lack of a tool for automatic and consistent classification of laboratory test results leads to delays in operational decision-making and increased risk of machine failures. Development of an API microservice based on ML models that classifies oil condition into one of three classes: NORMAL, INDICATION, ATTENTION. The system processes data from laboratory samples, determines the trend of parameter changes over time, and generates SHAP visualizations explaining which factors determined the prediction result. A conversation is the first step in identifying organizational needs and assessing project feasibility
Tabular Data Analysis
When is it worth investing in tabular data analysis?
Business Benefits
Identification of Problem Sources
Better Data-Driven Decisions
Better Operational Decisions
Foundation for Further AI Solutions
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