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Data & AI Client: Metinvest

Knowledge base on Microsoft SharePoint with enhanced search and machine learning capabilities for enterprise

ENTERPRISE SEARCH DOCUMENT INTELLIGENCE SHAREPOINT AUTOMATION

Client

Metinvest is a vertically integrated group of steel and mining companies that manages every link of the value chain, from mining and processing iron ore and coal to making and selling semi-finished and finished steel products. Metinvest Digital is a part of Metinvest.

Challenge

Metinvest faced significant challenges in managing and searching through terabytes of data stored on Microsoft SharePoint. Like many large enterprises, they struggled with inefficient document search and retrieval, compounded by the complexity of access management, security, and data governance. Redundancy and duplication of documents made it difficult to maintain consistent document quality and format, while manual categorization and sorting were time-consuming and resource-intensive.

Solution

A multilingual search with enhanced machine learning capabilities allows for decreased document management time and improved document discovery and search relevance.

Features

Full-text, attribute-based, and category-based multilingual search. Our solution offers a robust multilingual search system that supports full-text, attribute-based, and category-based searches. This capability allows users to perform comprehensive searches across multiple languages, ensuring that they can find relevant documents quickly and efficiently, regardless of the language in which they are written.

Linked documents identification. The system can identify and link related documents, providing users with a more connected and contextual understanding of their data. By recognizing relationships between documents, it helps users navigate through relevant materials seamlessly, enhancing the overall document management experience.

Document duplicates discovery. To optimize storage and streamline data management, our solution includes a feature that discovers and flags duplicate documents. This reduces redundancy, saves storage space, and ensures that users are always working with the most current and accurate versions of their documents.

Meaningful information extraction from documents. Leveraging advanced machine learning algorithms, the system can extract key data insights from documents. This feature allows users to quickly identify and utilize important information without manually sifting through extensive content, thereby increasing productivity and decision-making efficiency.

Automated document categorization. Our solution automates the categorization of documents based on their content, significantly reducing the time and effort required for manual sorting. This automated process ensures that documents are organized logically and consistently, facilitating easier retrieval and management.

Data access management based on Azure Entra ID (formerly Azure Active Directory) organization’s permission model. Security and access management are very important. The solution integrates with Azure Entra ID to provide robust data access management, ensuring that document access is controlled and compliant with the organization’s permission model. This ensures that sensitive information is protected and accessible only to authorized personnel.

Results

The automated processes and advanced search capabilities decreased the time required to manage and retrieve documents. The multilingual search and document linking features improved the relevance and accuracy of document searches, duplicate detection reduced redundancy and saved storage space, and automated categorization streamlined workflows and decision-making.

Technologies

  • Azure
  • Blazor
  • ASP.NET
  • Entity Framework
  • SharePoint Online SDK
  • Microsoft Graph SDK
  • Azure Cognitive Services
  • Azure Machine Learning
  • Milvus
  • Azure Machine Learning Studio

Integrations

  • Microsoft Entra
  • SharePoint Online

Metinvest, an international steel and mining group, spent significant time managing and searching through terabytes of data on Microsoft SharePoint. In response, we integrated an advanced multilingual search system enhanced with machine learning. This solution identifies linked documents, detects duplicates, extracts key data insights, and automates document categorization. Read the full story.