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Automatic entity extraction from unstructured documents data

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Service description

The service, through artificial intelligent models based on hybrid approaches (leveraging machine learning, deep learning, LLMs, regex or heuristics), is capable of extracting structured entities from unstructured data such as documents and log files in order to enrich Databases, structure company knowledge and automate knowledge-intensive processes. This service can run on batch documents for massive ETL operations or be deployed in any integrated process to automate data extractions activities.
Expected results: ‘- enriched database, structured company knowledge and automated knowledge-intensive processes.
Methodology: ‘- Needs and Requirements Analysis; –  Analysis, of Concept; – Data Preparation Pipelines, – On site Test before Invest
Target: ‘- Industrial plant and asset provider, machine learning platforms providers\n – Software solution provider for quality process optimization
  • Type: Test
  • SUT/E: Datasets
  • Average duration: 6.0 months
  • Contact: contact@ai-matters.eu

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Co-Funded by the European Union Under grant agreement number 101100707

Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Directorate-General for Communications Networks, Content and Technology. Neither the European Union nor the granting authority can be held responsible for them.

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