Case Study

Semantic document search thanks to Auto-Tagging

The company

Our customer is an Italian infrastructure and construction services company.

The need

Easily access and query more than 133k documents, based on their content and topic. Unfortunately, the metadata inside each document were not enough to perform advanced queries from the beginning.

The solution

We developed an intelligent platform, using Lyriko engine, that would allow our customer to identify, research and extract documents from its database. The challenge was to create a semantic search process instead of a simple text based search.

First the platform performed a fully automatic tagging of context-aware documents within its cloud environment. This made it possible to search for documents on the basis of their semantic content.

Then, Natural Language technology was applied to smooth the research process, with Conversational AI that identified important fields for a more precise and detailed search.

The results

  • 133k searchable documents
  • Over 20 document categories
  • More than 40 context-aware tags

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