Leveraging AI for Advanced Document Retrieval

Transforming a seemingly complex task into simplicity: 100,000 documents to be easily accessible and searchable. With our intelligent search platform, equipped with automatic tagging, we provided the perfect solution.
Fabio Musso
Data Science Leader

The Company

Our client, a leading Italian infrastructure company, found itself in possession of an extensive collection of documents. This collection comprised over 100,000 files in various formats, including PDFs, scans, text documents, and email messages, stored in the client’s cloud.

The Need

The client recognized the need to implement a solution that would enable efficient access and retrieval of the vast document repository. Traditional search methods proved inadequate in efficiently navigating a vast volume of files and extracting pertinent information due to the absence of structured organization. In this context, documents were stored without designated paths or descriptive names, resulting in a laborious and time-consuming search process.

The Solution

We have addressed this challenge by developing an intelligent search platform that operates seamlessly within the customer’s cloud environment.

This platform offers a range of powerful capabilities:

  • It performs automatic and contextualized tagging of documents, regardless of whether they are in digital or scanned format.
  • It enables searches using natural language queries or predefined categories such as document type, date, or author, utilizing the potential of Conversational AI technologies.
  • Through semantic analysis, it suggests related documents based on the content and meaning of the documents.
  • Furthermore, the platform can transcribe and summarize the text within documents, and if needed, even provide translations.

By leveraging these advanced features, our intelligent search platform transforms the client’s document management process, streamlining retrieval and enhancing overall productivity.

The Results

The implementation of Hyntelo’s intelligent semantic system yielded significant results for our client, saving valuable time and resources.

The key outcomes and achievements were as follows:

  1. Searchable Documents: the system made 133,000 documents searchable, allowing everyone to easily access and retrieve relevant information from the vast document repository. This resulted in a significant reduction in search time and improved overall efficiency.
  2. Document Categories: with the introduction of the document tagging mechanism, the system automatically classified the documents into more than 20 distinct categories. This categorization enabled users to filter and narrow down their search results based on specific document types, further enhancing the search experience.
  3. Context-Aware Tags: the semantic analysis capabilities of the system led to the creation of over 40 context-aware tags. These tags captured the nuanced content of the documents, allowing users to refine their searches based on specific themes, topics, or keywords. This feature significantly improved the accuracy and relevance of search results.

I am Fabio Musso, Leader of the Data Science division at Hyntelo.

Recently, we developed a solution for a client in the infrastructure sector who had a specific and seemingly simple need: to make 100,000 documents of various formats available and searchable within their cloud environment. At that time, the documents were stored in a completely unstructured manner, with non-descriptive paths and filenames, making the document retrieval process highly time-consuming.

To address this problem, we developed an intelligent search platform capable of performing automatic and contextualized tagging within the client’s cloud environment.
The platform is capable of:

  • Performing fully automatic tagging based on the context of the documents, whether they are digital documents or scanned documents
  • Conducting searches in natural language or through predefined categories, such as document type, date, or author, using Conversational AI technologies
  • Suggesting related documents based on the semantic information of the documents
  • Transcribing and summarizing the text of the documents and, where necessary, even translating it.

So far, the platform has successfully tagged 133,000 documents across 20 different content types, utilizing 40 contextualized tags. This has made the search process more accurate and fast, saving the client time and resources.

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