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 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.
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 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:
- 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.
- 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.
- 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.