Text Intelligence involves retrieving information from unstructured data to uncover trends and patterns and derive insights from the output data. For enterprises, it has been a key enabler for tracking the digital footprints of customers.
Recently, there has been a shift to a global analysis of textual data for more varied use cases. Nevertheless, garnering intelligence from unstructured data in the form of text – in emails, social media conversations, chats, annual reports, press releases, scientific publications, blogs, mobile transactions, customer transcripts, title deeds, and more – is easier said than done.
Analyzing textual data with manual processes is challenging because it is time-consuming, laborious, and has limited scalability. Besides, it is demanding to build automated tools to analyze textual data from scanned PDFs/images, as it requires a blend of artificial intelligence (AI), machine learning (ML), and natural language processing (NLP).
In such a scenario, Straive’s text intelligence solution can help as it enables enterprises to:
Enabled by the Straive Data Platform (SDP), our text intelligence solution helps customers by extracting, enriching, and delivering data and actionable insights from text-heavy documents in any format (PDFs, emails, Word files, scanned images/documents, and more). Our solution helps enterprises in their knowledge discovery processes by ingesting source documents and leveraging cognitive technologies, inputs of Subject Matter Experts (SMEs). The result is highly accurate structured datasets.
Download our text intelligence case study to learn more about how we helped a leading data and analytics solutions provider to the risk, insurance, and financial services industries accelerate data extraction from complex documents.
In this case study, we offer insights on how our Straive Data Platform helped the client automate the process of extracting critical data elements from the scanned police auto accident reports and maintaining the accuracy of the extracted elements.