Software development company Accusoft has added a new feature - Document Q&A - to its secure document reader, PrizmDoc, to streamline Enterprise Content Management (ECM) management.
"The introduction of the Document Q&A feature underscores Accusoft's dedication to enhancing document processing efficiency for our clients' ECM solutions. This innovative feature provides our ISV customers with a competitive advantage, reducing development time and speeding up their market readiness", said Steve Wilson, Chief Product Officer, Accusoft.
Using IBM watsonx.ai, the Document Q&A feature employs an AI assistant to enable users to ask questions and quickly locate relevant information within a document. This move reduces the need to manually filter through long, complex papers by providing precise and context-aware responses nearly quickly.
PrizmDoc's Document Q&A function enhances the user experience while also saving time and money by analyzing large documents and providing relevant facts based on chat-based queries.
What are the key benefits?
Document Q&A transforms ECM document management by quickly obtaining information, dramatically reducing the time necessary to examine and analyze complicated documents. In addition, it reduces expenses by giving extensive, correct information with a simple chat query, allowing staff to focus on higher-value duties.
The solution also allows for simple real-time data analysis, reducing human error and boosting the quality of business choices with more informed insights.
PrizmDoc's Q&A capability increases efficiency, accuracy, compliance, and document management at the same time, providing ECM ISVs with a competitive all-in-one solution.
Additional features
Moreover, Accusoft has added Auto Summarization, Auto Tagging, and Classification modules to PrizmDoc, in addition to Document Q&A. These new features, developed in collaboration with IBM Watsonx.ai and IBM Granite, leverage generative AI and machine learning technologies to write concise summaries of large documents while automating tedious tagging and classification processes.
