Pryon has officially launched its new platform, the Pryon Retrieval Engine, designed to securely extract information from a wide range of complex content types. This includes text, images, videos, and more. The Pryon Retrieval Engine is fully API-enabled for custom deployments or can be accessed through Pryon’s web or mobile interface, providing a comprehensive end-to-end solution.
Understanding the challenges associated with retrieval, particularly in terms of accuracy, scalability, security, and efficiency, is crucial. Historically, implementing effective retrieval has been difficult, especially with complex document content that includes tables, images, graphics, and schematics. The rise of AI has further complicated matters, introducing risks such as unauthorized document access, data leaks, and potential cyber-attacks. So, how does Pryon’s new platform address these issues?
The solution lies in its high-precision ingestion capabilities, designed to enhance accuracy from the outset. Pryon’s technology emulates human-like document analysis and utilizes proprietary OCR to extract text from images, graphics, schematics, and handwritten notes. It also employs vision segmentation to identify and label key components, performs content normalization and filtering to remove irrelevant objects, and uses visual semantic segmentation to create more intelligent document chunks.
Chris Mahl, President & COO of Pryon, stated, “Every enterprise is striving to implement Generative AI but faces a significant hurdle: the challenge of efficiently accessing and securely utilizing their vast amounts of knowledge and data to advance their AI initiatives. This results in lost productivity, reduced customer experiences, and increased business and reputational risks—risks that large organizations can’t afford. Pryon’s new offering directly addresses these issues by providing rapid, accurate, and secure retrieval at scale.”
The solution’s ability to understand queries more effectively is another key feature. Pryon uses a proprietary combination of query expansions, out-of-domain detection, and query embedding to interpret natural language queries for matching ingested content. It then applies three proprietary models to quickly find and rank the most relevant content.
