The business was facing a significant challenge in managing a massive database of over 16 million documents, many of which were lengthy and of varying quality. These documents included bids, attachments related to bids, and vendor-related documents. Replicating a bid or extracting useful information from these documents was time-consuming and inefficient due to their inconsistent quality and the large volume of data.

The primary challenge was filtering high-quality documents that could be useful for future references, as well as storing these filtered documents in a way that allowed for efficient retrieval and use.

Our goal was to leverage AI for automating their document management process. Our approach involved several key steps:

• Implementation of automated filtering mechanisms to identify and retain high-quality documents. This process involved sophisticated algorithms to evaluate the relevance and quality of each document, which was essential due to the vast amount of data.
• Storage of the filtered documents in a database, which required indexing each document. This involved calculating a vector for each text fragment (chunk) to capture the meaning of the paragraph. These vectors called embeddings allowed us to easily represent the semantic content of the documents.
• We summarized each document to create a more manageable and searchable database. These summaries were also converted into embeddings, enabling efficient searches based on the meaning rather than just text matching.

Our solution enabled users to perform queries on the database using embeddings. This allowed for precise and relevant search results, significantly improving the efficiency of document retrieval. Using our AI tool, DocsHunter, users could draft parts of a document, search for related information within the database, and use Large Language Models like ChatGPT to generate new, specific text. This ensured the new documents were accurate and based on verified information.

By integrating AI into their document management process, Authorium achieved several significant improvements regarding efficiency for document generation. The automated filtering and indexing processes significantly reduced the time required to manage and retrieve documents; risk of errors in document creation was eliminated; and reducing the manual labor involved in document management meant an important cost-saving.

DocsHunter as an AI integration into their business enables users to quickly draft and finalize documents with confidence in the reliability and up-to-date nature of the information. They successfully transformed its document management process enabling this company to scale its processes limitless.