Information Retrieval

Are your employees wasting time finding information stored on your resources? Among the pain points related to the increase in the amounts of data stored, this is one of the most recurrent. In the twentieth century, corporate paper records were counted in kilometres of thickness, now they are counted in terabytes and sometimes even petabytes. With one teraoctet (or 8 terabytes) being approximately equivalent to 40 linear kilometers of paper, one can easily how drastically the scale of stored documents volume has changed. These documents are all available from any computer connected to your servers and yet, the information is less and less accessible because it is drowned in a mass that is impossible to comprehend.

A magnet can find the needle in the haystack, but how do you find a drop of water in the ocean? Large Language Models’ (LLMs) can understand the meaning of an information in a document and store it in a semantic database. This allows the construction of a semantic search engine with much more advanced capabilities than a classic keyword search engine such as those of internal wikis (Confluence, Notion, etc.). A tool based on this technology can find any information lost in the depths of a document that you had forgotten existed based on the meaning of the question you are asking and provide you a summary in a written answer while citing its sources.

eleven can equip you with a semantic search engine on your organization’s own data and allow all your employees to eliminate search time by interacting with the best possible archivist, the one who remembers all your documents as if they had just read them.