EES, is an advanced semantic search engine, supporting semantic search and intelligent integration and utilization of data information. We refer to this engine as an Investigative Search Engine. While current search engine answer a user query by presenting a ranked list of the documents found relevant, based on the occurrence of search terms in the single document, Investigative Search Engine will find real or possible links between domain objects(such as suspects) based on the information contained in multiple documents and databases. Moreover, it assists the user in recognizing patterns of interest in the information bases. 

EES is marketed as  three distinctive products in a layered architecture: (1) semantic enterprise search engine, (2) an investigative search engine, and (3) the investigator’s workbench. Each of these products actually utilizes the solution of the precedent, to which it add a new layer. Each layer addresses the primary needs of different user categories within an organization.

EnMentoSofts’ main strategy is to tailor EES for selected market segments. This is achieved through collaboration with key actors  within given segments, such as the Danish State Police is for the investigative search market segment. International sales to institutions and enterprises in (or close to) a segment will mainly be pursued through partners. We have indentified possible international partners, with whom a strategy for a specific  segment or geographical area will be worked out.    
EES is a semantic enterprise search solution which provides employees with a single point of access to data residing within the corporate network. The EnMentoSoft platform allows information access to any file, any fileserver, any application, and to any external/internal website.

The information is readily available through user-friendly interfaces, and features an advanced semantic search algorithm which ensures quick, relevant search & retrieval.
EES is unique thanks to the innovative use of semantics to resolve the difficulties that arise when we try to make computers understand human language. The use of semantics to solve this issue is relatively new, and applying fuzzy logic to match relevance from the search query to documents lying underneath is a totally new approach to the problem.

Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory, to deal with reasoning that is approximate rather than precise. Just as in fuzzy set theory, the set membership values can range (inclusively) between 0 and 1, in fuzzy logic the degree of truth of a statement can range between 0 and 1 and is not constrained to the two truth values {true, false} as in classic predicate logic. And when linguistic variables are used, these degrees may be managed by specific functions, as discussed below.So with fuzzy logic and our TermNet, we can determine relevance between the search query and the objects searched, in a highly efficient manner that isn't bound to "yes" or "no", but also includes: somewhat, perhaps, most likely and other imprecise labels.

Founded out of pioneering research at Esbjerg University, the EnMentoSoft vision has been to revolutionize the way people interact with information by providing software able to automate operations on not just structured and semi-structured but also unstructured information.