Quicklogix mines the organizational expertise through machine learning. Now all stakeholders (Sales, Project Management & Engineering) have full view of all data to make decisions, with ‘Google-like’ ease.
Quicklogix allows the search to be Objective or Context oriented. The objectives of analysis can be varied based on user role or the scenario.
A designer may want to locate the most relevant design or a drawing, while project managers may be interested in associating their search to project risk.
Quicklogix allows the Organizations to define their Objectives. The underlying platform automatically learns to relate the data to the objective and rank the most relevant results to the user. Unlike a regular search in a File system or Google, a contextual search brings the solution to an analytics problem rapidly.
One of the main features of Quicklogix is to enable faster discovery of information from various sources that are relevant to the user context. The Context is defined as a way to tell the machine on "What to focus?" and "How to Rank?". With underlying data, the machine learns various possible ways that one can search and generates an optimum or relevant outcome that is useful in making certain business decisions.
Information in Silo is not a fair way to make a decision. Quicklogix learns similarities between the entities inside the data, so be it a project, a drawing or even a specific material that is used inside a design. The similarities allow the users to compare the Entities side-by-side with a deeper view of network of data that made them look similar or dissimilar. Comparisons allow users to choose the best solutions, among many.
A machine learning that learns from the natural distribution of information with the context and generates a probability distribution of influence for each data point. Subsequently, when the user searches, the system performs the estimate of influence of search term for each instance of the context and performs dynamic ranking.
Similarity model is built using several algorithms considering both structure and the semantics. The structure of data can be sequential, columnar, or even a graph. The semantic similarity is used when processing textual data where same content can be mentioned in various ways but represent similar knowledge of information across data sources.
This unique similarity measure offers users to perform deeper analysis on why certain entities like projects, sheets or quotes are similar or dissimilar. When comparing entities, the system automatically offers the corresponding data points in 'comparison detail' screens
If you imagine the data to have a tree structure with content (that are coded in color) like the following pairs, Quicklogix platform will evaluate the similarity between the two structure and the content inside them and generate a similarity. The following is just a visual representation of comparisons on projects and sheets – represented in tree structure
Fuzzy search is driven by content and often used to recognize data in real world situations. The system automatically generates various forms of same information during the learning process. Some of them are