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iXmatch: eXactly what you're looking for
iXfind returns the most relevant results in the industry.

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iXfind leapfrogs current search technology by seamlessly integrating several cutting-edge text mining and information-retrieval techniques to deliver the most relevant results in the industry.

iXfind enables our customers to provide their employees and customers with results that make sense by looking at everything as a multi-dimensional object, using our patented scoring model, and dynamically analyzing every set of results to determine what options will best aid navigation.

One way iXfind puts content into context is by dynamically clustering search results into groups of similar documents. People can better manage information overload and more easily navigate through search results by selecting relevant clusters, or content groups. For example, a search for “mercury” returns thousands of results that are clustered into sub-groups based on context, such as the planet, the element, the Greek god and others. By choosing the “planet” cluster, other results are removed from view and the user is left with results primarily related to the planet Mercury.

iXfind also makes contextually-relevant product and service predictions by dynamically identifying patterns in the data, not based on preconceived hypotheses or static business rules.

iXfind functionality:

Search Querying unstructured text and structured databases
Scoring Patented method of evaluating every object to determine its overall fit
Bidirectional matching Scoring every object not just based on how it fits the query, but also based on how well the querying object fits it. e.g. On a jobs site, a successful match only occurs when a candidate meets a job’s criteria and the job meets her criteria.
Fusion Merging and ranking search results from multiple sources
Query augmentation Dynamically refining a query based on user preferences and search results
Dynamic clustering Determining what makes groups of documents / objects similar
Auto-classification Automated categorization based on example documents
Self-learning Leveraging past behavior to sharpen focus
Dynamic differentiation Determining what makes documents / objects different
Predictive analytics Dynamic discovery of trends in large customer data sets