For existing Blaze customers who might be evaluating Xpress, what are some of the advantages of integrating the two solutions? Please give some examples as they relate t the Insurance Underwriting industry.

These two products certainly work very well together.

Blaze can be used as a convenient tool to define decision goals and preprocessing logic such as customer segmentation rules or predictive models that calculate risk scores. So it is used both as a user interface to maintain this logic and optimizaton parameters and as a deployment environment for rules and predictive models.

Xpress can then take all raw and Blaze-generated decision variables and optimize the final decision against the entire set of variables at the micro- and macro-level - for example, produce individual price quotes that maximize revenue while taking into account some constraints set at the entire customer portfolio level.

This kind of "here is a billion of possible decision combinations, choose one that works best for me" problem is something that is impossible to implement in a rule-based solution alone because of its immense computational complexity. However, Xpress uses some very advanced optimization algorithms that allow to process all possible combinations and find the best solution in a reasonable timeframe - in many cases in real time. Add analytics on top of that and you get a full set of tools your application needs to produce the very best decision every time.

These two products certainly work very well together.

Blaze can be used as a convenient tool to define decision goals and preprocessing logic such as customer segmentation rules or predictive models that calculate risk scores. So it is used both as a user interface to maintain this logic and optimizaton parameters and as a deployment environment for rules and predictive models.

Xpress can then take all raw and Blaze-generated decision variables and optimize the final decision against the entire set of variables at the micro- and macro-level - for example, produce individual price quotes that maximize revenue while taking into account some constraints set at the entire customer portfolio level.

This kind of "here is a billion of possible decision combinations, choose one that works best for me" problem is something that is impossible to implement in a rule-based solution alone because of its immense computational complexity. However, Xpress uses some very advanced optimization algorithms that allow to process all possible combinations and find the best solution in a reasonable timeframe - in many cases in real time. Add analytics on top of that and you get a full set of tools your application needs to produce the very best decision every time.