Sainapse approaches this classic machine learning problem in a way that’s very different from popular traditional methods. At the core of Sainapse’ beliefs are:

  • Enterprise data (however unstructured) is generated by a rather structured process that’s defined and managed, with few records potentially standing out as anomalous exceptions to rule.
  • Data generating enterprise processes have been in operation for some time even though they might not have been digitally captured.
  • Dramatically higher accuracy even with high number of dimensions/classes is possible to achieve, if one tries to identify the ‘class’ based on cohort and not just an unitary record.

This fundamentally different approach that’s proprietary and purpose built for enterprise Customer Support use cases, returns >70% accuracy on Day 0 and higher than 90% accuracy in classification within a month or two even in an omni-lingual context.

Sainapse’s ability to read from case attachments and adding the essence from those attachments to title and description of reported case adds to sharpness of Sainapse inferencing.

Sainapse is truly agnostic to number of dimensions or classifiers in a case as long there is minimum data present in all of them to learn from. This makes Sainapse go beyond usual customer support queries and even capable of handling large volumes of machine generated alerts or system messages should that be the need of the hour.

Sainapse classifier much like all other Sainapse capability is language agnostic and would not be impacted should one class among 10 be in a language completely different from the other 9.