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Process Analytics

Rules driven processes

With process analytics as a service, business processes can be simplified and straight through processing achieved by automation of predictive analytics user stories. The data analysis of each process gets hacked into hours from traditional time frame of months if not years.

Techniques starting from frugal flow analysis to advance prescriptive analytics including association rule learning, cluster analysis, classification, and regression could be employed to present a singular view. Features and predictive models created over this can be automatically redesigned to reflect changing data, ensuring long-term relevance and continuous value.

Establish a higher ROI led feature engineering process without higher external costs and anyone can do it framework.

The intelligent process framework has the following attributes:
Modular, Extensible and Stateless

Modular and extensible components for faster modelling and stateless process building blocks.


Flexibility and Multi tenancy model to onboard new channels and partners with minimal effort. Clear separation of tenant specific data across all layers, modules and configurations.

Dynamic Routing and Workflows

Allow to update, add transformations without affecting production level system performance. Dynamic routing for transformation, 3rd party and multiple backend systems to fulfill incoming requests. To build set of re-usable services for standard message transformations independent of the schemas/data.

Orchestration and Integration

Orchestrate all the services exposed by a provider to complete a business process. Aggregate multiple service responses from providers if required to respond with a single standard response. Integrate with service providers using a wide range of standard protocols.


Highly scalable SOA/SCA architecture allows for infinite scalability where the applications can scale up to meet higher demand by XLC. Able to support migration to the cloud.

How would we do it?
Step 1: Outline the process definition
Step 2: Establish the nodes of data flow and data trespass
Step 3: Measure data tenacity at each node
Step 4: Configure our dynamic decision tree framework for:
  • Each scenario
  • Each scenarios with rule sets
  • Each rule sets with rules
  • Each rule with decision and association with a dynamic workflow
Step 5: Pilot process as a service scenario and run through dynamic work flows
Step 6: Model the outcome analytics and visualize in a as a service tool like PowerBI
Use cases of interest:
Use cases of interest


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