Capventis Communications & Data Integration

The Capventis data integration framework (provisionally named Glu) provides a consistent environment for data movement, transformation, and integration. It provides a massively scalable core that, when combined with specialist micro-data services, creates an extremely efficient data management system, capable of integrating services regardless of underlying protocols, geographic locations, or potential infrastructure restrictions.

In its simplest form, the framework provides connectivity between two or more systems to simplify integration. Each component communicates with all other framework components to provide extremely flexible data management and/or conversion service for both real-time and batch environments.

Companies often require connections between different in-house and third-party products, each providing application programming interfaces specific to these products. The data integration framework performs in-line conversions between data formats to allow otherwise disparate systems to access information consistently. This approach also caters for incremental integration, connecting new services to legacy systems as the business needs change or grow. In this environment, each component simply plugs in to the common underlying framework, automatically discovering other components and sharing the load as required. This allows companies to introduce one or two components and add more as the business matures.


For data visualisation services, the framework creates a simple end-point that combines the inputs from multiple data sources, or complex processing services.

Within a central core, the framework supports scheduler, health monitor, forensic audit service, an internal message queue, data mapping services and data abstraction service to remove reliance on fixed data structures. Components then connect to this framework via the message queue to perform specific tasks.


In a typical implementation, each component runs within its own container. By using containers, the framework can scale rapidly or integrate different services running within different security contexts without compromising the overall security model. As an example, the integration framework can communicate with cloud or social media services without exposing any part of the internal systems to the wider Internet.



Typical implementations include

  • Social media links to extant systems
  • Data cleansing or synchronisation services
  • Linking legacy systems to real-time sensors or Internet of Things components
  • Integration between customer services platforms and other corporate systems
  • Linking telephony suites to CRM/CX services
  • Extracting real-time event data and combining with geospatial data from multiple sources
  • Combining information and presenting it to data visualisation tools
  • Real-time or batch synchronisation between service or system, and so on



The data integration framework supports multiple operating systems, mixed environments and all common cloud services. The framework can be deployed on single or multiple hosts, including hybrid (cloud/in-house) environments. An API allows for easy integration with other systems. For secure systems, the framework can also enforce privacy policy rules or in-line data transformation (automatically obfuscating data to allow visualisation without exposing raw or source data).

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