Technology we build.

At SynerScope, we built our two core product from scratch. We designed and tested the required workflows for unstructured data to make our software fit for even the largest of enterprises. All that unstructured data knowledge has shaped the way we build our software.

Iximeer

Iximeer is built on a stack closer to the hardware. It uses a C++ core library for maximum performance, and interfaces directly with NVidia GPUs to provide the lowest latency possible. By introducing our patented text analytics. The view configurator in Iximeer provides a very rich set of visualization templates. So for each data type there is just the right representation. By having all of the data at the fingertips of our users and immersing the user into the data, we let the data tell the story. If we break that virtual experience, because the user has to switch between views or components, he or she will lose track quickly. Thus we had to make certain to always link all the data in view, and design our user interfaces around that core concept.

Ixiwa

Our Ixiwa product tightly integrates with core Hadoop components, so it leverages the best that data lake technology has to offer. To get those functions in front of our users (and in their hearts), we worked hard to provide a seamless UI experience.  The data sources are just a mouse click away. This requires a lot of bookkeeping and automated decision making under the hood. We therefore developed our processing stack in Python and Scala (on the spark side), and JavaScript/TypeScript on the middleware and front-end side.

Data Correlator

Certain solutions we needed were simply not available. For example, resolving similar data objects across an entire data lake was not possible. Thus we invented and patented an algorithm that brought down the time complexity. This allowed us to present our users with data linkage suggestions even on the largest of data lakes. The Data Correlator was born.

Technology we use.

Technology is at the core of our company, but we could have never gotten this far with our technical details without standing on the shoulders of some giants. In our case, we leverage well known and industry-tested components from the open source software landscape. Components like Apache Spark helped us scale out our CPU and IO intensive compute across multi-node systems, so that we no longer have to worry about scaling limitations. We picked these technologies because they are fit for purpose and have the right maturity level. Each component has to pass the gates of security design and tests before it is adopted in our stack.

For our in-house developed image and text processing logic, we make intensive use of Deep Learning. We support that through the Google TensorFlow library system. The great critical mass that this library is getting in the industry, helps us quickly adopt new and emerging technologies, as the library itself expands its functionality in an ultra-rapid pace.

Internal API’s

We take meticulous care to keep our platform modular and manageable. Internal API’s make sure that components can be replaced when necessary, and this eases our product life-cycle management.

Our web front-end does not exist without various crucial Node.JS and JavaScript open source libraries. Whether it is for formatting strings, building nice looking user interfaces or for making sure our interfaces are as responsive as our backend systems, these libraries form the building blocks that construct core features of our platform.