Say hello to Graph 2.0
- Simple deployment
- Simple data ingestion
- Time relative ML and queries
- Millisecond training and response
Built with speed in mind, scaling to millions rows per second. Ingestion turns raw data directly into a temporal graph, complete with all properties.
Analyse your graph data across time, even while newer data is being ingested. Use any of our 25+ built-in graph ML algorithms or write your own with ease.
Our open source version of the core platform is available at GitHub.
Recently we have completely reworked the platform and we have seen x10 speed improvement on ingestion speed, x20 improvement on memory footprint and added off-heap storage going up to 1Bn transactions per 64gb machine. Our pledge is to release this version back to the OS as soon as it is well-documented and stable.
If you would like to discuss your use case and understand how we can help you then reach out.
We have developed the platform from the ground up with graph analysis in mind. It is fault tolerant, scalable and extensible.
You can find detailed explanation on the official documentation page.
You can refer to the official GitHub page for both out-of-box supported graph algorithms as well as our Lord of The Rings example analysing how Gandalf interacts with characters on his journey over time!
For a more in-depth use case such as AML, Fraud or Insurance, don’t hesitate to reach out.
We are designing the platform to be agnostic and facilitate many use cases. We currently work with AML, crypto and insurance providers.
We are looking to work with you to design and build use case specific algorithms to get the most out of your data.
Do you have a use case you would like to discuss? Contact Us