Say hello to Graph 2.0

We take away many of the widely spread pains associated with temporal graph analytics. One contextual platform to rule them all!
  • Simple deployment
  • Simple data ingestion
  • Time relative ML and queries
  • Millisecond training and response

Load existing data

Ingest and load data from any source, Kafka, CSV, Parquet, SQL and many more. No need for expensive transformation pipelines or SME teams.

Time Travel

Move backwards and forwards in time, analysing your data without re-ingesting. Simply adjust the time range of your query!

Graph ML

Run native Graph Machine Learning (ML) on static and dynamic networks. Identify causal trends or risk across time. Raise alerts, and drive intelligent decisions.

Open Core

Our core platform is open source and available on GitHub. However, it has been completely reworked to be x10 faster and will be re-released soon for free.

Ingest your data from anywhere

Ingest millions of rows per second from your relational database, CSV files, cloud storage or big data warehouses. No need to move or transform your data, simply point to your datasource!
Cloud Storage
Run Queries / Analysis

A real-time Data Time Machine

The only platform that allows you to natively rewind your data in seconds, run analysis across time epochs and unearth causal relationships.
Temporal analysis allows you to easily focus on causality in data. Identifying what really matters.

Our platform has been applied to solutions across multiple sectors. Including
  • Crypto and financial fraud
  • Insurance and root-cause
  • Social network analysis
With Code Window

Designed to be simple

Our platform is designed to make ingestion and analysis simple, with no need to move or transform data. We make ingesting and analysing your data sets easy!

Simple Ingestion

Built with speed in mind, scaling to millions rows per second. Ingestion turns raw data directly into a temporal graph, complete with all properties.

  • Files (CSV, Parquet, etc.)
  • Cloud storage (AWS S3, Azure Blob)
  • Streaming/PubSub Platforms (Kafka, Pulsar)
  • Databases (Postgres, Redis, etc.)

Native analytics

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.

  • Standard algorithms with global scope
  • Pattern detection and temporal motifs
  • Taint/Contagion tracking
  • Real-time native scoring

Any questions?

We attempt to answer some of our frequently asked questions. If you like to learn more and discuss your use-case, get in touch now.

Is the software open source?

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.

How does the platform work?

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.

Where can i see some examples?

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.

What use-cases do you support?

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