Less friction, more flow
We aim to be a flexible platform that provides options for different skillsets and tools of choice.
Introducing QPython, a module with a set of libraries and helper functions that allow data scientists and analysts to leverage Python and their familiar tools while analyzing data from Quantexa and integrate the results of their analysis through Assess.
Effortless Data Exploration
We’re continuing to improve our Geospatial capability introduced in Quantexa 2.1.
Now an investigator can see multiple data points over a period of time and their directional path, allowing geospatial data to be viewed in the sequence it happened, enhancing investigations with valuable context.
Data Viewer provides an advanced data analysis experience for all types of data spanning through use cases in Financial services, Telco, Insurance and Government sectors.
We’re adding functionality for in-depth data analysis, the ability to slice and dice as you want, granular filtering options and more – and all that with no limitations to the number of data points used.
This expands Quantexa’s capabilities when analyzing a wide set of data types, e.g. customer engagement data for our Telco customers, claims for Insurance, etc.
Quantexa is elevating user management to the next level.
We’re introducing Quantexa User store that keeps information about users natively in the platform, which opens a set of exciting possibilities.
In this release we are making it easier to enable users to share their investigations and tasks within the platform, without the need of complex external integrations.
This is the first step in Quantexa’s user management functionality and further advancements are on the roadmap. Expect greater control over the data and features that users can access and custom views and layouts.
We’re speeding up the Scoring setup process even further.
As a part of the recently introduced Assess tool, Accelerate takes contextual analytics even further.
Assess Accelerate is a configuration-driven tool that enables data scientists and developers get started quickly with Scoring.
Data standardization is a complex task as the different data types that are used often require different parsing and cleansing methodologies.
For example, addresses, businesses and individuals often don’t follow fixed rules and naming conventions, which make data parsing more difficult, whilst account information parsing requires custom code.
The new version of Parsers continues to tackle these challenges. We’ve created a new account parser and applied machine learning models to our business and address parsers to identify these entities better.
See the next generation of our platform in action