Paul Regan 
What is Publish?
DADI Publish is developed to optimize editorial workflow with flexible interfaces that adapt to your requirements, meaning more time can be spent making content than wasting time managing it. It is built to support the principles of COPE (Create Once Publish Everywhere).
Journalists and editors can seamlessly manage output across multiple products from one place, while engineers can make light work of even complex content collections. Publish currently works with DADI API but our development roadmap will soon allow use with a variety of data stores.
DADI uses Publish to power this site.
Full features
Centralized media management
Power a suite of digital products from a single content store. Saves time and effort for engineers and editors alike.
Article publish scheduling
Pre-load content for automatic publishing at a time that suits the content, not your working day. Also create drafts for internal review.
Setup and connect in 30 seconds
The perfect partner for DADI API so you are up and running in days and weeks not weeks and months. Soon to work with other data stores.
Custom user permissions
Enhanced features for large teams – keeping the right hands on the right parts of the system. Extends to support editorial workflow.
Collaborative document editing
See where colleagues are in shared articles and avoid treading on toes – work together seamlessly to update content.
Revision history with quick revert
Provides a complete audit trail allowing you to restore to any point in time. Document differentials provide an overview of changes.
Latest Publish articles
Paul Regan
James Lambie 
Eduardo Bouças 
Paul Regan
Publish: Step-by-step first install
Publish is the content management component of the DADI web services stack, a set of flexible interfaces to easily manage content stored in API.
Arthur Mingard 
Apps
Q1 2019
store
A cloud storage solution for all types of data, with built-in security, privacy and redundancy.
Q3 2019
identity
CRM layer that works with anonymous and known records to make user data directly actionable.
Q3 2019
track
Real-time, streaming data layer providing accurate metrics at individual and product level.
Q4 2019
visualize
Data visualization for Identity and Track, but capable of taking data feeds from any source.
Q4 2019
predict
A machine learning layer that predicts user behaviour based on past interactions.
Q4 2019
match
Taxonomic framework for automated content classification through machine learning.
