Connect Ziflow and Neo4j to Build Intelligent Automations
Choose a Trigger
When this happens...
Proof Created
Reviewer Comment
Reviewer Decision
Choose an Action
Automatically do this!
- Request a new Action for Neo4j
Enable Integrations or automations with these events of Ziflow and Neo4j
Enable Integrations or automations with these events of Ziflow and Neo4j
Triggers
Proof Created
Triggers when a proof is created.
Reviewer Comment
Triggers when a reviewer makes a comment on a proof.
Reviewer Decision
Triggers when a reviewer makes a decision on a proof.
Actions
Create a Folder
Creates a new folder in Ziflow to organize proofs and files.
List Root Folders
Get a list of top level folders the user can access.
List Users
Get a list of users registered in your account.
List Contacts
Get a list of all contacts.
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Frequently Asked Questions
To start, connect both your Ziflow and Neo4j accounts to viaSocket. Once connected, you can set up a workflow where an event in Ziflow triggers actions in Neo4j (or vice versa).
Absolutely. You can customize how Ziflow data is recorded in Neo4j. This includes choosing which data fields go into which fields of Neo4j, setting up custom formats, and filtering out unwanted information.
The data sync between Ziflow and Neo4j typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Ziflow and Neo4j. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About Ziflow
Ziflow is an online proofing tool designed for marketing teams and agencies to review and approve creative content. It supports various file types and provides features for collaboration and feedback.
Learn MoreAbout Neo4j
Neo4j is a leading graph database platform that enables organizations to unlock the value of connections, influences, and relationships in data. It is designed to handle highly connected data and is used for a variety of applications such as fraud detection, real-time recommendations, and network management.
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