Connect V7 Go and Textgrid to Build Intelligent Automations
Enable Integrations or automations with these events of V7 Go and Textgrid
Enable Integrations or automations with these events of V7 Go and Textgrid
Actions
Create Project
Creates a new project.
Create a New Session
Create a new session
Delete Project
Delete a project.
Update Project
Update a project .
Create an export
Creates a new export
Delete an Export
Deletes an export
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- multi keyword matchcmntdqJzLMYSReplytoCommentMultiKeywordAdd Step
Automate Social Media Post Creation and Scheduling Across Multiple Platforms
- InstagramInstagram CaptionGenerate ImagePublish Photo with TaggingAdd Step
- YoutubeYoutube Video ScriptYoutube Description VideoCreate an Avatar VideoDelayRetrieve Shareable Link for a Video1Upload VideoAdd Step
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- LinkedinLinkedin ArticleCreate a linkedin Article or URL postAdd Step
Automate Instagram DM Replies, Reactions, and Team Alerts
- has responseSendDMMessageAdd Step
- Notify teamSendMailAdd Step
- has reactSendDMMessage ReactAdd Step
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Frequently Asked Questions
To start, connect both your V7 Go and Textgrid accounts to viaSocket. Once connected, you can set up a workflow where an event in V7 Go triggers actions in Textgrid (or vice versa).
Absolutely. You can customize how V7 Go data is recorded in Textgrid. This includes choosing which data fields go into which fields of Textgrid, setting up custom formats, and filtering out unwanted information.
The data sync between V7 Go and Textgrid 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 V7 Go and Textgrid. 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 V7 Go
V7 Go is a cutting-edge platform designed for AI-driven image and video annotation, streamlining the process of training machine learning models. It offers advanced tools for data labeling, management, and collaboration, making it an essential resource for developers and data scientists working on computer vision projects.
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