Connect Slite and Nanonets to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Slite
Choose an Action
Automatically do this!
- Request a new Action for Nanonets
Enable Integrations or automations with these events of Slite and Nanonets
Enable Integrations or automations with these events of Slite and Nanonets
Actions
Search Docs
Performs a search using keywords in the docs you have access to.
Fetch Doc
Returns a readable Doc by ID.
Create Doc
Creates a doc inside a parent doc or private channel.
Fetch Sub Docs
Returns a paginated list of sub docs.
Replace Doc
Replaces the entire content and/or title of a doc.
Update Doc
Updates a specific part of a doc.
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- Send for Approval acceptCreate Entry of expensesAdd Step
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Frequently Asked Questions
To start, connect both your Slite and Nanonets accounts to viaSocket. Once connected, you can set up a workflow where an event in Slite triggers actions in Nanonets (or vice versa).
Absolutely. You can customize how Slite data is recorded in Nanonets. This includes choosing which data fields go into which fields of Nanonets, setting up custom formats, and filtering out unwanted information.
The data sync between Slite and Nanonets 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 Slite and Nanonets. 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 Slite
Slite is a team collaboration app for creating, organizing, and sharing notes and documents. It allows real-time collaboration, rich text editing, and easy organization, helping teams stay aligned and efficient.
Learn MoreAbout Nanonets
Nanonets is a cutting-edge AI platform that specializes in automating data extraction and processing using machine learning models. It allows businesses to streamline their workflows by converting unstructured data into structured formats, enhancing efficiency and accuracy in data handling.
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