No, you cannot manually add pre-existing data to a Browse AI Table as you would in a spreadsheet application like Google Sheets or Airtable. Tables are designed specifically to display data that has been extracted by your robots.
Understanding Tables vs. spreadsheets
Browse AI Tables serve a different purpose than traditional spreadsheets:
Tables: Display and organize data captured by robots, maintaining a historical record of extractions.
Spreadsheets: Allow for manual data entry, calculations, and more flexible data manipulation.
This means you cannot manually type in or paste data that wasn't extracted by the robot itself.
The "Add Row" feature explained
While you can't add pre-existing data, Browse AI does provide an "Add Row" feature that allows you to:
Add new parameters (like URLs) for your robot to scrape.
Queue up new extraction tasks.
Run your robot on these new targets.
This isn't the same as manually adding data - instead, it's a way to initiate new data collection by your robot.
How to use Add Row to extract new data
If you need to add new data to your Table:
Click the Add Row button above your Table.
Enter the URL and any other parameters your robot needs.
Click Start extracting data to queue this for extraction.
The robot will run on these parameters and add the extracted data to your Table.
Alternatives for combining manual and robot data
If you need to work with both robot-extracted data and manually entered information:
Export and combine: export your Table data to CSV, then combine it with manual data in a spreadsheet application.
Use integrations: Set up integrations to automatically send robot data to platforms that support manual data entry.
Create a custom database: For more complex needs, export to a database where you can combine multiple data sources.
Why Tables don't support manual data entry
Browse AI Tables are designed to be a reliable historical record of what your robots have extracted. This design ensures:
Data integrity (all data comes from verified extractions).
Clear tracking of data sources and timestamps.
Consistency in data structure.
Reliability for analysis and monitoring.