You can send Browse AI website data directly to your own Amazon S3 bucket by connecting the bucket to a robot, then exporting its Table as CSV or JSON. The data path is website → Browse AI robot → robot Table → S3; setting up the connection does not automatically send every robot run to S3. Browse AI’s AWS S3 Integration Guide documents the setup.
What you need before connecting S3
- An AWS account with permission to create CloudFormation stacks and IAM roles.
- An existing S3 bucket, plus its exact name and AWS region.
- A random 20-character external ID made of letters and numbers. Browse AI’s guide uses it as an additional security verification.
- Optionally, a bucket folder path (prefix) if you want the integration restricted to a subfolder.
- A Browse AI robot with website data extracted into Tables when you are ready to export.
The documented CloudFormation resource creates a role with the minimum permissions needed for export; deleting the stack revokes those permissions. Follow the current AWS and Browse AI interface prompts, since labels and template behavior can change.
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Connect Browse AI to your S3 bucket
- In the AWS console, open CloudFormation and create a stack from this template: Browse AI S3 integration template.
- Enter a descriptive stack name, your exact bucket name, the 20-character external ID, and an optional bucket path.
- Review the stack settings and submit. Acknowledge the prompt about creating IAM resources if AWS displays it.
- After stack creation completes, open the stack’s Resources tab and locate S3AccessRole. Copy its IAM Role ARN. The ARN is also available in Stack info, according to Browse AI’s guide.
- In Browse AI, open the robot and select Integrate. Choose AWS, add an S3 bucket, and enter the bucket’s region, copied role ARN, same external ID, bucket name, and optional path.
- Save the connection. Browse AI can now export that robot’s Table data directly to the configured bucket.
For the documented sequence and connection requirements, see the AWS S3 Integration Guide.
Export a Table to S3
- Open the robot’s Tables view and select the tab and data you want to send.
- Set filters and visible columns, and choose whether the export should include historical data. These view choices affect the exported dataset.
- Select Export, then choose JSON to S3 or CSV to S3.
- Start the export and monitor its progress in Tables. Browse AI says this route transfers the file to S3 without downloading it locally.
CSV and JSON are both documented export formats; choose according to what your downstream workflow accepts. Browse AI explains the export flow in How to export your data to AWS S3 and the Table-view options in How to export data from Tables.
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Understand the exported files and folders
Browse AI creates a directory named in the pattern export_{ISO 8601 timestamp}_{unique export ID}, under the selected bucket or optional path. Files are organized by Table tab. Depending on export settings, a tab may be written as a combined CSV or JSON file, large output may be split into numbered parts, or separate-record export may produce one JSON file per record with record IDs in the filenames.
The integration guide uses a file smaller than 100 MB as an example of a small file. Treat that as an illustration, not a guaranteed chunking threshold. Check the current export interface or documentation for behavior applicable to your account and data.
Choose manual or scheduled delivery
Manual export
The Export flow above is a one-time action for the selected Table view and settings. If the robot runs again, export the resulting data separately unless you have enabled scheduled exports.
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Browse AI describes scheduled S3 exports as a beta feature and says users must contact support to unlock it. The documented prerequisites are an approved robot with extracted data and a configured S3 integration. The guide does not establish universal access or a guaranteed schedule cadence; confirm availability and timing in your account. See How to set up scheduled exports for AWS S3.
Troubleshoot connection and export problems
- Connection validation fails: Check that the external ID exactly matches in the CloudFormation stack and Browse AI connection, and verify the bucket name and region.
- Bucket cannot be reached: Confirm the bucket already exists and that the AWS account has permission to create the required CloudFormation stack and IAM role.
- Role access fails after setup: Verify the generated role has not been manually modified. The integration guide identifies changes to that role as a connection issue.
- You cannot find the role ARN: Open the CloudFormation stack’s Resources tab and inspect the S3AccessRole resource, or check Stack info.
- The export contains unexpected rows or columns: Revisit the selected Table tab, filters, visible columns, and historical-data setting before exporting again.
- The export appears split across files: Browse AI documents numbered chunks for large files. Check all parts under the timestamped export directory rather than assuming the first file is the whole dataset.
These checks follow Browse AI’s integration troubleshooting guidance.
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If your goal is a screenshot rather than structured website data in a Table, ScreenshotNeo is a website screenshot API and MCP server. It does not replace Browse AI’s Table-to-S3 export. One GET request captures a page as an image or PDF; see the ScreenshotNeo API documentation.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpScreenshotNeo accepts cookie or consent banners and removes more than 60 known consent platforms, newsletter popups, and chat widgets before capture; each of those steps can be turned off. Bot checks, blank pages, timeouts, failed loads, and cache hits are not billed, and responses identify the page verdict and billing status. Its MCP server provides take_screenshot, get_page_info, and capture_pdf for Claude, Cursor, and other MCP clients. The free plan includes 1,000 screenshots per month without a card; paid plans start at $5 for 3,000.
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