You can send website data directly to Amazon S3 by first extracting it with a Browse AI robot into a Table, connecting Browse AI to your existing S3 bucket, then exporting the Table as CSV or JSON. The direct export goes to your bucket without downloading a local copy; it does not automatically send every robot run to S3 unless you separately configure scheduled exports. Browse AI’s AWS S3 Integration Guide documents the connection setup.
What the Browse AI–S3 integration does
The workflow has three distinct stages: a robot extracts information from a website into Browse AI Tables; you connect your AWS account and S3 bucket to Browse AI; then you export the Table data you want. Manual export is the standard documented flow. Scheduled exports are a separate beta feature with an access requirement.
What you need before setup
- An AWS account whose user or role can 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 setup guide calls for this as an additional security verification; use the same value in AWS and Browse AI.
- A Browse AI robot and extracted data in its Tables when you are ready to export.
- Optionally, a bucket folder path (prefix) if you want the integration limited to a subfolder.
The CloudFormation stack creates an IAM role with the permissions the guide describes as the minimum needed for export. Deleting the stack revokes those permissions. Review the template and the resources AWS will create before submitting it. The bucket must already exist. See the official integration guide for the setup details.
Connect your S3 bucket to Browse AI
- Create the AWS stack. In the AWS Console, open CloudFormation and create a stack using the existing template URL https://browse-ai-integration.s3.us-east-1.amazonaws.com/s3-integration.yaml.
- Enter the stack parameters. Choose a descriptive stack name and enter the exact S3 bucket name, your 20-character external ID, and an optional bucket path. Check the region and spelling carefully.
- Review and submit. Review the proposed resources and acknowledge the IAM resource creation prompt if shown. Wait for the stack creation to complete.
- Copy the role ARN. In the CloudFormation stack’s Resources tab, find
S3AccessRoleand copy its IAM Role ARN. If it is difficult to locate, check the stack’s Stack info as well. - Add the connection in Browse AI. Open the relevant robot, go to Integrate, choose AWS, and add an S3 bucket. Enter the bucket region, role ARN, same external ID, bucket name, and optional path, then save the connection.
Browse AI’s interface labels may change; confirm the current labels in your account as you follow the flow. A saved connection enables direct exports from Tables to that bucket. The detailed sequence is in the AWS S3 Integration Guide.
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Export Table data as CSV or JSON
- Open the robot’s Tables and select the tab or view you intend to export.
- Set the filters and visible columns to define the data you want. Choose whether the export should include historical data or only the latest data, as applicable.
- Click Export, then choose CSV to S3 or JSON to S3.
- Start the export and follow its progress in Tables. Browse AI sends the data directly to the bucket; no local download is needed for this route.
The selected view matters: filters, visible columns, and the historical-data setting affect the exported dataset. Browse AI documents both direct-to-S3 formats and Table export controls in its AWS S3 export steps and Tables export guide.
Choose the format for the destination system
| Format | Use it when |
|---|---|
| CSV | Your downstream workflow expects a tabular, delimited file and you want the exported Table data in that format. |
| JSON | Your downstream workflow expects JSON records or a JSON file structure. |
The documentation provides both choices but does not establish one as universally better. Base the choice on what will consume the export.
Understand the files Browse AI creates in S3
Each export is placed in a directory named using the documented pattern export_{ISO 8601 timestamp}_{unique export ID}. If you set a bucket path, the export directory sits under that prefix. Files are organized by Table tab. An export may produce one file for a tab, or split a large file into numbered parts. The integration guide uses a file smaller than 100 MB as an example of a small file; it is an illustration, not a guaranteed universal size threshold. If separate-record export is configured, JSON files use record IDs in their filenames.
These details help when setting up downstream ingestion: account for timestamped directories rather than expecting every run to overwrite one fixed object key, and allow for multiple parts or per-record files where those options apply. See Browse AI’s integration guide and export guide.
Set up recurring exports only if your account has access
Browse AI documents scheduled S3 exports as a beta feature and says to contact support to unlock it. The documented prerequisites are an approved robot with extracted data and a configured S3 integration. The available cadence and access can depend on the account, so check the current account documentation or support before designing a recurring pipeline around it. Follow the scheduled exports guide.
Troubleshoot connection and export problems
- Connection fails validation: Check that the external ID entered in Browse AI exactly matches the one used when creating the CloudFormation stack.
- Bucket cannot be found: Confirm the bucket exists and that its name and AWS region are correct in both services.
- Stack creation fails: Confirm the AWS identity has permission to create CloudFormation stacks and IAM roles, and review any permission or resource errors shown by AWS.
- Role ARN is missing or unclear: Open the CloudFormation stack’s Resources tab and locate
S3AccessRole; the Stack info may also help identify it. - The connection worked but export access fails: Check that the generated role has not been manually modified and that the configured bucket path matches the intended destination.
- No expected rows or columns appear: Recheck the selected Table tab, filters, visible columns, and whether the export is set to include historical data.
- Export has not completed: Check export progress in Tables. For AWS-side issues, inspect the stack and its status; for role, bucket, region, or external-ID checks, use the official integration troubleshooting guidance.
Or skip the browser setup
If your goal is to capture a web page as an image or PDF rather than extract structured records into a Browse AI Table, ScreenshotNeo is a separate website screenshot API and MCP server. It does not replace this Browse AI-to-S3 Table export; it is for taking page screenshots.
One GET request returns a screenshot or PDF. For example, using cURL to save a WebP screenshot:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpSee the ScreenshotNeo API documentation for options and setup. It can accept cookie or consent banners and remove 60+ known consent platforms, newsletter popups, and chat widgets before capture; each step can be turned off. Bot checks, blank pages, timeouts, failed loads, and cache hits are not billed, and response headers indicate the page verdict and billing status. Its MCP server offers take_screenshot, get_page_info, and capture_pdf for AI agents. The free plan includes 1,000 shots per month with no card; paid plans start at $5 for 3,000.
Sign up for ScreenshotNeo’s free plan to get 1,000 screenshots a month with no card.
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