Extract prospect data, qualify leads and draft personalised outreach with Bardeen’s AI tools and connected workflows.
Bardeen combines browser automation, data extraction and AI tools for lead research. Teams can gather information from web pages, enrich records and apply qualification criteria before preparing outreach. Its AI offering is therefore connected to a data workflow: a generated message can use the information collected about a prospect rather than begin with an empty prompt.
The scraper extracts information from websites, while connected tools can supply an existing list from Google Sheets, Notion or Airtable. AI Qualifier evaluates records against the user’s criteria and provides reasoning for the result. This is useful when a list needs more context than a simple title or location filter. The user still needs to check that the collected information is current and the qualification rule reflects the intended audience.
AI text generation turns extracted data into draft emails, social content and other personalised text. Records can then move back to a supported destination or be downloaded as CSV. Scraping, enrichment and generation can be combined into a repeatable sequence, reducing the need to move each record manually between separate interfaces.
Bardeen’s Chrome extension supports its browser-based workflows and requests permissions used for navigation, scraping and integrations. The security documentation describes local browser storage for connected application data, alongside specific cloud execution arrangements. Teams should evaluate the permissions and execution mode that apply to their own automation rather than assume every task runs in the same location.
Pricing is based on credits consumed by actions and output rows. Scraping and AI tools generally use one credit per row, while enrichment uses more; utilities and CSV downloading are described as free. Paid plans expand the available budget and enterprise arrangements add custom support. Unused credits expire with the relevant billing period. A sensible rollout tests a small source list, reviews the extracted columns and qualification explanations, and checks the final message before any outreach. Users must also respect source-site rules and the permissions governing prospect data.
