Where AI fits in a responsible lead research workflow

Define the research question first

Describe the type of organization you want to understand and the reason it may need your service. Ask for specific information from sources you are permitted to use. A narrow question is easier to review than a broad request for a large list of prospects.

Separate evidence from inference

A company’s published service page is evidence of what it says it offers. A model’s guess about budget or purchase intent is an inference. Keep those categories distinct in your research notes, and preserve source references in the process that produces your records.

Review before creating a lead

Check whether the contact details belong to the intended person or organization and whether the information is current enough for your purpose. Do not fill missing fields with plausible guesses. Decide what information belongs in the lead record and what should remain in the research system.

Connect through a controlled workflow

A custom server-side integration can submit reviewed lead fields to Happitap. Use the correct workspace, protect credentials, validate the payload, and test with fictional contacts. Design duplicate prevention and retry handling before scheduling repeated runs.

Keep outreach a separate decision

Creating a lead record does not establish permission to contact someone or prove purchase intent. Have a person decide whether a conversation is appropriate and what would make it useful. Use AI to support that judgment, then keep the outcome and next step clear in the workspace.