Prospect research, qualification, review, and follow-through
I designed and operate an Airtable CRM workflow through AI chat and connected tools. I review qualification, drafts and next actions, and corrected overlapping follow-up plans so callbacks and holds stay intact. How I build and review →
I organized the work around the decisions needed before outreach: whether a prospect fits, what the research supports, whether the contact is appropriate, and what factual next action should follow.
Keep qualification, proposed drafts and manual sending separate. A draft is something to review, not proof a message was sent.
Represent holds, stop conditions and requested callbacks so a generic cadence cannot overwrite a specific contact outcome.
Check existing actions before creating more, and distinguish planned activity from completed work.
Sample Harbor Services: record the source and a possible workflow need before claiming a fit.
Business fit appears plausible. Contact-role evidence is missing, so the record remains on hold.
Next action: verify the decision-maker’s role. The example does not send a message.
Apollo research → Airtable record → AI-assisted draft → human review → manually controlled next action
The project materials below show the requirements, decisions and workflow behind this case. Interactive examples use fictional data.
The September 4 operating review documents a 100-prospect queue and an initial pilot. I kept completed activity, planned follow-up and unresolved items separate so the record reflects what actually happened.
Evidence: Final September 4, 2026 operational reconciliation; private records paraphrased.
Prior calls overlapped with a generic future email schedule. I had the follow-up plans corrected to preserve requested callbacks and holds and reflect the actual contact outcomes.
Evidence: Final operational correction record; all personal and company details omitted.
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