Build 06 / Respectful Audience Discovery
Audience Finder AI
A planned respectful audience discovery system for opportunity queues, fit scoring, memory, and human-gated decisions. The model surfaces candidates; the operator decides what is real.
Greg's take
Audience selection in B2B marketing often sounds more certain than the data underneath it.
AI makes that faster. It does not automatically make it more reliable. The same judgement calls still happen, only faster and harder to audit.
This build puts the evidence and the operator decision back into the audience selection workflow.
Build milestones
Product maturity checkpoints.
Audience Queue
The build frames audience discovery as a queue rather than a loose list.
Manual Accept/Reject Workflow
Human decisions stay explicit before any audience action is taken.
Evidence Trail
Each opportunity is positioned around supporting evidence and review context.
Export and Diagnostics
The active layer prepares reviewed audience choices for useful output.
Memory-Adjusted Prioritisation
The next step uses previous decisions to improve future queue order.
The problem
AI can find audiences faster, but speed does not make the audience trustworthy.
Segments hide assumptions
Audience decisions are built on partial signals, ageing data, market assumptions, and manual judgement.
Scoring needs explanation
A fit score without evidence is just a black box with a number attached.
Review cannot disappear
The operator needs to see the suggested site, assess fit, and decide whether it deserves action.
Memory prevents repeat waste
Accepted and rejected domains should be remembered so the system learns from review decisions.
What it does
A planned review loop, not an outreach automation engine.
Review queue
Planned queue for reviewing suggested audience opportunities.
Human gated
Every opportunity is checked before any action.
Memory layer
Planned memory for reviewed domains and repeated decisions.
No autonomous action
No outreach automation, scraping, remote writes, or auto-submission.
How it works
A respectful audience intelligence loop.
This planned build shows the review-loop model: discovery inputs, fit scoring, memory, and human review before action. A future production path would connect approved discovery inputs, AI qualification and scoring, memory, human review, and client-ready outputs.
Finder Agent
Discovers candidate websites, communities, directories, newsletters, podcasts, and events.
Qualification Agent
Scores fit, authority, backlink value, respect value, effort, and spam risk.
Pitch Agent
Drafts a respectful submission angle or introduction note for manual review.
Memory and review
Stores reviewed opportunities and keeps Greg in the approval loop before action.
Planned review flow
Audience opportunity review queue concept.
The planned workflow keeps audience discovery human-reviewed, evidence-backed, and separated from autonomous outreach.
1. Evidence collection
Candidate audiences would be gathered from approved sources with clear fit signals and context.
2. Human review
A reviewer would inspect the opportunity, evidence, risk, and suggested next action before anything happens.
3. Decision memory
Accepted, rejected, and watch-list decisions would become memory for future scoring and review.
Greg's take
Audience selection is usually treated like a clean targeting exercise. It is not. It is a pile of assumptions, partial signals, ageing data, and judgement calls dressed up as segmentation.
The AI can help. But it should not quietly decide who matters and who does not. That decision needs to be visible, evidence-backed, and operator-controlled - not embedded in a scoring model that nobody can explain when the campaign underperforms.
This build turns audience selection into a queue with evidence, scoring, accept/reject decisions, and an operator who stays in the loop at every step. The model surfaces candidates. The human decides what is real.
An AI that recommends audiences without a review step is not a targeting system. It is a black box with an export button. The value is not in the recommendation - it is in the evidence trail and the operator decision that follows it.
Want to discuss Audience Finder AI?
For speaking leads, respectful community introductions, or ideas for the audience discovery workflow.
