The Art of Passive Income Efficiency in Land Investing: Using AI to Maximize Deal Flow
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Dec 4, 2025 
Join Fred, co-founder of Reworked/WeWorked AI, as he shares his innovative journey from land investing to developing 'Betty'—an AI that optimizes mailing efficiency. He discusses how machine learning eliminates human bias, improving seller targeting and cutting costs. Discover surprising demographic indicators predicting sales readiness and why traditional scrubbing methods may hide valuable leads. Fred emphasizes the importance of data over assumptions, and how smarter mailing strategies reduce waste and enhance deal flow.
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Mailers Built A Data Partnership
- Fred met his WeWorked AI co-founder after accidentally mailing a non-seller who turned out to be a data scientist.
- That mailer sparked a year-long collaboration that led to building Betty to score seller propensity.
Segment Mailers By Propensity Score
- Use Betty to shrink large mail lists into high-propensity segments so you spend far less on postage.
- Action the top-scoring portion with premium outreach and treat lower tiers with cheaper channels like standard mail or texts.
Supervision Prevents AI Hallucinations
- LLM hallucinations happen because models operate in uncontrolled environments.
- Betty avoids hallucination by using supervised machine learning with defined inputs and outputs.