AI deal flow workflow
A simple source, screen, score, follow-up, and review workflow for investors using AI to keep acquisition work organized.
Open resource →Proof and Operating Resources
A public reference shelf for the way I use AI in real work: sourcing, underwriting, seller follow-up, acquisition decisions, and operating reviews. These are examples and frameworks, not private deal disclosures.
Public Proof Assets
Seller follow-up summary
An anonymized example of what a useful post-call summary should capture: seller motivation, structure, open questions, and next action.
Open resource →RV park first-pass checklist
A first-pass diligence checklist covering site count, occupancy, utilities, zoning, financial proof, management, and exit risk.
Open resource →Before AI / after AI workflow
A public explanation of how AI changes the acquisition workflow without replacing the investor's judgment.
Open resource →Sample seller follow-up summary
A useful AI-generated seller summary should never pretend to make the decision. It should give the investor a cleaner human follow-up.
Seller motivation: retiring operator, tired of daily management, open to a quieter transition.
Asset fit: in target geography, site count inside buy box, utility diligence still unknown.
Structure signal: seller may consider a note if the buyer can protect continuity and timeline.
Open questions: current occupancy by site type, utility ownership, trailing twelve-month income, capex history.
Next human step: ask for financials, utility details, and whether a seller-financed transition is worth discussing.
Before AI / after AI deal flow
Before: scattered tabs, broker emails, county searches, notes in different places, and follow-up dependent on memory.
After: one ranked weekly queue, clear source links, buy-box tags, follow-up prompts, and open diligence questions ready for human review.
What stays human: relationship, negotiation, final underwriting, financing structure, site visits, and the acquisition decision.