Lesson 18 of 20
From Three Incompatible Equipment Proposals to One Number Your Capital Committee Can Approve
Supplier Screening & Evaluation · Capital equipment / facilities
Synthetic case data — evidence from real ChatGPT, Claude, Gemini and Copilot runs.
The cheapest proposal on your desk is $78,650 more expensive than it looks.
Three vendors quoted six electric reach trucks over five years. Summit's operating lease came in at $333,000, the lowest headline. Ironclad's purchase came in at $375,400, the highest. Once you normalize the cost categories, add the battery Summit excludes, the excess-hours surcharge it buries in the terms, the return fee, and subtract the residual value from the ownership options, Summit is the most expensive at $400,050 and Ironclad is the cheapest at $321,400.
Same proposals, same arithmetic, different question. Two skills make that flip visible: extracting one comparable cost structure from three incompatible formats, and compressing it into the one-page memo a capital committee can approve in ten minutes.
You’ve read the free three. The other seventeen are where the misses live.
The first lessons show what a general-purpose AI catches on a procurement task. The rest show what it misses — the unit conversion that survives an expert prompt, the two clauses it lists but never connects, the table that contradicts its own arithmetic. That gap is the job.
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