We did not repeat the same demo three times. Each experiment was designed to expose a different failure mode in AI-assisted prospect research.
我們不是把同一個 Demo 做三次。每個實驗都刻意測試 AI Prospect Research 中不同的弱點。
A Canadian outdoor-accessories brand explored Japan. The test focused on importer, distributor, retailer, and manufacturer classification, deduplication, skeptical audit, and contact-route separation.
以加拿大戶外配件品牌探索日本市場，重點測試 Importer、Distributor、Retailer、Manufacturer 的角色判斷、去重、反向 Audit 與聯絡路徑分離。
The mission began with “find distributors.” Repeated market evidence showed that integrators and engineering firms also controlled access to deployment, so the mission was formally revised.
一開始的任務是「找 Distributor」，但市場研究反覆顯示 Integrator 與 Engineering Firm 也掌握實際導入，因此系統正式修改 Mission。
FlyPig used itself to find Canadian early adopters, then a fresh high-capability LLM solved the same business problem without reading the FlyPig method.
FlyPig 用自己找加拿大第一批潛在採用者，之後再讓一個全新的高階 LLM 在完全不讀 FlyPig 方法的情況下處理同一問題。
The experiments focus on whether prospect research stays consistent, traceable, and correctable as the mission becomes larger and more ambiguous.
這些實驗聚焦一件事：當任務越來越大、資訊越來越模糊時，研究流程能否維持一致、可追蹤，而且發現錯誤後能修正。