AI-powered investment screening, adopted as the client standard

The client

A leading Asian firm whose corporate investing, M&A, and core business teams each assessed targets their own way. Three groups, three vocabularies, three spreadsheets, and no shared answer on which companies deserved capital and time.

The work

I designed the investment thesis and deal-sourcing framework first, because a screening engine without a thesis is a faster way to be busy. Then I built the AI-powered evaluation workflow on top of it: a large-scale company universe screened down to a qualified shortlist, each target scored the same way, each one carrying a clear recommended route, whether to partner, invest, or acquire.

The outcome

The client adopted the methodology as its standard for investment and acquisition decisions. Three groups that had worked in silos now run one process, and the client's internal team operates the sourcing engine independently, without me in the loop. That last part is deliberate: a process the client cannot run alone is a dependency, not a standard.

What this shows

AI screening at scale only works when the judgment layer is designed first. The machine reads the whole universe the same way every time; the thesis and the gates decide what a qualified target actually is.

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