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Cheap testsEvidence-driven iterationProduct-market-impact fit

GetFound

Testing the intervention before scaling it

Testing the weakest assumptions of a reverse-recruitment model before committing substantial engineering capacity.

01

Problem and crux

GetFound was testing reverse recruitment, where employers approach pre-vetted candidates. A full product would have been expensive, while several basic assumptions were still uncertain.

The crux was whether candidates and employers would complete the full path to a successful placement.

02

Assumptions to test

Candidates needed to want the model and complete a demanding onboarding process. Employers needed to trust the profiles. The service then needed to produce actual placements.

03

What I did

Initial paid acquisition produced less than 1% click through and about 1.7% downstream conversion, so I changed the approach. I built a lightweight no-code candidate experience and kept parts of the service manual. This let us test the full causal chain before committing engineering capacity.

04

Evidence and result

>70%Onboarding conversion
84%Assessment completion
CHF 2.2mLater funding round

The redesigned funnel produced hundreds of vetted candidates and the first successful placements. The evidence also contributed to a later CHF 2.2 million seed round.

05

Relevance to AI safety operations

New safety interventions often begin with substantial uncertainty. I know how to isolate a decision relevant assumption, build a cheap test and use the result to decide whether the work deserves more resources.