Daniel McKinnon's kid had a genetic mutation that clinical labs completely missed. The reason? Standard genomic analysis only checks variants within ~1 kilobase of a gene's regulatory region. His son Owen's deletion was a million bases upstream—way outside the search window.

The trade-off makes sense when humans are manually reviewing variants. You can't check everything. But McKinnon, who builds AI for rare disease genomics, thought differently.

A specialist eventually found Owen's variant manually. Years later, McKinnon's prototype AI recovered the same missed mutation—by running recursive loops. First loop: coding regions. Second loop: regulatory elements. Third loop: keep expanding the search radius until something shows up.

The insight: most genomic pipelines are single-pass. His system iterates. If it misses something, it expands the search space and runs again. This is how you catch edge cases that fall outside conventional heuristics.

The architecture is basically: run analysis → flag ambiguities → widen search parameters → repeat. Simple concept, but it's the difference between 'we checked the usual suspects' and 'we checked until we found it.'

This is what happens when someone with skin in the game builds the tool.