Vector Space Axioms as engineering operating evidence

#aiengineering #engineervetting #engineeringtelemetry #teamstationai

Keyword matching can get two engineer decisions wrong in opposite directions.

A profile can say "no Java experience" and still trigger a Java match, while another engineer can show deep Spring Boot work, leave out the word Java, and get missed. Same filter, two bad reads.

Vector Space Axioms shows the math under TeamStation's engineer-vetting operating system. Skills and concepts become coordinates, related ideas sit close even when the words change, and every interview answer stays in its own evidence unit before the final synthesis.

That matters when US teams vet AI engineers across LATAM bc a model can scale a weak match as fast as a good one. Semantic distance catches context, per-answer evidence limits halo effects, and human review still owns the call.

The source maps the full path from keyword failure to calibrated evidence:

https://engineering.teamstation.dev/decisions/vector-space-axioms/