Case study · June 1, 2026
AI recruiting at scale
Search, evaluation, integrations, and data systems built for recruiting workflows where quality and reliability both matter.
The problem
Recruiting software combines human judgment, inconsistent data, search relevance, and operational scale. A system can be technically correct and still be unhelpful. It can also be clever and impossible to operate.
My scope
Across my work at SeekOut, I have built and led systems spanning LLM-assisted candidate evaluation, search, partner APIs, recruiting integrations, and the data systems that support them.
Selected outcomes
The work helped recruiters make sense of large candidate pools, supported dependable partner integrations, and gave engineers a more practical way to use AI in their daily work.
What mattered
Across those systems, I kept coming back to the same things: clear contracts, observable failure modes, evaluation grounded in real tasks, and architecture that the next engineer could understand without a private tour.
Some implementation details remain private, so this case study focuses on the shape of the problems, my role, and the public outcomes rather than internal architecture.