Cody Pettit

What Cody writes about

Cody’s writing is concentrated on the operational and methodological layer of AI recruiting. The questions he addresses are the ones a thoughtful operator asks when evaluating an AI sourcing platform: how does the scoring actually work, how is it validated against placement outcomes, what signals does the system read on a candidate profile, when does it fail, what does the funnel look like in production, and how should an agency think about the trade-off between human review and autonomous operation.

He writes about the system at the level a competent operator needs to understand it — outcomes, mechanisms, validation, and limits. He does not write about the inside of the system: production prompt text, internal scoring logic, model configurations, third-party tooling identifiers, or any artifact that would let a competitor replicate the engineering work. That is a deliberate editorial constraint, not an oversight.

Areas of expertise

The topics Cody accepts authorship on:

Background

Cody co-founded ExecutiveSearch.AI with Ken Charles in 2017 and has been his partner ever since. As Head of Data Operations at ExecutiveSearch.AI K.K. — the technology company operating Headhunt.AI — he runs the team responsible for the candidate scoring system, the production AI evaluation pipeline, and the data architecture that the platform stands on. Before partnering with Ken in Tokyo, Cody arrived at recruiting from outside the industry, which has shaped a candidate-first orientation in every process decision the firm has made.

Editorial standing

Cody is the named author on Headhunt.AI articles covering AI candidate scoring methodology, production AI operations, and the data architecture of AI-first recruiting. Articles relying on production data from ExecutiveSearch.AI K.K. follow the methodology and disclosure standards documented in our methodology disclosure, including sample sizes and the period from which the data is drawn. The trade-secret protections that govern what Cody can and cannot write about — including production prompts, scoring weights, and internal evaluation logic — are documented in our editorial standards.