Most hiring systems begin with resumes and keyword matching. At InGen Labs, we are exploring a different starting point: the evidence a candidate can actually show.
The platform uses specialized AI agents to help recruiters and founders move from an ambiguous hiring request to an interview-ready shortlist. Aristotle converts a rough requirement into a sharper role brief. Sherlock then examines public professional evidence, including work visible through platforms such as GitHub and LinkedIn, to help surface higher-signal candidates.
What the system needs to do well
- Translate business language into clear technical and behavioral requirements.
- Keep claims traceable to evidence instead of producing unsupported rankings.
- Present findings in a form that a hiring team can review quickly.
- Preserve human judgment for final evaluation and interviews.
The engineering work combines full-stack product development, data pipelines, retrieval, agent orchestration, and careful output design. The goal is not to automate hiring decisions. It is to reduce the time spent searching and give decision-makers a more useful starting point.
This project reflects the kind of system I enjoy building: technically ambitious, grounded in a real workflow, and designed around a measurable user problem.