In this episode, we talk with Aaron Jorgensen about how JobHive came to life - starting as a small résumé-parsing experiment and gradually growing into a structured, AI-supported interview workflow. Aaron explains how the system handles voice capture, transcription, prompts, and AI avatars, and why he moved toward a multi-agent approach instead of relying on one model to do everything.
We dig into what “fair scoring” actually means, how cross-checking evaluators and confidence levels work, and why it’s important to keep the reasoning behind decisions visible to both employers and candidates.
From the builder’s perspective, Aaron walks through the practical side of developing the platform: shaping an MVP, working with LangChain, choosing AWS tools that reduce overhead, and dealing with the usual setbacks—broken features, unreliable external services, and the moments that test your patience. He also talks about the routines and habits that helped him stay consistent during the harder stretches.
If you’re interested in hiring workflows, AI tooling, or the reality of turning a rough prototype into a functioning product, this conversation covers it all.
To learn more about Aaron's work, check out his websites or reach out to him on socials:
JobHive: https://jobhive.ai
Aaron's Website: https://ajeema.com/
LinkedIn: https://www.linkedin.com/in/mraaronjorgensen/
Circle: https://pybites.circle.so/u/22287446
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Book mentioned in ep: https://pybitesbooks.com/books/P3EFa-WuMMkC
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