This is a thoughtful approach to a real problem. Interview quality usually breaks down not because teams don’t care, but because creating structured, compliant interview material takes a lot of time — especially when hiring managers are stretched.

I like that you’ve focused on research + structure + compliance, rather than just generating random questions. In practice, that’s where many AI interview tools fall short: they produce content that sounds good but isn’t consistent or defensible when used at scale.

One thing I’ve seen matter a lot once these kits are in use is how well they translate into the actual interview experience. Even with good rubrics, interviewers and candidates often struggle with alignment — what’s being asked, what “good” looks like, and how answers are evaluated. That’s where downstream tools for interview execution and preparation come into play. Platforms like Futuremug, for example, sit closer to the interview interaction itself, helping candidates and teams practise and standardize how those questions are handled in real conversations.

Overall, this feels like a solid building block in the hiring workflow. The long-term value will probably come from how well these kits integrate with real interview behaviour and feedback loops, not just generation quality.