Most companies treat interview notes as receipts. The candidate is hired or rejected, the note proves it happened, the note is filed. A year later somebody asks "what did we learn from those 40 senior PM interviews" and the answer is silence.
A knowledge graph treats notes as nodes. The candidate is a node. The interviewer is a node. The role is a node. Each skill, each competency, each company a candidate came from. The edges between them are the relationships that emerge across interviews.
This sounds abstract until you see it work. A team at an AI infrastructure startup used their interview graph to do something nobody had told them to do. They benchmarked compensation. The graph knew which candidates had performed strongly in interviews, where they were coming from, and what they were earning. The team saved roughly 50,000 dollars per hire by setting comp bands based on actual demonstrated performance rather than market averages.
That use case was never designed. It emerged because the graph existed.
Other emergent uses. Finding the third best senior engineer from six months ago when a new role opens. Identifying which interviewers consistently predict on the job performance. Spotting the moment a JD started attracting the wrong candidate pool.
You cannot do any of this with notes in a Google Doc. You need the data structured, linked, and queryable. Neo4j or FalkorDB style graphs are the right primitive. Mazle's Cortex layer is built around this exact pattern.
The shift in mindset is the hard part. Stop thinking about interview notes as records of a decision. Start thinking about them as the slowest cheapest research database your company will ever own.
