A lot of you must be wondering how does @graph actually audit a Data & AI resume. So thought of sharing what's under the hood.
Every bullet point gets checked against 7 things:
What: the work itself. What was built, changed, fixed or delivered.
How: the tools, languages, platforms or methods used.
Scale: how big the work was. Data volume, record counts, number of pipelines or users, budget. "Large-scale" with no figure is vague. And a percentage never counts as scale.
How much: a measured result. A percentage, time saved, cost, a before and after. "Improved" or "faster" with no number is vague.
Why: the problem or purpose behind the work. What was wrong before.
Your role: was it actually your work? Built, led, owned pass. Helped, supported, "worked on" are vague.
Outcome: who it helped. Users, the team, the business, a system. If the effect is only implied, it's vague.
Ironically, the best way to stand out in today's job market is to be more human: genuine, specific, credible, and uniquely you.
graph helps you get there.
Unlike generic AI resume builders, graph probes you like an interviewer to uncover the decisions, trade-offs, constraints and outcomes behind your work.
It turns these insights into a resume that shows not just what you've done, but why it matters, and helps you prepare to defend your experience in interviews.
