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.

Great idea! Excited to see this develop further.

@martin_janse_van_rensburg Thanks a lot for your encouragement. Means a lot the team.

@martin_janse_van_rensburg Thanks, Martin! It's been quite a journey getting this out there. Excited for what's ahead and looking forward to learning from everyone using it.

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.

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.

Great idea! Excited to see this develop further.

@martin_janse_van_rensburg Thanks a lot for your encouragement. Means a lot the team.

@martin_janse_van_rensburg Thanks, Martin! It's been quite a journey getting this out there. Excited for what's ahead and looking forward to learning from everyone using it.

graph

Audit your Data & AI resume for missing context & evidence.

Audit your Data & AI resume for missing context & evidence.

Forum Threads

BTS - how graph helps identify the missing context

Forum Threads

BTS - how graph helps identify the missing context