Good work should not go unseen.
That is why we built graph.
graph comes from years of hiring for AI, data and analytics roles. That work shows us exactly where strong candidates lose out, and graph exists to fix it.
Years of hiring in AI, data and analytics showed us where good candidates lose out
We hire only for AI, data and analytics roles. Doing that for years, we kept seeing the same gap between what a candidate actually did and what their resume says. It is not the same gap for everyone: a data engineer and a computer vision engineer have almost opposite problems.
Over 8 in 10
of the improvements resumes claim are round figures, like 30% or 40%
Numbers read as estimates, few resumes say what the engineer owned, and the production incident story that interviewers ask for is missing.
See the four gaps, with a fix for eachAbout half
already cite papers or patents
But nothing tells a recruiter what was built or why it mattered, and the fundamentals a technical screen asks about, like hyperparameters and embeddings, almost never appear.
See the four gaps, with a fix for eachAlmost none
use the word “owned” to describe their work
Resumes describe the model that was built, but rarely the decision it supported or who owned the outcome. About two in three now mention GenAI, so it no longer sets anyone apart on its own.
See the three gaps, with a fix for each2 in 3
have a job title no other resume shares
Job titles vary so widely that a recruiter searching one label misses people who use another. Naming your real specialisation, and what you shipped with it, is what gets you found.
See how to make it findableNone of this is about ability. It is about work that was never written down properly, and what is missing depends on the role. graph is built to ask the domain-specific questions while the work is still fresh.
Figures come from our own analysis of real resumes we have reviewed: 128 Data Engineer resumes, 21 AI and computer vision resumes, 28 Data Scientist resumes and 15 ML and AI Engineer resumes. The last two sets come from candidates who had already reached an interview stage, so they are stronger than average, if anything, and small sets from a few job postings. Treat them as a guide, which is why small sets are described loosely, such as \u201calmost none\u201d. Figures show how many resumes mention each term, except the round-figure count, which is the share of 574 percentages quoted on the resumes.
The details fade before you need them
A line like “built data pipelines” hides the scale, the decisions, the trade-offs and the results that made the work hard. By the time someone sits down to update a resume or prepare for an interview, those details have faded, and what is left reads like everyone else’s.
We think no one should struggle to answer the question: why should I be hired, promoted, recognised or rewarded? graph helps you keep the context and evidence behind your work while it is fresh, and turn it into something others can understand.
Built data pipelines.
Migrated batch ETL pipeline to real-time Spark streaming on Kafka, reducing data latency from 6 hours to 90 seconds while sustaining 45,000 events/sec.
An illustrative example.
What we believe
Four ideas that shape what graph does, and what it will not do.
Your graph belongs to you
It is private by default, and nothing is shared with employers or other third parties without your permission.
Specific beats impressive
A clear account of what you did and what changed is more credible than a polished summary.
Being seen should not be homework
graph asks questions because it is curious about your work, not to hand you a list of things you owe.
Built for one field
Because graph focuses on Data, AI and Analytics, it can ask the domain-specific questions a general tool will not.
Make your work impossible to overlook.
Start with your resume. graph shows you what it understood and what is missing.