A graph CV is your career as a living, connected structure — not a flat document.

The résumé as a compression artefact

A résumé takes years of work — skills developed, roles held, projects completed, domains overlapped — and compresses them into a format designed for quick human scanning. That compression is useful for recruiters. It discards the structure that makes a career legible as more than a list of dates and titles.

A graph CV does not replace the résumé. It makes the structure visible that the résumé was compressing.

What a graph makes explicit

A graph makes three things explicit that a flat document leaves implicit. Connections: skills connect to the roles and projects they came from, and to adjacent skills they overlap with. Provenance: a claimed skill can carry the evidence of where it came from — the role, the project, the domain context. Exposure: AI task-overlap is a property of the nodes and their connections, readable as a number and visible as a position on a resilience ramp.

These three things together produce a representation of a career that is navigable — by you, by a recruiter who wants depth, and by an AI system parsing your fit for a role.

Person-first, not document-first

The cvgraph design is deliberately person-first. You build the graph from your actual skills and roles, not from an uploaded document or a scraped profile. That means the graph reflects your real work — including the parts that do not fit neatly into a job title or a keyword list.

Building a graph CV takes more intentional input than uploading a PDF. It produces a more accurate representation of your career as a result.

What you can do with a graph CV

A shareable graph CV is a public billboard for depth. It carries your skill graph, your AI-exposure position, and the provenance behind your claims — readable by a recruiter, a hiring manager, or an answer engine assessing your fit.

The AI-exposure layer makes career resilience legible: not as a general anxiety about technology, but as a specific, measurable position that you can track and move over time through deliberate skill work.

Frequently asked questions

What is a graph CV?

A graph CV is your career represented as a connected structure of skills, roles, and occupations — not a flat document. It makes the connections between your skills visible, attaches provenance to your claims, and shows your AI-exposure position as a measurable coordinate on the graph.

How is a graph CV different from a LinkedIn profile?

A LinkedIn profile is structured around job titles, endorsements, and a social network. A graph CV is structured around the actual connections between your skills, roles, and task domains — and includes an AI-exposure layer that LinkedIn does not model. It is a technical representation of your career, not a social one.

Do I need to upload a CV to build a graph CV?

No. cvgraph is built person-first: you add your skills and roles directly, not by uploading a document. This produces a more accurate graph because it reflects what you actually know and do, not what a parser extracted from a formatted page.

What does 'exposure' mean in a graph CV?

In a graph CV, exposure is a 0 to 1 measure of how much of your current skill and task mix overlaps with areas where AI systems are demonstrably capable. It is calculated from your personal graph, not from a generic occupation code, so it reflects your specific work rather than an average.

Who is a graph CV for?

A graph CV is most useful for people who want to understand the depth and connectivity of their career — in a transition, when making a case for a non-obvious role, or when tracking AI-career resilience over time. For a recruiter doing an initial keyword filter, the résumé is still the faster format; the graph carries depth alongside it.