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ProductCandidate intelligence

A clearer read on every applicant.

Turn résumés and applications into a structured profile your team can search, compare and revisit — without reading every CV from scratch.

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Studio North / hiring workspaceInteractive demo
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Candidate profile

Alex Morgan

Product designer · Example candidate

Current stage Applied

Everything, in context.

Application, experience, files and feedback. One profile your whole team can work from.

Portfolio & résuméFiles stay with the candidate
Team feedback

“Thoughtful approach to research. Let’s explore the project work in the interview.”

JLJamie, hiring team
Made-up people. A real feel for the workflow.

A structured profile, from the application.

Rolefern reads each application into a consistent profile — skills, seniority, years of experience, education and languages — so similar candidates are described in comparable terms instead of ten different résumé formats.

Find the right people faster.

Filter your candidate database by the skills a role needs, a seniority band, a minimum experience, an education level or a language. Combine filters to shortlist from a large pipeline without opening every profile.

A summary, not a replacement for judgement.

Use the structured read as a starting point for the conversation, not a score that decides for you. Your team still reviews the person, their work and their context before moving anyone forward.

From a stack of applications to a shortlist you can defend.

A structured profile should help your team compare people fairly and explain a shortlist, not hand the decision to a score.

  1. Let the profile do the first pass.

    As applications arrive, Rolefern reads each one into the same structured shape — skills, seniority, experience, education and languages — so you can scan a pipeline in comparable terms instead of ten different résumé layouts.

  2. Shortlist with filters, not guesswork.

    Narrow a large pipeline to the people a role actually needs: the required skills, a seniority band, a minimum experience or a language. Keep the filters you used so the shortlist is easy to explain later.

  3. Review the person, then decide.

    Open the full profile — application, résumé, files and feedback — before moving anyone forward. The structured read starts the conversation; your team still makes the call.

What a structured candidate read contains

A fictional example for a mid-level engineering role. These are signals to support your review, not an automatic decision.

Skills
TypeScript, React and AWS, with some backend exposure.
Seniority & experience
Mid-level, around five years, matching the band for the role.
Education & languages
Computer science degree; works in English and German.
Next action
Shortlist for a first conversation and confirm the backend scope in the interview.

A few practical
details.

Want the wider picture?
More questions, answered

Does the AI decide who to hire?

No. It reads applications into a structured profile and powers search and filters. Your team reviews the person and makes every hiring decision; the profile is a starting point, not a score.

Can we search by skills and seniority?

Yes. Filter the candidate database by skills, a seniority band, a minimum experience, an education level or languages, and combine them to shortlist from a large pipeline.

What if the structured read gets something wrong?

Treat it as a prompt to check, not a verdict. The full application, résumé and files stay with the candidate, and your review is what moves someone forward.

Put the idea into practice.

Keep the rest connected, too.

Applicant trackingKeep applications, candidate profiles and hiring progress connected in one workspace your team can actually follow.Careers pagesGive candidates a place to meet your company, find their role and apply. A careers page that carries your identity from the first click.Team collaborationBring messages, interviews and feedback together around the candidate. Give everyone involved a clearer way to contribute.