From CV to decision, in three steps.
No spreadsheets, no manual comparison. Here's exactly what happens between dropping in a CV and having a scored, ready-to-act-on candidate.
Name your analysis & pick the job
Give the analysis a name you'll recognise later - the client, the req number, whatever fits your workflow. Then pick a preset role or paste in your own job description. Helixon reads it the same way a hiring manager would: required skills, nice-to-haves, seniority, and tone.
Upload the CV
Drag in a PDF or Word file. No reformatting, no copy-pasting into a template - Helixon parses the document as-is, including tables and multi-column layouts, in a matter of seconds.
Get your score
You get a match score out of 100, a plain-English summary of why, standout factors, red flags worth asking about, and a ready-to-send follow-up email - all on one screen, ready to drop into your pipeline.
Under the hood
What's actually happening at each step
What Helixon actually reads
Work history, dates, titles, skills listed and skills implied by the roles held, education, and any certifications on the page. It also reads the job description the same way - pulling out what's required versus preferred, and the seniority level the role is written for.
How the score is calculated
The score weighs how closely a candidate's experience, skills, and seniority match what the job description asks for. It's not a keyword count - a candidate missing an exact job title but with equivalent responsibilities elsewhere still scores fairly.
What counts as a red flag
Unexplained employment gaps, a mismatch between stated seniority and years of experience, or a pattern of short tenures. These are surfaced as talking points for your screening call, not automatic disqualifiers.
Your data stays yours
CVs and job descriptions are processed to generate your analysis and are never used to train any model. Everything is stored in Switzerland, which the UK and EU recognise as adequate, in line with GDPR.