A fit score that shows its work.

Crosshire's matcher — the fit-scoring product at fitscore.crosshire.ch — does one thing, deeply: it reads a single CV against a single job description and returns an honest fit. A score with its worst case, every skill traced to the line that proves it, the gaps named out loud, and a clear verdict. Not keywords. Not a ranking you can't interrogate. About fifteen minutes, and the same engine serves both sides of the desk. This is the tour of what it does and how to use it.

It answers a single honest question, asked from two directions. The reading underneath is identical — only the reader changes.

Same engine, two perspectives. It's tuned today for data, software and AI roles across the DACH region and the UK — the taxonomy, the scoring calibration and the skill ontology are built for those specifically; adjacent fields still work, with less precision.

FitScore names the shape of it plainly, from both ends of the same broken exchange:

The job market doesn't have an information problem; it has a signal problem. Most AI tools make it worse — helping people apply to more roles, faster, with even less context. That's volume, not signal. FitScore takes the opposite line: read deeply, score honestly, cite the evidence, and tell both sides the truth.

Every pairing produces the same six things — each one written for whoever is reading it.

The headline is one word, but it never arrives naked. It sits on a rung you can read at a glance — and it carries the downside, not just the upside.

Beneath the verdict sits the Crosshire Score, 0–100, banded so the number becomes a judgement: 85+ reads Excellent, 70+ Good, 55+ Fair, below that Poor. And it always shows its worst case alongside its best — because a score that hides its downside is exactly the thing a screener will find for you.

Abstract is easy to trust and easy to dismiss. So here is one whole Summary, start to finish — a senior data architect scored against a mid-level Data Analytics Consultant contract (Databricks/Azure, 100% remote, ~5 months). Every company name is anonymized; everything else is exactly what the engine returned.

Underneath the headline, the score is decomposed skill by skill — recent activity separated from lifetime, and each dimension marked match, transfer, or gap:

Then the evidence — traced back to the roles it was earned in, most recent first. This is the part a screener would otherwise reconstruct by hand:

● used — core skill  ·  ○ used — transferable  ·  × not used at this role  ·  10 roles, 2004–2026

The strengths and the gaps are named just as plainly — five things carrying the fit, two things pulling against it, each with what to do about it:

And where it cannot be certain, it says so, and hands you the exact question to ask — instead of guessing:

That is the whole shape of it: a number with its worst case, the skills decomposed and dated, the evidence traced to real roles, the gaps named with remediation, and the one thing to confirm before anyone spends a screening slot. Decision-support — not the decision.

There's nothing to configure. Whichever side you're on, it's three moves to a Summary.

The honesty isn't a tone; it's a set of rules the engine is held to on every Summary.

The Summary is the front door. Behind it sits the rest of the toolkit.

The fastest way to feel the difference is to score one thing that matters: your own CV against a job you're actually considering, or one real candidate against a role you're actually hiring for. Each capability here can earn its own close-up later — the score and its range, the cited evidence, the telemetry, the recruiter pipeline — but the overview is best tested, not read.

Written from the live product — the verdict ladder, the score bands and the honesty promises are taken from what's shipped at fitscore.crosshire.ch, not a projection. — Crosshire.