Most AI hiring tools hand you a number. This piece shows the machinery behind ours — the Fit Score — end to end, with a worked example, because a score you can't reconstruct is a score you can't trust. (That principle has its own manifesto; this is the engineering-room tour.)
A Fit Score is a 0–100% score that blends a candidate's AI CV evaluation and AI interview results into a single ranking number, weighted the way the hiring team chooses, and traceable at every step back to written, evidence-citing justifications.
The pipeline, in four stages
Stage 1 — evidence becomes criterion scores. Two engines evaluate the candidate independently against the same role rubric: one reads the CV, one conducts and scores the async interview. Each produces a 0–10 score per criterion, and every score carries a written justification citing what was found. Nothing downstream exists except as an aggregation of these.
Stage 2 — weight tiers shape each engine's rollup. Rubric criteria carry tiers — Must Have, Very Important, Important, Good to Have — so a 4/10 on a Must-Have drags the rollup far more than a 4/10 on a nice-to-have. Each engine's criterion scores combine through those tiers into a weighted 0–10: a CV score and an interview score.
Stage 3 — the project's blend combines the engines. Every hiring project sets how much the CV and the interview each count — 50/50 by default, but a role where the interview is the real signal might run 30/70. The system normalizes whatever you set to a total of 100%.
Stage 4 — the blend becomes the Fit Score. The weighted combination lands on 0–100%, displayed with its full decomposition. One deliberate consequence: because the blend is project-scoped, the same candidate assessment can produce different Fit Scores on different roles. Fit is relative to the role — the score is honest about that.
The worked example
Meet Sarah Collins (a fictional candidate from our demo data), applying to an agentic-AI developer role with a six-criterion rubric.
| Criterion | Tier | Interview score |
|---|---|---|
| Proficiency in AI development | Must Have | 10/10 |
| Programming skills | Must Have | 10/10 |
| Understanding of ML algorithms | Very Important | 9/10 |
| Experience with AI ethics & safety | Important | 8/10 |
| Research & continuous learning | Important | 9/10 |
| Collaboration & communication | Good to Have | 7/10 |
Notice the shape: perfect on both Must-Haves, softer on a Good-to-Have. The tier weighting rolls this up to an interview score of 9.2/10 — the 7 costs her little because the rubric says collaboration is a tiebreaker for this role, not a pillar. A rubric with Collaboration at Must-Have would punish the same answers hard. Same evidence, different role definition, different score — that's the rubric doing its job, visibly.
Her CV evaluation, run against the same criteria by the CV engine, lands at 9.2/10 with its own justifications. The project blends CV and interview 50/50: each contributes 46 of its possible 50 points, and Sarah's Fit Score is 92% — ranked first in the pipeline, with every layer inspectable underneath: click the 92%, see the components; click a component, see the six criteria; click a criterion, read the justification; click through, and the transcript itself is there.
The rule that makes the number trustworthy
One scenario reveals more about a scoring system than any accuracy claim: what happens when evidence is missing? Suppose a candidate's CV never touches one criterion. Three designs are possible: score it 0 (punishes the candidate for the document, not the skill), guess from context (fabrication with confidence), or say "Cannot evaluate" — score nothing, exclude the criterion from the weighted math, and show the gap in the report.
Rubrily does the third, everywhere, without exception. A missing answer is treated as missing, not as bad. This is why we say the product never fabricates a score: every number in a Fit Score exists because the AI found evidence and wrote down what it was.
How is a Fit Score different from a "match score"?
A match score is typically a single opaque percentage from unstated criteria — you can't see its parts, so you can't contest them. A Fit Score is a stated aggregation: your criteria, your tiers, your blend, per-criterion justifications, and full decomposition on demand. The difference isn't the number; it's whether the number can be interrogated.
What this means for how you rank
Because the Fit Score is explainable, ranking by it is a starting posture, not an abdication: sort the pipeline by Fit, then read the justifications behind any decision that matters. Two candidates at 84% and 82% are a coin toss until you read why — one may be weak on a criterion you privately know matters more than the rubric admits (which is a signal to fix the rubric, not to trust vibes). The score compresses the evidence; the report preserves it. Use both.
FAQ
What is a Fit Score? A Fit Score is a single 0–100% score blending a candidate's CV evaluation and AI-interview results, weighted per project. It's fully traceable: components, per-criterion 0–10 scores, written justifications, and the underlying transcript and CV are all inspectable behind the number.
Can I change how the Fit Score is weighted? Yes, per project: each component (CV eval, interview) has a weight you control, normalized to 100%. Criterion-level influence comes from the rubric's four weight tiers. Because weighting is project-scoped, the same assessment can legitimately produce different Fit Scores on different roles.
What are the exact tier multipliers? The tier mechanics (Must Have > Very Important > Important > Good to Have) are as described; the precise internal constants are implementation detail we don't publish — what we guarantee is the property that matters: every rollup decomposes back into visible per-criterion scores and justifications, so any candidate's number can be reconstructed and audited from the report.
Does a low Fit Score reject a candidate automatically? No. Nothing in Rubrily auto-rejects anyone. Scores rank the pipeline; humans read reports, add reviews, and decide. The pipeline makes the ranked shortlist actionable — it doesn't act on your behalf.
See a Fit Score decompose in your own pipeline — define a rubric, screen your applicants, click through every number. Start free →