Scovai Scovai

Deterministic by design.

Every other AI hiring tool gives you a score from a model you can't inspect. Scovai is built so the AI never decides the number: it only prepares the evidence.

How scoring actually works.

AI reads and structures

CVs, assessments, interview transcripts become clean, structured signals. The model's job ends here.

A formula scores

Every competency, match and rank is a transparent, weighted calculation. Same inputs → same output, every time.

You see the full breakdown

Which signals fed the score, at what weight, at what confidence level. No black box.

Humans decide

The platform recommends. People hold the decision. Every time.

Raw signals in

CVs, assessments, interview transcripts

AI structures

Models read and normalise, no scoring here

Formula scores

Weighted, deterministic. Same in → same out

Re-rank + confidence

Ordered list, every score flagged by certainty

Auditable output

Full breakdown, traceable to every input

Deterministic Explainable Human decides

The two-stage re-ranking pipeline: AI reads, formula ranks, humans decide

Why it matters.

Explainable

Not "explainable-ish." Every number traces back to its inputs in one click.

Auditable

Reproducible results, full action log. If someone asks why, you have an answer.

Fair

Demographic signals excluded from scoring by design. Fairness tracked, not assumed.

Defensible

When a candidate, an auditor, or a regulator asks "why this person?", you have a real, structured answer.

S.C.O.V.A.I.: the method, not a made-up word.

Semantic Career Orchestration & Validation through Augmented Intelligence.

S
Semantic

We read meaning, not keywords. A CV in any supported language becomes structured, comparable signal.

C
Career Orchestration

We don't stop at the hire. We carry the same data from application to onboarding to development.

V
Validation

Every claim gets verified: skills tested, identity confirmed, traits measured. Nothing taken on faith.

AI
Augmented Intelligence

The operative word is augmented. The AI does the heavy reading. The human makes the decision. Always.

Semantic embedding space

Every CV and every role is embedded as a vector of meaning, not keywords. The closer a candidate sits to the role in this space, the stronger the semantic fit, across any language.

The open role
SR Strong match: near in vector space
JD Weaker fit: further away

Semantic matching: candidates and roles in the same embedding space

The engine behind the extraction runs on Cortex — private, governed infrastructure built for regulated environments.

See how

The algorithm is inspectable. The result is the same every time.