Hire the Truth.
We verify technical, functional and domain competencies and accomplishments of a candidate, before your first conversation.
You are hiring on a hunch and unverified evidences.
Resumes
are no longer self-authored. A tailored résumé takes 60 seconds to generate. 47% of candidates admit to embellishing qualifications. AI has made this effortless and scalable.
Interviews
score rehearsal, not performance. Candidates now receive real-time AI coaching during video interviews. The interview is no longer measuring the candidate — it measures how well they prepared to be measured.
References
are selection bias, not a check. Candidate-selected. Conducted after weeks of company investment. Rarely candid. The process hasn't changed. The risk underneath it has.
You cannot answer "is this true?" from a résumé, a screen, or a reference the candidate selected. Elev8 answers it — before the interview, not during it.
How It Works
A pre-interview trust platform — built on what candidates actually delivered.
Verification Engine
A candidate documents a specific accomplishment — what they delivered, at which company, in which date range. Two peers confirm it. A leader confirms it. Independently. Employment overlap and company membership are authenticated before any confirmation counts.
AI can generate a résumé. It cannot generate those human confirmations from real professional relationships.
Matching Engine
Five-predictor scoring — Role Fit, Delivery, Reputation, Career Path, and Learning — run against your specific job requirements. Weights shift by career level. Every score opens into its evidence.
Skills are roughly 20% of whether a hire succeeds. The other 80% — how someone delivers, learns, and works with people — is what we score. Every number traces back to the people and outcomes that produced it.
What You See
A ranked shortlist — with the evidence behind every score.
Three profiles. Each one scored before your first recruiter hour is spent.
Maya Chen
Staff Software Engineer
6 years · Fintech
Verified Profiles
Applying for
Principal Engineer — Platform Infrastructure
5-Predictor Score
Verified
Claimed
Role Fit
Strong Fit
Delivery
Demonstrated
Reputation
Endorsed
Career Path
Growing
Learning
Well-Rounded
score360
84
/100
Org-wide delivery scope · 4 verified accomplishments
Why You Can Trust What You See
The evidence is built to hold up — structurally, not by policy.
Verified vs. Claimed
The line no other platform draws.
Every signal is labeled. A candidate who submits only a résumé is still scored — but you see exactly how much of their score is verified by real colleagues and how much is self-reported. The distinction is always visible. Two candidates with the same score are not the same candidate.
Honest Reviews
Anonymous. Authenticated. From people who were there.
Peer and leader reviews each contribute independently to a candidate's full picture. No single reviewer shapes the outcome — the score draws from multiple authenticated voices, each evaluated separately. That independence is what keeps any one biased perspective from tipping the scale.
Full Audit Trail
Every score traces to its source.
Behind every predictor sits the exact accomplishment and the exact people who confirmed it. Defensible to any hiring committee, legal team, or external auditor. Not a black-box number you're asked to trust.
No Prestige Bias
Scored on what they did. Not where they did it.
Employer name, school, and job title are not inputs to any score. Whether a candidate built their career at a household brand or an unknown firm, their record is evaluated solely on what they delivered and who confirmed it.
Regulatory Compliance
Built for the laws governing AI in hiring — from the ground up.
As AI hiring tools face increasing regulatory scrutiny, Elev8 is designed to meet — not retrofit for — the standards enterprise procurement now requires.
NYC · Local Law 144
Automated Employment Decision Tool Act
Requires independent bias auditing, public disclosure of results, and candidate notice before any AI scoring tool is used for NYC-based roles. Our bias audit documentation is complete, synthetic matched-pair tests pass across all protected-class vectors, and source code is available for independent auditor review.
What we built — not retrofitted
No protected-class signals in any scoring model
Employment gaps carry zero weight on career scoring
Prestige inference removed — employer name and school are not scoring inputs
LLM sentiment proactively excluded — known to introduce national-origin bias in cross-cultural review text
Anonymous reviews — no demographic data enters the scoring pipeline
Every verification requires authenticated professional relationships — employment overlap confirmed before any confirmation counts