For talent teams

Applications, read by meaning not keywords.

Rolerise catches AI-fake résumés and bot floods, returns the good candidates your filters wrongly reject, and searches everyone you've ever screened in plain language. Every call comes with a reason.

Live queue — Senior Backend Engineer
1,204
processed
37
rescued
6
flagged
92
matched by meaning
03 · Meaning over keywords
MATCHED
"Designed the payments platform"
Read as systems-design experience — the required skill was never a keyword.
Evidence: matched by meaning, not a keyword hit
05 · Reasoned record
PASSED
J. Park — career gap, 14 months
Gap is not treated as a disqualifier.
Evidence: skills verified before & after the gap
04 · Rescue
RESCUED
M. Alvarez — 8 yrs backend, ex-Stripe
Auto-rejected for "not local" — role is fully remote-eligible.
Fix: role is remote — returned to shortlist
00 · Integrity
FLAGGED
14 applications, one source
Matching geolocation, employer and timing pattern.
Signal: same IP, 14 apps in 90 minutes
72%
of recruiters have seen AI-fabricated applications — Skillfuel
88%
of employers admit their systems reject qualified candidates — Harvard Business School / Accenture
1 in 4
candidate profiles will be fake by 2028 — Gartner
82%
of candidates want an explanation when they're rejected — GCheck

Screening broke in both directions at once

The market bought filters. Filters made hiring worse. The same first pass now lets fabrication through — while cutting strong candidates for formatting it can't parse.

What gets through
Employers and degrees with no verifiable trace
Technologies listed before they existed
Seniority claimed on a fraction of the required tenure
Keyword blocks hidden in white text
What gets lost
A location that reads as on-site when the role is remote
A skill that's in the job description but never a keyword
A non-standard title that erases real seniority
A career gap treated as grounds for rejection

Six passes before a decision is final

Every application clears every pass before your team ever sees a verdict — and every verdict carries the reasoning behind it.

00 · Integrity
Broken files, hidden white text, stuffed keywords and duplicates — caught before the model ever runs.
01 · Structural truth
Overlapping dates, roles ending before they start, seniority the timeline can't support. Arithmetic, not opinion.
02 · External reality
Does the employer exist? Does the school? Does company size match the seniority claimed?
03 · Meaning over keywords
"Designed the payments platform" reads as systems design — a skill is stored with its proof.
04 · Rescue
The reverse question, asked of everyone: is this person strong, and are they being lost to a formality?
05 · Reasoned record
A verdict with a reason, the evidence, what would have changed it, and a decision log for every person involved.
The other half of screening

Rescue runs the check most filters skip.

A location field misread as on-site. A title that doesn't map to a level. A gap treated as a red flag. These aren't rare edge cases — they're systematic, and they look identical to a weak candidate until someone checks.

Rescue checks: it re-reads every rejection for a parsing error, re-evaluates skills by meaning instead of keywords, and re-scores anyone who matches despite a formal gap. Nothing here lowers the bar — what's genuinely missing stays missing. It fights false rejections, not screening itself.

27M
"hidden workers" in the US alone, qualified and screened out — Harvard Business School / Accenture
3
what Rescue checks on every candidate: a parsing error, a skill read by meaning, a match despite a formal gap

Questions worth answering upfront

Does this replace our ATS?

No. Rolerise is a reading layer that sits before your team decides — it works alongside the system you already have.

Will it auto-reject anyone?

No. Every flag is a signal with a reason attached. A person makes the final call, always.

How is this different from a bot blocker like Cloudflare Turnstile?

Those stop traffic at the door. Rolerise also fixes what happens after — catching fraud and returning candidates wrongly rejected, in the same pass.

What about compliance and explainability?

Every decision is logged with a reason and the evidence behind it — built for markets where explainability is now a condition of the sale, not an afterthought.