A Whizz Tech product · the hiring engine
Every CV read.
Every candidate
answered.
Hire hosts your careers page and publishes the job, then screens every application against your rubric — scores with verbatim CV quotes, a ranked top-K, and the interview kit for whoever you advance. $0.05 a CV versus your recruiter's hour.
50 trial credits · 50 CVs screened · wz_test_keys included
Scripted replay of a real screening · 1,204 CVs, condensed
Eight stages. Zero black box.
Every screening streams its stage telemetry over the API — GET /v1/runs/:id shows what the engine is doing and what it found. This is that view.
Intake — one pipeline, every source
Career page, embed, email-in, bulk upload — everything lands in one stack. Duplicates are merged by email before anything is counted twice, and every applicant gets an acknowledgment.
1,204 applications
career_page 861 · embed 204 · email_in 96 · upload 43
38 duplicates merged — same email, two CVs
11 knockout failures flagged — not auto-rejected
1,204 acknowledged · 1,204 portal links sent
Eight stages. Zero black box.
Every screening streams its stage telemetry over the API — GET /v1/runs/:id shows what the engine is doing and what it found.
01 · intake
Intake — one pipeline, every source
Career page, embed, email-in, bulk upload — everything lands in one stack. Duplicates are merged by email before anything is counted twice, and every applicant gets an acknowledgment.
cv_stack1,204 applications
career_page 861 · embed 204 · email_in 96 · upload 43
38 duplicates merged — same email, two CVs
11 knockout failures flagged — not auto-rejected
1,204 acknowledged · 1,204 portal links sent
02 · parse
Parse — pages read as images
CV pages are read visually, not through the text layer. Scanned files, two-column layouts, and Arabic PDFs — which text-layer parsers reliably corrupt — come out as clean structured extracts.
cv_extract.json — 2 of 1,204title Senior Backend Engineer · yrs 7
skills python · django · postgres · kafka
layout two-column PDF · lang en
مهندس برمجيات أول — ٨ سنوات خبرة · بايثون · قواعد بيانات موزعة
lang ar · parsed natively, not translated
03 · anonymize
Anonymize — names removed before scoring
Name, email, photo, address, and age markers are stripped before any scoring model runs. Measured result: name removal eliminates nearly all demographic bias. Prompting a model to “be fair” does not.
anonymized_extract — what the scorer seesname Rana Khalaf
email rana.k@gmail.com
address Al Barsha, Dubai
photo removed.jpg
experience kept · skills kept · education kept
names removed before scoring — the model grades work, not identity
04 · score
Score — your rubric, visible math
Each criterion scores 0–100, pointwise, three shuffled passes aggregated — because single-pass ranking is unstable exactly at the cutoff you care about. The overall is a weighted average. You set the weights.
criterion_scores — candidate #0447backend depthw884distributed systemsw992postgres in productionw778logistics domainw565communication en/arw488weighted overall 83.6 · 3 passes, order shuffled, aggregated
05 · evidence
Evidence — verbatim or it doesn't exist
Top candidates get quotes from their own CV per criterion, with page references. Every quote is validated against the extracted CV text — a quote that fails verbatim match is dropped, never displayed.
evidence.json — rank 1, distributed_systems"rebuilt order-routing from a Django monolith into 14 services handling 2.3M requests/day"
page 1 · match verbatim ✓
2 quotes failed the match this run → dropped, criterion flagged
0 fabricated quotes — every line cited or cut
06 · rank
Rank — re-weight without re-paying
Scores are stored per criterion, so moving a weight re-ranks the whole pool instantly — zero model calls, zero credits. The math stays on screen: change w9 to w5 and watch who moves.
ranked — weights edited, 0 LLM calls01 Rana Khalaf 91.7 strong
02 Omar El-Sayed 88.4 ↑2 after distributed w8→9
03 Lina Haddad 84.9 ↓1
04 Yusuf Rahman 82.2 ↓1
re-ranked in 41ms · rubric change written to the decision log
07 · topk
Top-K — a recommendation, not a verdict
You choose K. The engine flags its top K with the receipts attached — and that is where its authority ends. Advancing or rejecting is a human act in the dashboard, logged with who and when.
top_k — k = 5 of 1,204k = 5
5 gold-flagged · 12 borderline within 4 points — worth a human look
in_top_k is a recommendation field, nothing more
the AI cannot reject anyone. That button belongs to people.
08 · kit
Kit — the interview, pre-built
For any candidate you advance: a 30-minute structured interview in six question categories, with probes generated from the CV's actual claims — model answers, scoring rubrics, and red flags included.
interview_kit — rank 1 · 30 minplan 4′ warm-up · 8′ medium · 8′ hard · 6′ scenario · 4′ close
sections easy · medium · hard · scenario · cv_based · job_based
probe — CV says "led the migration"
→ scope? team size? what rolled back?18 questions · model answers + red flags · 12 credits
One script tag.
A careers page
that ranks.
Hosted page or embed on your own domain — either way it's the same pipeline: server-rendered JobPosting schema for Google, a public JSON API if you'd rather build the UI yourself, and an apply flow that acknowledges every candidate and hands them a live status portal.
And the job itself starts from almost nothing. Title, location, seniority, a few skills — the composer writes the full JD in English and Modern Standard Arabic, drafts the screening rubric, and attaches the knockouts. JD generation costs 0 credits, on every plan.
- — Hosted, embedded, or headless via JSON — one pipeline
- — JD in EN + AR — written, not translated
- — Acknowledgment + status portal for every applicant
<!-- One script tag. The careers page renders itself. --><script async src="https://jobs.whizztech.ai/embed.js" data-board="falak"></script><div id="whizz-jobs"></div><!-- Your styles, our pipeline. Every application submitted herelands in the same screening stack as your hosted page —acknowledged, portal-linked, ready to rank. -->
# you type
Senior Backend Engineer
Dubai — hybrid
senior · 5+ yrs
python · postgres · distributed
# you get
JD — full description, EN + AR (MSA)
rubric — 5 weighted criteria, editable
knockouts — work auth, notice period
schema — JobPosting JSON-LD attached
Thirty minutes,
planned to
the minute.
For any candidate you advance: a structured 30-minute interview across six question categories, each question with a model answer, a scoring rubric, and the red flags to listen for. Not a question bank — questions generated from this job's rubric and this candidate's CV.
The probes are the part nobody else ships: the kit reads the CV's claims and tells you where to press. 12 credits per kit — about a dollar. The nearest tools that interview from the CV start at five figures a year.
- — 6 categories: easy · medium · hard · scenario · cv_based · job_based
- —Probes grounded in the candidate's actual claims
- — Model answers + red flags on every question
4′
warm-up · easy
8′
medium
8′
hard + cv_based
6′
scenario
4′
job_based + close
question
Your CV says you rebuilt order-routing into 14 services at 2.3M requests/day. Which service should not have been split out?
model answer looks like
Has a real answer — anyone who did the work regrets at least one boundary. Specifics about data coupling are the tell.
red flag
Defends all 14. Nobody gets 14 boundaries right.
probe · grounded in the CV
says "led the migration" — probe scope, team size, rollback plan
Publish once. Appear everywhere it's honest to claim.
Job boards in 2026 are gatekept, and most ATS vendors paper over it with the word "multipost". We show per-board status instead — the same badges you see here are the ones in the product. If it says pending, it's pending.
Google for Jobs
liveJSON-LD + Indexing API
Adzuna
liveXML feed
Jooble
liveXML feed
Talent.com
liveXML feed
Jora
liveXML feed
Tanqeeb
liveMENA aggregator
LinkedIn Basic
pendingfeed approval in review
Indeed
in progressATS partner build underway
Bayt
onboardingpartnership in progress
Wuzzuf
onboardingpartnership in progress
Naukrigulf
onboardingpartnership in progress
Sponsored posts
your budget, passed through at cost
The MENA boards have no open APIs — we're building those partnerships one BD deal at a time, from Dubai. Incumbents can't shortcut that either.
Receipts for
regulators.
AI hiring tools are regulated now — NYC Local Law 144, Illinois' notice rules, GDPR Article 22, the EU AI Act's employment provisions. Most vendors treat that as a legal-page problem. We built it into the data model.
Designed to the shape of those regimes, documented plainly in our AI disclosure. Your obligations vary by jurisdiction — we give you the artifacts, not legal advice.
Decision log
Every screening, score, weight change, and human disposition is written to an append-only log with actor and timestamp. Four-year retention — the California FEHA number, applied everywhere.
Human-in-the-loop gate
The engine recommends a top-K; it cannot reject anyone. Rejection is a human action in the dashboard, and overriding the AI's recommendation is itself recorded as an audit signal.
Candidate disclosures
Jurisdiction-aware notice templates — posting notices, pre-use candidate disclosures — with a record of what was shown, to whom, and when.
Evidence-cited scores
No score ships without its receipts: criterion-level numbers, the weights, and verbatim CV quotes with page references. Explanation requests take minutes, not meetings.
Priced per CV,
not per seat.
One unit: 1 credit = 1 CV screened. An interview kit is 12. JDs are free. Seats are unlimited on every tier — the whole team reads the same evidence.
Starter
$49 /mo
300 CV screens · 10 interview kits
5 active jobs · unlimited seats
≈ $0.163 per CV screened
Growth
Most teams land here
$99 /mo
1,500 CV screens · 60 interview kits
15 active jobs · unlimited seats
≈ $0.066 per CV screened
Scale
$249 /mo
6,000 CV screens · 250 interview kits
50 active jobs · unlimited seats
≈ $0.042 per CV screened
Overage is a posted $0.05per CV. Workable charges $299 and meters your AI. We don't.
Full pricing + calculator→Stop reading CVs at midnight.
50 credits free — 50 CVs screened with evidence, no card. Post the job today; read a ranked shortlist tomorrow morning.