XOR
XOR is a text-first conversational-AI recruiting platform that pre-screens and scores high-volume and hourly candidates with recruiter-defined knockout/scoring questions over SMS, WhatsApp, and Messenger, then auto-schedules interviews on the recruiter's calendar.
§ 01 — Score breakdown
§ Score breakdown
Category scoring
Weighted contribution shown to the right of each bar.
- 01
Article 11 Technical Documentation
Weight 20%20
+4.0
- 02
Bias Audit Transparency
Weight 18%18
+3.2
- 03
FRIA Support
Weight 15%25
+3.8
- 04
Data Governance Disclosure
Weight 15%42
+6.3
- 05
Human Oversight Design
Weight 12%40
+4.8
- 06
Post-Market Monitoring
Weight 12%33
+4.0
- 07
Customer Documentation
Weight 8%48
+3.8
§ 02 — Strongest · weakest
Strongest category
Customer Documentation
Raw score 48 · contributes 3.8 to total.
Weakest category
Bias Audit Transparency
Raw score 18 · contributes 3.2 to total.
§ 03 — Cited evidence
Download diligence record→§ Evidence
Cited per category
Every score is backed by at least one cited piece of evidence.
§ 04 — Editorial notes
Company overview
XOR (XOR Inc.) is a US recruiting-automation vendor founded in 2016 by Aida Fazylova and Nikolay Manolov and headquartered in San Jose, California. Its XORbot chatbot engages applicants over SMS, WhatsApp, and Messenger to pre-screen, score, and self-schedule them, and is aimed squarely at high-volume hourly and blue-collar hiring in sectors such as healthcare, retail, and staffing. Recruiters configure the screening questions and assign scores so candidates 'qualify themselves,' and the platform integrates with 20+ ATS/HRIS systems including Bullhorn, Greenhouse, Lever, and SAP SuccessFactors while marketing a low cost-per-hire, text-first hiring funnel.
Regulatory exposure
XOR's chatbot scores and filters applicants against recruiter-defined thresholds, which makes it an Automated Employment Decision Tool under NYC Local Law 144 and a high-risk employment system under the EU AI Act. Despite this, XOR publishes no bias audit, no model/system card, no AI policy or explainability statement, and no NYC- or EU-specific deployer guidance. Its footer displays self-asserted ISO 27001, Veracode, and GDPR badges, and the privacy policy names sub-processors (Elucru Inc., Microsoft Azure, Twilio) and Standard Contractual Clauses, but there is no ISO 42001, SOC 2, dedicated security/trust page (xor.ai/security returns 404), or Fundamental Rights Impact Assessment material. Deployers therefore carry direct LL 144 and EU AI Act obligations with very little vendor-supplied evidence to rely on.
Path to a higher score
XOR could raise its score fastest by commissioning and publicly posting an independent NYC LL 144 bias audit (e.g., BABL AI, Warden AI, or Holistic AI) and publishing an AI/system model card with an explainability statement describing how scores are produced. Standing up a dedicated security/trust page (SOC 2 or ISO 42001 plus a training-data / data-exclusion statement), documenting concrete human-in-the-loop, override, and audit-log controls, adding EU AI Act deployer guidance and a FRIA template, and opening a public status/changelog and security-disclosure channel would move XOR from a marketing-only posture to defensible, citable compliance documentation.
Conflicts of interest
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