Entelo
Entelo is an AI-powered talent sourcing and diversity-recruiting platform that aggregates hundreds of millions of public candidate profiles and applies predictive machine-learning models to score, rank, and surface (including underrepresented) candidates for recruiters.
§ 01 — Score breakdown
§ Score breakdown
Category scoring
Weighted contribution shown to the right of each bar.
- 01
Article 11 Technical Documentation
Weight 20%30
+6.0
- 02
Bias Audit Transparency
Weight 18%20
+3.6
- 03
FRIA Support
Weight 15%25
+3.8
- 04
Data Governance Disclosure
Weight 15%45
+6.8
- 05
Human Oversight Design
Weight 12%42
+5.0
- 06
Post-Market Monitoring
Weight 12%35
+4.2
- 07
Customer Documentation
Weight 8%40
+3.2
§ 02 — Strongest · weakest
Strongest category
Data Governance Disclosure
Raw score 45 · contributes 6.8 to total.
Weakest category
Customer Documentation
Raw score 40 · contributes 3.2 to total.
§ 03 — Cited evidence
§ Evidence
Cited per category
Every score is backed by at least one cited piece of evidence.
§ 04 — Editorial notes
Company overview
Entelo, founded in 2011 by Jon Bischke and John McGrath and headquartered in San Francisco, is an AI talent-sourcing and diversity-recruiting platform that mines public web and social profiles, applies predictive 'recruitability' scoring, and offers diversity filters (gender, race, ethnicity, veteran status) plus an 'Unbiased Sourcing Mode' that anonymizes bias-prone profile attributes. Its assets were acquired by SilkRoad Technology in 2022 and rebranded under Rival; the entelo.com domain now redirects to rival-hr.com and the product is marketed as Rival Recruit with a 700M+ passive-candidate database. Pricing is undisclosed but positioned mid-market/enterprise for talent-acquisition teams.
Regulatory exposure
Entelo/Rival sits squarely in the EU AI Act high-risk band: AI candidate scoring and ranking for recruitment is Annex III high-risk, and its predictive 'likelihood to move' and demographic diversity filtering raise both bias and data-provenance questions under NYC LL 144, Illinois, and Colorado SB 205. Because the tool ranks and scores candidates, it can meet the AEDT definition under NYC LL 144, yet no public bias-audit summary exists (Entelo/Rival does not appear in the ACLU's LL 144 tracker). The diversity product explicitly infers race/ethnicity/gender, heightening data-governance and disparate-impact scrutiny, while sourcing from aggregated public profiles raises GDPR lawful-basis concerns for EU candidates.
Path to a higher score
Entelo/Rival could materially raise its score by commissioning and publicly posting an independent NYC LL 144 bias audit (BABL AI, Holistic AI, Warden AI, etc.) with auditor and date, and publishing a model/system card or AI policy describing how its scoring and unbiased-mode features work. Concrete wins: an explicit deployer-facing compliance pack (LL 144 notice templates, EU AI Act Article 27 FRIA guidance, DPAs), a documented data-exclusion and training-data statement, ISO 42001 certification, and in-product explainability plus documented human-override/audit-log controls. Surfacing a security/AI-incident contact and an AI changelog would lift post-market-monitoring evidence beyond the current generic status page.
Conflicts of interest
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Casework has no commercial relationship with this vendor.