Loxo
Loxo is an AI-native talent-intelligence platform that combines candidate sourcing, a recruiting CRM, and an applicant tracking system, using proprietary ranking models and a self-built talent graph of 800M+ people to source, rank, and match candidates for recruiting and staffing firms.
§ 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%30
+4.5
- 04
Data Governance Disclosure
Weight 15%50
+7.5
- 05
Human Oversight Design
Weight 12%50
+6.0
- 06
Post-Market Monitoring
Weight 12%40
+4.8
- 07
Customer Documentation
Weight 8%58
+4.6
§ 02 — Strongest · weakest
Strongest category
Customer Documentation
Raw score 58 · contributes 4.6 to total.
Weakest category
Bias Audit Transparency
Raw score 20 · contributes 3.6 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
Loxo is a Denver, Colorado-based talent-intelligence company founded in 2012 by Matt Chambers and Ilia Cheishvili. Its platform unifies AI sourcing, a recruiting CRM, an ATS, and outreach into a single system of record, powered by proprietary deep-learning ranking models (marketed as a seventh-generation algorithm) and a self-built talent graph of 800M+ people (cited up to ~1.2B on the AI product page). Loxo says it serves 13,000+ executive-search, RPO, professional-recruiting and staffing teams and has raised roughly $115M led by Tritium Partners.
Regulatory exposure
Loxo's AI ranks and shortlists candidates, placing it squarely within EU AI Act Annex III high-risk hiring use and within NYC Local Law 144's definition of an automated employment decision tool. Despite this, Loxo publishes no bias audit, no model or technical documentation, and no EU AI Act or LL 144 deployer guidance; instead its AI product page makes an unsubstantiated marketing claim that the algorithm 'eliminates the potential for human bias.' Loxo's public compliance surface is data-protection oriented (SOC 2 Type II, GDPR/CCPA tooling, a DPA with Standard Contractual Clauses, a subprocessor list) rather than AI-governance oriented, leaving a material gap between its regulatory exposure and its published evidence.
Path to a higher score
Loxo could raise its score by commissioning and publicly posting an independent NYC LL 144 bias audit and repeating it annually, releasing model/system cards and an explainability statement for its ranking models, adding EU AI Act Article 27 (FRIA) and LL 144 deployer guidance, and disclosing what candidate data is and is not used to train its models (an exclusion list). Standing up a public status page and a formal vulnerability-disclosure channel would strengthen post-market monitoring, and pursuing ISO/IEC 42001 would consolidate these into a recognized AI-management framework.
Conflicts of interest
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