Moonhub
Moonhub is an AI recruiting platform whose agents (Qualify AI, Engage AI, Monitor AI) search, score, outreach to, and engage candidates across more than a billion public and proprietary profiles, working alongside human expert recruiters who vet candidates before presenting them.
§ 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%18
+3.2
- 03
FRIA Support
Weight 15%25
+3.8
- 04
Data Governance Disclosure
Weight 15%40
+6.0
- 05
Human Oversight Design
Weight 12%48
+5.8
- 06
Post-Market Monitoring
Weight 12%22
+2.6
- 07
Customer Documentation
Weight 8%38
+3.0
§ 02 — Strongest · weakest
Strongest category
Data Governance Disclosure
Raw score 40 · contributes 6.0 to total.
Weakest category
Post-Market Monitoring
Raw score 22 · contributes 2.6 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
Moonhub is an AI recruiter / agentic sourcing platform founded in 2022 by ex-Meta engineer and Stanford PhD Nancy Xu, headquartered in Menlo Park, California. It combines proprietary AI agents that identify, score, and engage candidates across 1B+ profiles (LinkedIn, GitHub, Stack Overflow and proprietary data) with human 'talent partner' recruiters who vet candidates before presenting them to clients. The company raised roughly $14.4M from Khosla Ventures, GV, AIX Ventures, Day One Ventures and Salesforce. In June 2025 Salesforce acqui-hired part of the Moonhub team; per TechCrunch the standalone company is winding down (Salesforce itself disputes that this was a formal 'acquisition'), so the public product appears to be in a closing posture.
Regulatory exposure
Moonhub's product sits squarely in scope for high-risk AI hiring rules: as an agentic sourcing and screening tool it falls under the EU AI Act's high-risk employment use case, and its candidate scoring/screening behavior would be an automated employment decision tool under NYC Local Law 144 and similar Illinois/Colorado regimes. Public disclosure is thin: the data-security-and-privacy page commits to CCPA/GDPR alignment, a 'do not use list' opt-out, and a data-subject access process, but a dedicated AI policy is still marked 'Coming Soon' and the /ai-policy URL returns 404. No NYC LL 144 bias audit, no EU AI Act / FRIA deployer materials, no model cards, and no ISO 42001 / SOC 2 evidence are published. The standard terms push compliance with employment-discrimination laws onto the customer and disclaim all accuracy/fitness warranties.
Path to a higher score
The most consequential step would be publishing the promised AI policy with an explainability statement and model/system documentation, then commissioning and publicly posting an independent NYC LL 144 bias audit (auditor, date, downloadable summary). Adding EU AI Act deployer guidance / a FRIA template, a detailed data-governance disclosure (training-data sources, exclusion lists, protected-attribute handling) and recognized certifications (SOC 2, ISO 42001) would lift several categories. A public status/security-incident channel and a model-update changelog would address post-market monitoring. In practice, given the June 2025 Salesforce acqui-hire and apparent wind-down of the standalone product, it is unlikely Moonhub will invest further in this documentation as an independent vendor.
Conflicts of interest
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Casework has no commercial relationship with this vendor.