Humanly
Conversational AI for high-volume recruiting that engages, screens, and schedules candidates across chat, SMS, email, voice, and video, with structured AI interviews and automated scoring.
§ 01 — Score breakdown
§ Score breakdown
Category scoring
Weighted contribution shown to the right of each bar.
- 01
Article 11 Technical Documentation
Weight 20%45
+9.0
- 02
Bias Audit Transparency
Weight 18%40
+7.2
- 03
FRIA Support
Weight 15%35
+5.3
- 04
Data Governance Disclosure
Weight 15%40
+6.0
- 05
Human Oversight Design
Weight 12%58
+7.0
- 06
Post-Market Monitoring
Weight 12%30
+3.6
- 07
Customer Documentation
Weight 8%52
+4.2
§ 02 — Strongest · weakest
Strongest category
Article 11 Technical Documentation
Raw score 45 · contributes 9.0 to total.
Weakest category
Post-Market Monitoring
Raw score 30 · contributes 3.6 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
Humanly (legal entity HumanlyHR Inc.) is a Seattle-based conversational-AI recruiting vendor founded in 2018 and a 2019 Y Combinator graduate. Its platform automates candidate engagement, screening, interview scheduling, reference checks, and structured AI interviews across chat, SMS, email, voice, and video for high-volume hiring teams. In October 2025 it acquired Sprockets, Qualifi, and HourWork to extend into sourcing, voice screening, and post-hire retention, and reports roughly 250,000 candidate interviews per month.
Regulatory exposure
Humanly sits in the high-risk zone of employment AI: it screens candidates against knockout criteria, applies "consistent scoring models," and surfaces scored candidate snapshots used to advance or filter applicants — functionality that maps to an AEDT under NYC LL 144, a high-risk system under EU AI Act Annex III, and a "consequential decision" tool under Colorado SB-205 and Illinois law. Its compliance commitment is mediated through a third-party governance vendor (FairNow, named publicly in a September 2024 release), but Humanly itself publishes no bias-audit report, no LL 144 audit summary, and no per-jurisdiction deployer guidance. A notable disclosed practice: GeekWire reports it trains candidate-scoring models on millions of chat and video transcripts — a use its own privacy policy does not address.
Path to a higher score
The highest-credibility wins are: publishing an independent NYC LL 144 bias-audit summary with auditor name, date, and impact-ratio tables on a stable, ungated URL; un-gating the "Ethical AI Framework" whitepaper and publishing an instructions-for-use / model-card style document; standing up a real trust/security page with any SOC 2 or ISO certifications (the previously referenced /data-security page now 404s); and adding explicit deployer-facing guidance for LL 144 notices, EU AI Act Article 27 FRIA support, and Colorado SB-205.
Conflicts of interest
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