Manatal
Cloud-based applicant tracking system and recruitment CRM for agencies and HR teams, featuring an AI engine that scores and ranks candidates against job requirements and an asynchronous AI video Interviewer.
§ 01 — Score breakdown
§ Score breakdown
Category scoring
Weighted contribution shown to the right of each bar.
- 01
Article 11 Technical Documentation
Weight 20%33
+6.6
- 02
Bias Audit Transparency
Weight 18%22
+4.0
- 03
FRIA Support
Weight 15%33
+5.0
- 04
Data Governance Disclosure
Weight 15%52
+7.8
- 05
Human Oversight Design
Weight 12%60
+7.2
- 06
Post-Market Monitoring
Weight 12%43
+5.2
- 07
Customer Documentation
Weight 8%62
+5.0
§ 02 — Strongest · weakest
Strongest category
Customer Documentation
Raw score 62 · contributes 5.0 to total.
Weakest category
Bias Audit Transparency
Raw score 22 · contributes 4.0 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
Manatal (Manatal Co., Ltd.) is a Bangkok-based cloud recruitment platform combining an applicant tracking system and recruitment CRM, founded in 2019 by Jeremy Fichet and Yassine Belmamoun and backed through Sequoia's Surge accelerator. Its AI features include an AI Recommendation engine that reads job descriptions, extracts required/preferred criteria, and scores and ranks candidates with adjustable weighting and per-requirement match explanations, plus an asynchronous AI Interviewer that conducts and evaluates video screening interviews at scale. It is positioned as an affordable SMB-focused tool starting around US$15 per user per month, used by recruiting teams across 100+ countries.
Regulatory exposure
Manatal's candidate-ranking recommendation engine and AI Interviewer are automated tools that substantially assist hiring decisions, placing the product within the EU AI Act Annex III high-risk category and within scope of NYC Local Law 144 and Illinois' AI video interview law when deployed by covered employers. Manatal offers a credible security and data-privacy posture (SOC 2 Type II, GDPR/CCPA/PDPA tooling, a DPA with Standard Contractual Clauses and sub-processor transparency) and unusually strong in-product human oversight, but it publishes no bias audit, no AI system/model documentation, and no EU AI Act or FRIA deployer guidance, so deployers must shoulder the AEDT and high-risk-provider obligations largely on their own.
Path to a higher score
The highest-leverage moves are to commission and publicly post an independent NYC LL 144-style bias audit of the recommendation and Interviewer models, and to publish an AI system/model card and explainability statement (ideally under an ISO/IEC 42001 management system) that documents training data, intended use, and limitations. Adding EU AI Act deployer guidance — a FRIA template and a clear allocation of provider vs. deployer obligations — would lift the currently near-zero FRIA and Article 11 dimensions. Disclosing training-data sources and exclusions for its own AI, and adding a model-update changelog alongside its existing status page, would further strengthen data governance and post-market monitoring.
Conflicts of interest
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