Manatal
HireAIScore rates Manatal 41 out of 100 (grade F, Substantial gaps) under rubric v1.0, last reviewed July 17, 2026. Manatal ranks 63 of 77 vendors rated in ATS-integrated AI, against a category average of 47. That score is drawn from 20 evidence items across 7 rubric criteria for Manatal — 15 with a cited source, 5 recording that nothing was located.
Score breakdown
Manatal, criterion by criterion
Each rubric criterion is scored out of 100 on the cited evidence, then weighted into the total.
Weighted contribution shown to the right of each bar.
Article 11 Technical Documentation
Weight 20% · category avg 51
33
+6.6
Bias Audit Transparency
Weight 18% · category avg 41
22
+4.0
FRIA Support
Weight 15% · category avg 32
33
+5.0
Data Governance Disclosure
Weight 15% · category avg 54
52
+7.8
Human Oversight Design
Weight 12% · category avg 57
60
+7.2
Post-Market Monitoring
Weight 12% · category avg 42
43
+5.2
Customer Documentation
Weight 8% · category avg 59
62
+5.0
Total weighted score
40.6(rounded to 41 for display)
Category avg is the mean raw score on that criterion across the 77 ATS-integrated AI vendors in scope of this rubric, this one included.
Strongest criterion
Customer Documentation
62raw / 100
Contributes 5.0 to the total. 62 against a 59 category average.
Weakest criterion
Bias Audit Transparency
22raw / 100
Contributes 4.0 to the total. 22 against a 41 category average.
Cited evidence
The evidence behind the score
Every score is backed by at least one cited piece of evidence. Open a criterion to see what was found, when, and where.
Download diligence recordEvidence ledger
- Items
- 20
- Documentation
- 12
- Public statement
- 3
- Absence
- 5
- With a source URL
- 15 of 20
- Source hostnames
- 3
- Fewest items
- 2
- Article 11 Technical Documentation
These figures measure how thoroughly Manatal was reviewed, not how Manatal performed — an absence row, recording that nothing was located, is counted like any other item.
Article 11 Technical Documentation
2 items33
- DocumentationCaptured Jul 17, 2026
Help-center documentation explains the AI recommendation mechanism (requirement extraction, weighting, match percentage) and provides line-by-line justifications for why each candidate matches or misses criteria.
- AbsenceCaptured Jul 17, 2026
No model/system card, explainability statement, AI policy, technical documentation pack, or ISO 42001 certification is published on Manatal's security, compliance, or support pages.
Bias Audit Transparency
2 items22
- AbsenceCaptured Jul 17, 2026
Searches of Manatal's security page, compliance feature page, and blog found no bias audit, independent algorithmic audit, or disparate-impact testing results for its AI candidate scoring or AI Interviewer.
- Public statementCaptured Jul 17, 2026
Manatal's own compliance blog states NYC Local Law 144 requires bias audits for automated employment decision tools, yet Manatal publishes no such audit of its own tools.
FRIA Support
3 items33
- AbsenceCaptured Jul 17, 2026
No EU AI Act Article 27 Fundamental Rights Impact Assessment template, deployer checklist, or high-risk provider/deployer obligation guidance found on Manatal's site.
- DocumentationCaptured Jul 17, 2026
A Data Processing Addendum with Standard Contractual Clauses, security exhibit, and sub-processor transparency provides a partial deployer/trust package but contains no AI-Act or FRIA provisions.
- Public statementCaptured Jul 17, 2026
A general recruiting-compliance article gives deployers high-level AI-hiring-law awareness (NYC LL 144, Illinois consent) but no EU AI Act or FRIA-specific guidance.
Data Governance Disclosure
4 items52
- DocumentationCaptured Jul 17, 2026
Security page details SOC 2 Type II certification, encryption, role-based access controls, US data storage, logged/audited access, and daily backups.
- DocumentationCaptured Jul 17, 2026
DPA specifies AES-256 at rest, TLS 1.2+ in transit, EU Standard Contractual Clauses for transfers, and a maintained sub-processor list customers can be notified of and object to.
- DocumentationCaptured Jul 17, 2026
Privacy policy states Manatal does not use Google Workspace API data for developing or training generalized AI/ML models (a narrow, API-specific exclusion).
- AbsenceCaptured Jul 17, 2026
No disclosure of the training-data sources, exclusion lists, or provenance for Manatal's own AI recommendation and Interviewer models, and no ISO 42001.
Human Oversight Design
2 items60
- DocumentationCaptured Jul 17, 2026
Support docs explicitly state recruiters can override the AI ('Absolutely'), edit criteria and weights, and make final pipeline decisions, with per-requirement explanations for each candidate.
- DocumentationCaptured Jul 17, 2026
Product page describes assignable per-criterion weights and a detailed per-requirement breakdown showing where each candidate fits or misses, supporting recruiter review.
Post-Market Monitoring
3 items43
- Public statementCaptured Jul 17, 2026
Public status page tracks 19+ components with 90-day uptime, documented incident postmortems, and email/Slack/RSS subscription for updates.
- DocumentationCaptured Jul 17, 2026
Security page cites SOC 2 Type II with regular third-party audit, logged and audited access, and an incident-response process updated after each incident.
- AbsenceCaptured Jul 17, 2026
No public model-update changelog, AI performance/drift dashboard, or continuous bias-monitoring surface for the AI models.
Customer Documentation
4 items62
- DocumentationCaptured Jul 17, 2026
Compliance feature page documents GDPR/CCPA/PDPA support, data subject rights, and SOC 2 Type II third-party audits.
- DocumentationCaptured Jul 17, 2026
Deployer-facing compliance guide covering anti-discrimination law, NYC Local Law 144 bias-audit requirements, and Illinois AI video interview notice/consent.
- DocumentationCaptured Jul 17, 2026
Detailed help-center documentation of the AI recommendation feature, scoring, weighting, explanations, and override options.
- DocumentationCaptured Jul 17, 2026
Public product page describing the AI recommendation engine's matching, scoring, weighting, and per-requirement explainability.
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.
Regulatory frame
What applies to ATS-integrated AI.
Ranking features are inside EU AI Act Annex III §4 (high-risk) and inside NYC Local Law 144 when they affect NYC-based candidates. Customers should treat the AI surface as a distinct deployer-FRIA scope from the rest of the ATS.
Compare
Manatal against its nearest-scoring peers.
The closest scores in ATS-integrated AI, side by side.
- Side-by-sideManatal vs Cezanne Recruitment41F
- Side-by-sideManatal vs Harri41F
- Side-by-sideManatal vs Recruitly41F
- Side-by-sideManatal vs Radancy41F
Ranking
Others rated in ATS-integrated AI
Ranked 63 of 77 by weighted total under rubric v1.0. The ordering is arithmetic on the rubric and carries no view on which tool suits a given hiring process.
- #60Harri41F
- #61Recruitly41F
- #62Radancy41F
- #63ManatalThis profile41F
- #64Recruit CRM40F
- #65Personio40F
Conflicts of interest
No vendor pays for placement, scoring, or removal. Casework - the consulting firm that operates this directory - provides paid services to some vendors. Any active or recent (within 24 months) commercial relationship is disclosed on the affected vendor profile and the review is reassigned to an independent reviewer. See the full policy on About.
Casework has no commercial relationship with this vendor.