Metaview

Metaview is an AI recruiting platform whose agents transcribe and summarize interviews into structured notes and scorecards, and whose Application Review feature screens and ranks inbound applicants against an auto-generated Ideal Candidate Profile with fit ratings.
ScreeningAssessmentATS-integrated

HireAIScore rates Metaview 54 out of 100 (grade F, Substantial gaps) under rubric v1.0, last reviewed June 7, 2026. Metaview ranks 29 of 160 vendors rated in Screening AI, against a category average of 46. That score is drawn from 16 evidence items across 7 rubric criteria for Metaview — 13 with a cited source, 3 recording that nothing was located.

Score breakdown

Metaview, 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.

  1. Article 11 Technical Documentation

    Weight 20% · avg 49

    38

    +7.6

  2. Bias Audit Transparency

    Weight 18% · avg 41

    80

    +14.4

  3. FRIA Support

    Weight 15% · avg 31

    33

    +5.0

  4. Data Governance Disclosure

    Weight 15% · avg 53

    60

    +9.0

  5. Human Oversight Design

    Weight 12% · avg 56

    60

    +7.2

  6. Post-Market Monitoring

    Weight 12% · avg 40

    48

    +5.8

  7. Customer Documentation

    Weight 8% · avg 58

    62

    +5.0

Total weighted score

53.9(rounded to 54 for display)

Category avg is the mean raw score on that criterion across the 160 Screening AI vendors in scope of this rubric, this one included.

Strongest criterion

Bias Audit Transparency

80raw / 100

Contributes 14.4 to the total. 80 against a 41 category average.

Weakest criterion

FRIA Support

33raw / 100

Contributes 5.0 to the total. 33 against a 31 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.

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Evidence ledger

Items
16
Documentation
7
Audit report
2
Public statement
4
Absence
3
With a source URL
13 of 16
Source hostnames
3
Fewest items
2
Article 11 Technical Documentation

These figures measure how thoroughly Metaview was reviewed, not how Metaview performed — an absence row, recording that nothing was located, is counted like any other item.

Article 11 Technical Documentation

2 items38
  • DocumentationCaptured Jun 7, 2026

    Application Review provides candidate-level written rationale and a documented Ideal Candidate Profile, but this is product UX, not a published model/system card or technical pack.

    https://www.metaview.ai/application-review

  • AbsenceCaptured Jun 7, 2026

    Searches of metaview.ai, its blog, privacy pages and support help center surfaced no AI model card, system card, explainability whitepaper, responsible-AI policy or ISO 42001 certification.

    Have a URL that disputes this?

Bias Audit Transparency

2 items80

FRIA Support

2 items33

Data Governance Disclosure

3 items60
  • DocumentationCaptured Jun 7, 2026

    Privacy page documents SOC 2 Type II, 256-bit encryption, AWS UK data residency, default 2-year customizable retention, DPA availability and on-demand deletion.

    https://www.metaview.ai/privacy

  • Public statementCaptured Jun 7, 2026

    Support center states Metaview does not use customer data for AI model training (relying on pre-trained LLM partners) and lists sub-processors in its Trust Center.

    https://support.metaview.ai/account-management/privacy-and-security/overview

  • DocumentationCaptured Jun 7, 2026

    Privacy policy details GDPR/CCPA data-subject rights, SCCs for transfers, an EU Article 27 representative (Osano) and exclusion of special-category data as structured fields.

    https://www.metaview.ai/privacy-policy

Human Oversight Design

2 items60
  • DocumentationCaptured Jun 7, 2026

    Application Review states 'AI informs. You decide.' and 'we never auto-reject', with an editable Ideal Candidate Profile and candidate-level reasoning humans can override.

    https://www.metaview.ai/application-review

  • DocumentationCaptured Jun 7, 2026

    Homepage describes user-defined guardrails on AI agents, audit trails documenting AI reasoning, and the ability to review, edit or override AI recommendations.

    https://www.metaview.ai/

Post-Market Monitoring

2 items48

Customer Documentation

3 items62

Editorial notes

Company overview

Metaview, founded in 2018 by ex-Uber and ex-Palantir leaders Siadhal Magos and Shahriar Tajbakhsh and headquartered in London (with a US entity, Metaview Technologies Inc.), began as an AI interview notetaker that records, transcribes and summarizes recruiting interviews and generates structured notes and scorecards. It has since expanded into a broader recruiting platform: sourcing, candidate search, job posts, reporting, and an Application Review agent that turns a job description into an Ideal Candidate Profile and screens every applicant into 'Strong / Good / Partial match' buckets with written rationale. The company has over 2,500 customers (including Sony, Brex, Deel and Deliveroo) and raised a $35M Series B led by GV in 2025.

Regulatory exposure

While the AI Notetaker is positioned as non-decisioning ('doesn't make decisions, evaluate candidates, or provide recommendations'), the Application Review product ranks and rates candidates against a profile, which makes it an automated employment decision tool implicating NYC Local Law 144 and likely EU AI Act Annex III high-risk scrutiny. Metaview engages Warden AI for monthly, ongoing bias audits of Application Review (latest 7 May 2026, 15,264-candidate sample) with a publicly downloadable NYC LL 144 report and intersectional sex/race analysis. On the EU AI Act, Metaview publicly asserts a non-high-risk position under the Article 6(3) exemption rather than publishing high-risk-deployer (FRIA) documentation. It is SOC 2 Type II certified, GDPR/CCPA compliant, hosts data in AWS UK, and states it does not train AI models on customer data.

Path to a higher score

Metaview already clears the hardest bar (continuous, publicly downloadable third-party bias auditing) and has strong security/data-governance disclosure. To raise its scores it should: publish Article 11-style technical documentation or a model card / explainability statement for Application Review and a responsible-AI policy; provide explicit NYC LL 144 deployer guidance and candidate-notice templates rather than leaving compliance to customers; document its EU AI Act Article 6(3) reasoning and offer FRIA / deployer-obligation guidance for customers who do treat it as high-risk; surface a public sub-processor list and a status/incident channel; and pursue ISO 42001 to formalize AI governance.

Regulatory frame

What applies to screening AI.

Treated as high-risk under Annex III §4 when the screening output is decisive. EU AI Act Article 13 transparency obligations apply; deployers must give candidates a way to contest.

Keep reading

All screening vendorsHow this score is builtAnnex IV section-by-section: what AI hiring vendors must documentWhat makes a defensible NYC LL 144 bias audit

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Ranking

Others rated in Screening AI

Ranked 29 of 160 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.

  1. #26iCIMS55F
  2. #27Classet55F
  3. #28Plum54F
  4. #29MetaviewThis profile54F
  5. #30Checkr54F
  6. #31Bullhorn54F
All screening vendors

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.