Fetcher
HireAIScore rates Fetcher 37 out of 100 (grade F, Substantial gaps) under rubric v1.0, last reviewed July 17, 2026. Fetcher ranks 69 of 86 vendors rated in Candidate sourcing AI, against a category average of 46. That score is drawn from 19 evidence items across 7 rubric criteria for Fetcher — 13 with a cited source, 6 recording that nothing was located.
Score breakdown
Fetcher, 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 48
27
+5.4
Bias Audit Transparency
Weight 18% · category avg 40
28
+5.0
FRIA Support
Weight 15% · category avg 31
25
+3.8
Data Governance Disclosure
Weight 15% · category avg 53
55
+8.3
Human Oversight Design
Weight 12% · category avg 56
52
+6.2
Post-Market Monitoring
Weight 12% · category avg 40
35
+4.2
Customer Documentation
Weight 8% · category avg 57
55
+4.4
Total weighted score
37.3(rounded to 37 for display)
Category avg is the mean raw score on that criterion across the 86 Candidate sourcing AI vendors in scope of this rubric, this one included.
Strongest criterion
Customer Documentation
55raw / 100
Contributes 4.4 to the total. 55 against a 57 category average.
Weakest criterion
FRIA Support
25raw / 100
Contributes 3.8 to the total. 25 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.
Download diligence recordEvidence ledger
- Items
- 19
- Documentation
- 10
- Public statement
- 3
- Absence
- 6
- With a source URL
- 13 of 19
- Source hostnames
- 2
- Fewest items
- 2
- Bias Audit Transparency
These figures measure how thoroughly Fetcher was reviewed, not how Fetcher performed — an absence row, recording that nothing was located, is counted like any other item.
Article 11 Technical Documentation
3 items27
- AbsenceCaptured Jul 17, 2026
No AI/model cards, explainability statement, AI policy doc, or ISO 42001 found across fetcher.ai, its blog, or help.fetcher.ai.
- Public statementCaptured Jul 17, 2026
Product pages describe AI that sorts, screens, and sources candidates but provide no technical or explainability documentation of how matches are produced.
- DocumentationCaptured Jul 17, 2026
SOC 2 Type 2 is security-focused (security, availability, processing integrity, confidentiality, privacy) and contains no AI model transparency or technical pack.
Bias Audit Transparency
2 items28
- AbsenceCaptured Jul 17, 2026
No NYC Local Law 144 bias-audit summary or independent algorithmic audit (BABL AI, DCI, ORCAA, Warden AI, Holistic AI, Credo AI) found on fetcher.ai or in the ACLU LL144 audit tracker.
- Public statementCaptured Jul 17, 2026
Fetcher markets machine learning plus human review to 'reduce bias' and provides demographic diversity metrics, but offers no audit substantiating fairness claims.
https://fetcher.ai/blog/6-diversity-sourcing-methods-to-reach-underrepresented-talent
FRIA Support
2 items25
- AbsenceCaptured Jul 17, 2026
No EU AI Act Article 27 FRIA template, deployer-obligation guidance, or high-risk-AI deployer material found anywhere on fetcher.ai or help.fetcher.ai.
- DocumentationCaptured Jul 17, 2026
EU coverage is limited to GDPR, the EU-U.S. Data Privacy Framework, and Standard Contractual Clauses for data transfers, with nothing addressing AI Act fundamental-rights assessments.
Data Governance Disclosure
3 items55
- DocumentationCaptured Jul 17, 2026
Privacy policy names data sources (LinkedIn, People Data Labs, FullContact, SeekOut, ContactOut, Hunter, SalesQL, RocketReach), excludes sensitive categories (sexual orientation, political views, health), states it does not currently train AI on email/ATS data, and sets an approximately one-year lead-retention window.
- DocumentationCaptured Jul 17, 2026
Holds SOC 2 Type 2 covering security, availability, processing integrity, confidentiality, and privacy.
- DocumentationCaptured Jul 17, 2026
GDPR help article describes encryption, regular security assessments, and Fetcher's controller/processor roles.
Human Oversight Design
3 items52
- DocumentationCaptured Jul 17, 2026
Product is framed as decision-support: it sorts/screens inbound and sources passive candidates for recruiters, with real-human customer-success iteration on search briefs rather than autonomous hiring decisions.
- DocumentationCaptured Jul 17, 2026
An 'Applicant Review' feature lets recruiters review inbound candidates before action, indicating a human-review step in the workflow.
- AbsenceCaptured Jul 17, 2026
No public documentation of per-candidate match explainability, per-jurisdiction toggles, override controls, or audit logs.
Post-Market Monitoring
3 items35
- DocumentationCaptured Jul 17, 2026
Maintains a product-updates changelog in its help center, though entries are dated and the most recent is roughly a year old.
- Public statementCaptured Jul 17, 2026
Publishes a GDPR contact (gdpr@fetcher.ai) and support channels for data-subject and privacy issues.
- AbsenceCaptured Jul 17, 2026
No first-party status/uptime page, vulnerability-disclosure/security.txt, or AI model-monitoring dashboard; status.fetch.ai belongs to the unrelated blockchain company Fetch.ai, not Fetcher.
Customer Documentation
3 items55
- DocumentationCaptured Jul 17, 2026
Public help center includes a GDPR FAQ alongside product-update documentation.
- DocumentationCaptured Jul 17, 2026
Public privacy policy, terms of service, and an opt-out mechanism are available, complemented by full product pages and a blog/guides resource hub.
- AbsenceCaptured Jul 17, 2026
No NYC Local Law 144 or EU AI Act deployer/compliance guidance and no publicly posted DPA for customers to assess AEDT obligations.
Editorial notes
Company overview
Fetcher (legally Tiplinks, Inc.) is a New York-based AI talent-sourcing platform, founded around 2014 and rebranded to Fetcher in 2019, that automates passive and active candidate sourcing plus personalized email outreach for corporate recruiting teams. Its AI scans a large candidate database (marketed at 500M+ profiles) to generate candidate batches that human sourcers and recruiters review before outreach, and it offers DEI/diversity search filters and pipeline diversity metrics. The company has raised roughly $40M, serves 1,000+ organizations, and holds SOC 2 Type 2 certification. Pricing runs self-serve from about $115/month up to mid-five-figure annual enterprise contracts, placing it in the mid tier.
Regulatory exposure
As an AI sourcing and outreach tool used by NYC and multinational employers, Fetcher sits adjacent to NYC Local Law 144 (its screening/ranking features could constitute an AEDT for deployers), the EU AI Act's high-risk employment provisions, and Illinois/Colorado AI-hiring rules. Despite this, Fetcher publishes no bias audit, no AEDT compliance guidance, no FRIA support, and no AI-specific technical or explainability documentation. Its diversity features (DEI filters and demographic pipeline metrics) also involve protected-characteristic processing, though the privacy policy states sensitive categories are excluded. Compliance obligations are pushed to customers: the terms make subscribers responsible for laws related to illegal discrimination.
Path to a higher score
Fetcher could raise its score by commissioning and publicly posting an independent NYC LL 144-style bias audit of its matching and screening AI (ideally two consecutive years), publishing a model/system card plus an explainability statement for how candidate matches are produced, and adding EU AI Act deployer guidance with a FRIA template. Restoring a first-party security/trust page with a subprocessor list and public DPA, standing up a status page and a vulnerability-disclosure channel, and documenting concrete in-product human-oversight controls (override, audit logs, per-jurisdiction toggles) would lift the technical-documentation, oversight, monitoring, and customer-documentation categories.
Regulatory frame
What applies to candidate sourcing AI.
Ranking outputs are inside Annex III §4 when they influence hiring decisions. GDPR Article 22 (automated decision-making) and the GDPR rights around profiling apply independently for EU candidates.
Compare
Fetcher against its nearest-scoring peers.
The closest scores in Candidate sourcing AI, side by side.
- Side-by-sideFetcher vs Arya by Leoforce38F
- Side-by-sideFetcher vs Loxo37F
- Side-by-sideFetcher vs RecruitBot37F
- Side-by-sideFetcher vs AmazingHiring37F
Ranking
Others rated in Candidate sourcing AI
Ranked 69 of 86 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.
- #66ZipRecruiter40F
- #67Paycor40F
- #68Arya by Leoforce38F
- #69FetcherThis profile37F
- #70Loxo37F
- #71RecruitBot37F
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.