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Top 10 Best AI Detection Services of 2026

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Top 10 Best AI Detection Services of 2026

Compare ai detection services and key use-case fit, including providers like NCC Group, Graphika, and Deloitte for teams choosing tools.

19 min readUpdated Editorially scoped research
How we ranked these tools
01Editorial Scope

Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.

02Source Research

Search agents gather the source set and the review record, including transcriptions of video reviews and podcasts.

Read our full methodology →

Framework: Features 40% · Ease 30% · Value 30%

Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy

AI detection services sit between content pipelines and compliance controls by turning provenance signals, visual forensics, and model-risk indicators into auditable outputs through APIs and configurable data models. This comparison helps technical evaluators weigh tradeoffs across deepfake and disinformation detection, image or media verification, and enterprise governance needs like RBAC, audit logs, and integration throughput.

NCC Group is the best pick for organizations that need AI-content detection evidence and adversarial testing support, because its evidence-grade reporting links outputs to testing assumptions and uncertainty for review boards, while Deloitte fits regulated teams needing documented, stakeholder-facing assessment trails beyond a simple score.

Editor’s top 3 picks

Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.

Editor pick
1

NCC Group

Evidence-grade reporting that ties detection outputs to testing assumptions and uncertainty for review boards.

Best for: Fits when organizations need AI-content detection evidence and adversarial testing support, not just a score.

2

Graphika

Editor pick

Entity-centric risk graphing that ties detections to relationships and explainable evidence paths.

Best for: Fits when investigators need evidence-linked AI detection across connected entities.

3

Deloitte

Editor pick

Documented assessment workflow built for stakeholder-facing evidence trails, not just detection scores.

Best for: Fits when regulated teams need defensible AI-content assessment tied to governance and legal review.

Comparison Table

1
NCC GroupBest overall
specialist
9.0/10
Overall
2
specialist
8.9/10
Overall
3
enterprise_vendor
8.8/10
Overall
4
enterprise_vendor
8.3/10
Overall
5
specialist
8.2/10
Overall
6
specialist
7.7/10
Overall
7
specialist
7.5/10
Overall
8
specialist
7.1/10
Overall
9
enterprise_vendor
6.9/10
Overall
10
enterprise_vendor
6.5/10
Overall
#1

NCC Group

specialist

AI security and model risk detection consulting services.

9.0/10
Overall
Features9.0/10
Ease of Use9.0/10
Value9.0/10
Standout feature

Evidence-grade reporting that ties detection outputs to testing assumptions and uncertainty for review boards.

NCC Group delivers AI detection as part of broader cyber, assurance, and risk testing work, which shapes its methodology for handling adversarial content and evidence trails. The service supports document-level assessment with reporting built for editorial and security workflows, including clarity on confidence and error risk.

NCC Group also fits organizations that need vendor-managed testing with controlled inputs, rather than only self-serve scoring. Multimodal handling is available through engagement-specific pipelines, covering text and other media types when provided in the request scope.

Pros
  • +Engagement-led methodology with report artifacts geared for governance reviews
  • +Clear treatment of false-positive and false-negative risk in deliverables
  • +Good fit for adversarial testing scenarios that stress classifier behavior
  • +Evidence-ready documentation supports downstream investigation workflows
Cons
  • –API integration and automation are limited compared with productized detection stacks
  • –Turnaround and customization depend on engagement scope and analyst availability
  • –Less suited to high-throughput self-service scoring without project support
  • –Multimodal coverage varies by intake format and agreed test pipeline
Use scenarios
  • security architecture teams

    ai detection for control gap mapping

    risk-informed remediation roadmap

  • incident response teams

    ai detection for infection-scope evidence

    containment actions and evidence

Show 2 more scenarios
  • security readiness leaders

    ai detection for tabletop incident exercises

    readiness gaps identified

    Runs exercises using controlled adversarial content inputs to surface decision and process gaps.

  • legal procurement teams

    ai detection for software continuity assurance

    continuity assurance for suppliers

    Coordinates escrow deposits and verifies whether deposited materials support reconstruction needs.

Best for: Fits when organizations need AI-content detection evidence and adversarial testing support, not just a score.

#2

Graphika

specialist

Network analysis and AI-generated disinformation detection.

8.9/10
Overall
Features8.9/10
Ease of Use8.9/10
Value8.9/10
Standout feature

Entity-centric risk graphing that ties detections to relationships and explainable evidence paths.

Graphika centers on relationship modeling and investigative context, which suits programs that need more than token-level flags. The system links signals across documents and sources so teams can trace why a finding exists and how it relates to known entities. It also supports operational use where analysts need repeatable workflows for triage, escalation, and documentation.

A tradeoff appears in throughput planning because graph construction and evidence aggregation can be heavier than lightweight text scoring. Graphika fits when teams can dedicate analysts and engineering time to tune thresholds, entity resolution rules, and review criteria for their corpus. It also works best in cases where outputs must withstand internal review, not only moderation decisions.

Pros
  • +Graph-first evidence trails connect findings to entities and relationships
  • +Investigation workflows produce analyst-readable output instead of only labels
  • +Configurable pipelines support repeatable triage and escalation steps
  • +Entity correlation helps reduce isolated false alarms
Cons
  • –Heavier integration work than single-model detection APIs
  • –Graph modeling choices can require ongoing tuning across data shifts
  • –Review workflows take analyst time for high-volume streams
  • –Multimodal coverage breadth can lag specialized single-medium vendors
Use scenarios
  • Fighting disinformation teams

    Trace coordinated synthetic campaigns

    Faster, evidence-backed investigations

  • Financial crime analysts

    Detect synthetic identity influence

    More defensible escalation decisions

Show 2 more scenarios
  • Policy and compliance teams

    Audit provenance of submissions

    Lower review rework

    Produces structured findings tied to evidence trails for internal review and governance.

  • Large newsroom operations

    Prioritize risky sources and edits

    Reduced false-positive editorial churn

    Correlates submissions and authorship signals so editors can triage suspicious batches.

Best for: Fits when investigators need evidence-linked AI detection across connected entities.

#3

Deloitte

enterprise_vendor

AI risk advisory and deepfake detection consulting services.

8.8/10
Overall
Features8.8/10
Ease of Use8.8/10
Value8.8/10
Standout feature

Documented assessment workflow built for stakeholder-facing evidence trails, not just detection scores.

Deloitte delivers AI-detection services as part of risk, compliance, and technology assurance engagements rather than a standalone consumer scanner.

Its delivery commonly pairs content-signal analysis for text and other media with calibration testing to control false-positive and false-negative rates in real workflows.

The engagement framing supports evidence handling and repeatable documentation for review by legal, compliance, and publishing stakeholders.

Pros
  • +Engagement delivery model supports documented, defensible detection outcomes
  • +Cross-media assessment fits legal and compliance review workflows
  • +Calibration and evaluation planning helps reduce false positives in practice
  • +Governance-oriented evidence handling aligns with regulated environments
Cons
  • –Service delivery can mean less self-serve product tooling than scanner vendors
  • –API and automation surfaces are not the primary customer artifact
  • –Detection coverage can depend on scope and team-built configurations
  • –Turnaround is tied to engagement cycles rather than real-time scoring SLAs
Use scenarios
  • healthcare data programs

    ai detection in interoperability review workflows

    consistent data exchange

  • real estate portfolio teams

    ai detection for appeal evidence handling

    organized appeal support

Show 2 more scenarios
  • supply chain technology teams

    ai detection during EDI transformation checks

    structured partner connectivity

    Combines signal analysis with calibration steps for false-positive and false-negative control.

  • marketing analytics teams

    ai detection alongside modeling documentation

    documented modeling insights

    Pairs content review documentation with channel effect modeling for planning decisions.

Best for: Fits when regulated teams need defensible AI-content assessment tied to governance and legal review.

#4

Accenture

enterprise_vendor

AI security and AI content detection consulting services.

8.3/10
Overall
Features8.3/10
Ease of Use8.3/10
Value8.3/10
Standout feature

Operational calibration and evaluation work built into delivery, using adversarial paraphrasing and threshold tuning.

Accenture is a consulting and delivery firm that applies AI-generated content detection as part of broader governance and risk programs. Its distinct angle is end-to-end integration across enterprise workflows, including policy, tooling, and operational controls for content review and authenticity checks.

Pros
  • +Integration into enterprise governance workflows with documented delivery artifacts
  • +Custom engineering support for multimodal pipelines across formats and channels
  • +Repeatable evaluation steps for detector behavior under adversarial paraphrasing
  • +Audit-oriented reporting for decisioning and operational monitoring
Cons
  • –Detection accuracy depends on implementation scope set during delivery
  • –API integration and automation surface are typically project-specific
  • –Requires cross-team alignment for calibration testing and threshold tuning
  • –Less suitable for teams seeking a ready-to-embed detector product
Use scenarios
  • enterprise architecture teams

    ai detection in governed portal approvals

    governed approvals and access

  • enterprise integration teams

    ai detection in B2B operations governance

    standardized partner connectivity

Show 2 more scenarios
  • enterprise marketing procurement

    ai detection for vendor content governance

    more controlled media buying

    Applies authenticity review controls across procurement orchestration and campaign execution artifacts.

  • CFO and finance transformation

    ai detection for audit-trail reporting controls

    consistent close processes

    Connects content review outcomes to reconciliation and audit-trail workflows for reporting and close.

Best for: Fits when enterprises need detection integrated into governance, review ops, and risk reporting.

#5

Sensity AI

specialist

Visual threat intelligence and deepfake detection services.

8.2/10
Overall
Features8.2/10
Ease of Use8.3/10
Value8.1/10
Standout feature

Structured detection output with confidence signals designed for triage and repeatable editorial decisions.

Sensity AI is geared toward production pipelines that evaluate authored content at scale, not just ad hoc checks. Output is organized to support review workflows, with scores that can be used for triage and reviewer handoff. The value is strongest when detection results must be routed into existing systems such as internal review queues or content governance processes.

Pros
  • +Document-level results support editorial triage workflows
  • +Classification confidence outputs help reviewers calibrate decisions
  • +API-first delivery fits automation inside existing review pipelines
  • +Consistent scoring reduces manual re-checking across submissions
Cons
  • –Text-first focus leaves image and video detection to other tools
  • –Governance requires clear thresholds to limit reviewer churn
Use scenarios
  • Editorial ops teams

    Pre-publication AI-text screening

    Faster approvals with fewer misses

  • Compliance reviewers

    Content authenticity workflow checks

    More consistent enforcement

Show 2 more scenarios
  • Developer teams

    API automation in content systems

    Lower manual workload

    Integrates scoring into ingestion and moderation pipelines for batch processing.

  • Academia publishing

    Submission screening at scale

    Reduced review bottlenecks

    Applies consistent document scoring to triage manuscripts before deeper checks.

Best for: Fits when publishing teams need automated AI-text screening with consistent, reviewer-friendly outputs.

#6

Truepic

specialist

Image verification and AI manipulation detection services.

7.7/10
Overall
Features7.7/10
Ease of Use7.7/10
Value7.7/10
Standout feature

Multimedia provenance-centric outputs that support review decisions beyond a single yes or no label.

Truepic focuses on AI content authenticity signals for media, with emphasis on image and video provenance evidence rather than only text scoring. It provides detection outputs that can be reviewed for classifier confidence and decision rationales used in editorial and compliance workflows.

Pros
  • +Designed around multimedia authenticity signals for images and video
  • +Integration and automation outputs that fit editorial and moderation workflows
  • +Classifier confidence surfaced alongside detection results for review decisions
  • +Consistent handling of provenance evidence across submitted media files
Cons
  • –Text-only AI detection coverage is not the primary strength
  • –Governance discipline is needed to keep thresholds aligned to policy
Use scenarios
  • brand protection teams

    ai detection for redistributed image attribution

    image origin evidence

  • platform integrity teams

    ai detection for cryptographic media receipts

    machine-checkable integrity evidence

Show 2 more scenarios
  • background screening HR

    ai detection support for verification records

    organized verification records

    Supports consent-based verification workflows that produce structured records for identity decisions.

  • inspection field operations

    ai detection for video moment traceability

    signoff tied to footage

    Links video inspection outcomes to exact footage moments for consistent signoff and collaboration.

Best for: Fits when review teams need image and video authenticity checks in a managed workflow.

#7

Logically

specialist

Disinformation and AI-generated content detection services.

7.5/10
Overall
Features7.5/10
Ease of Use7.6/10
Value7.4/10
Standout feature

API-driven detection runs on submission events with configurable handling for repeatable triage.

Logically targets teams that need consistent AI-generated text detection integrated into an editorial or compliance workflow. It focuses on document-level scoring with supporting signals that help triage suspect content instead of just returning a single verdict.

Pros
  • +API-first design supports automated checks inside existing review pipelines
  • +Document-level output supports consistent triage across long submissions
  • +Audit-friendly result handling helps teams keep decisions traceable
  • +Configuration options fit editorial rules without manual review for every item
Cons
  • –Multimodal coverage is limited, with stronger emphasis on text workflows
  • –Model behavior tuning requires operational discipline to reduce false positives
  • –Token-level highlight detail is not its primary strength for analysts
  • –Adversarial paraphrasing performance varies by writing style and domain
Use scenarios
  • Legal review teams

    Screen submissions for AI-written language

    Reduce manual text checks

  • Compliance and policy teams

    Verify AI generation in reports

    Improve documentation consistency

Show 2 more scenarios
  • Editorial operations teams

    Triage drafts from content vendors

    Speed up publication review

    Provides scores and supporting signals to route drafts into appropriate review lanes before publishing decisions.

  • Security and governance leads

    Enforce detection in submission pipelines

    Standardize governance workflows

    Runs API-driven AI detection checks that standardize handling of results across teams and workflows.

Best for: Fits when content teams need automated, document-level AI text checks inside an editorial workflow.

#8

Blackbird AI

specialist

Narrative risk and AI-generated threat detection services.

7.1/10
Overall
Features7.1/10
Ease of Use7.2/10
Value7.0/10
Standout feature

Segment-level evidence with highlighted spans and confidence supports reviewer triage and audit-style consistency.

Blackbird AI targets production screening where a single classifier score is not enough, because it provides segment-level outputs and evidence artifacts that editors and reviewers can inspect. The service covers text and multimodal inputs so the same workflow can handle documents that include pasted excerpts and images. The standout evaluation detail is how the system reports uncertainty alongside predictions, which supports calibration and review triage rather than blind rejection.

Pros
  • +Sentence-level scoring provides reviewer evidence instead of a single label
  • +Multimodal input support reduces workflow fragmentation across formats
  • +Per-segment confidence enables thresholding and triage routing
  • +API-first delivery supports automation in existing content pipelines
Cons
  • –Confidence drops on heavily paraphrased synthetic writing without review baselines
  • –Explaining mixed-format results requires tighter reviewer training
Use scenarios
  • Admissions operations teams

    Screen mixed text and attachments

    Faster triage with fewer escalations

  • Academic integrity staff

    Route assignments by risk

    Lower false review workload

Show 2 more scenarios
  • Editorial review teams

    Audit draft submissions at scale

    More consistent enforcement decisions

    Run automated scoring per segment and attach evidence artifacts to reviewer queues.

  • K-12 content moderators

    Handle images and written text

    Coverage across mixed submissions

    Score multimodal submissions so image-only attempts still trigger review attention.

Best for: Fits when editorial teams need segment evidence and multimodal handling with API-driven review automation.

#9

EY

enterprise_vendor

AI assurance and detection consulting services.

6.9/10
Overall
Features6.9/10
Ease of Use6.9/10
Value6.9/10
Standout feature

Forensic delivery approach that produces review documentation aligned to legal and compliance evidence standards.

EY is a services firm that offers AI detection support through forensic-grade analysis programs rather than a single generic detector. Its work typically combines text and content review workflows with evidentiary documentation for disputes, internal investigations, and compliance teams.

Pros
  • +Investigation-style evidence handling supports defensible review trails for disputes
  • +Works well with editorial and legal workflows that require documented findings
  • +Focus on calibration testing and false-positive risk management in program design
  • +Engagements can be tailored to document-level analysis needs
Cons
  • –Less suited for self-serve bulk detection without an engagement
  • –Automation and API integration depth is constrained by project delivery model
  • –Token-level highlighting and explainability reports are not delivered as a standard product layer
  • –Requires governance discipline to keep outputs consistent across reviewers
Use scenarios
  • health plan operations

    ai detection for dispute-ready content review

    evidence-ready review records

  • energy retail marketing governance

    ai detection for disclosure and approval artifacts

    documented approvals

Show 2 more scenarios
  • ERP program stakeholders

    ai detection for governed investigation procedures

    traceable decisions

    Integrates detection outputs into repeatable review procedures aligned to auditable decision trails.

  • regulated reporting teams

    ai detection for compliance evidence mapping

    more structured reporting controls

    Supports operational integration of content review with measurable audit needs and governance artifacts.

Best for: Fits when regulated teams need evidence-grade AI detection review procedures with documented decision history.

#10

KPMG

enterprise_vendor

AI risk and detection advisory services.

6.5/10
Overall
Features6.5/10
Ease of Use6.5/10
Value6.5/10
Standout feature

Assurance-oriented evidence packaging that links synthetic-content findings to governance and testing documentation for stakeholders.

KPMG focuses on AI governance and assurance work, so its AI-detection offerings are typically delivered as audit-grade analysis inside broader risk and compliance engagements. Capabilities center on provenance review, authorship analysis support, and evidence handling for investigations tied to synthetic content and content authenticity.

Pros
  • +Engagement-led evidence handling for authenticity and provenance reviews
  • +Documented assurance orientation for findings tied to control objectives
  • +Works well for regulated workflows needing traceable analysis artifacts
  • +Multidisciplinary support across AI risk, investigations, and compliance
Cons
  • –Limited turnkey detection access outside managed engagements
  • –Automation depth and API surface are not the primary delivery mechanism
  • –Turnaround and repeatability depend on engagement scope and staffing
  • –May require governance discipline to keep outputs consistent across cases
Use scenarios
  • transformation offices

    ai detection for value realization evidence

    governed program value view

  • internal audit and risk

    ai detection for control testing documentation

    traceable assurance findings

Show 2 more scenarios
  • marketing strategy executives

    ai detection for measurement framework governance

    measurable marketing strategy

    Supports stakeholder review with documentation that ties authenticity findings to measurement practices.

  • healthcare security risk leaders

    ai detection for security control baseline

    accountable remediation actions

    Assesses healthcare security controls and prepares incident workflows with accountability for remediation.

Best for: Fits when teams need assurance-grade AI authenticity analysis tied to investigations or compliance controls.

Conclusion

After evaluating 10 ai detection, NCC Group stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.

Our Top Pick
NCC Group

Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.

Frequently Asked Questions About Ai Detection

Which providers offer API-driven detection that can run inside an editorial workflow?
Logically and Blackbird AI both support automation-ready API surfaces so detection can execute during submission and feed downstream review steps. Sensity AI and Truepic also support programmatic access for structured outputs, but Blackbird AI additionally highlights spans for segment triage.
How do segment-level outputs change triage compared with document-only scoring?
Blackbird AI returns highlighted spans with per-segment confidence, which supports reviewer decisions at sentence or segment granularity rather than a single document label. Sensity AI and Logically focus on document-level scoring plus supporting signals, which reduces review time but limits pinpointing where synthetic characteristics occur.
When do multimodal capabilities matter for AI detection workflows?
NCC Group includes engagement-specific multimodal pipelines for adversarial testing when non-text inputs are part of the request scope. Blackbird AI and Truepic handle image and multimodal workflows that route evidence into review systems, which is useful for mixed submissions such as screenshots plus drafted text.
What breaks if detection thresholds are not calibrated to the organization’s content mix?
Deloitte treats calibration and accuracy testing as part of governance engagements, because false positives and false negatives shift when the underlying writing style distribution differs. Accenture similarly applies threshold tuning and calibration to manage error rates, and without that tuning, automated blocks may flag legitimate authorship.
Which providers are oriented toward evidence trails for legal, disputes, or governance boards?
EY and KPMG package forensic-grade analysis and assurance artifacts that map findings to decision history and control objectives for stakeholder review. Deloitte also emphasizes documented, repeatable methods for regulated processes, while Graphika focuses on evidence-linked investigation across connected entities.
How does entity-centric modeling affect investigations compared with standalone text classification?
Graphika connects people, content, and behavior into a configurable risk model, which supports investigation-grade prioritization when the same actor produces multiple submissions. Text-first workflows in Sensity AI and Logically can triage single documents well, but they do not produce the same relationship graph for cross-case reasoning.
How do onboarding and delivery models differ between consulting-led assurance and API-first detection?
Deloitte, Accenture, and KPMG typically deliver AI detection as part of broader risk, compliance, or assurance engagements with governance artifacts, testing assumptions, and documented methods. Logically and Sensity AI focus on API-driven detection checks that integrate into editorial automation, which shortens time-to-pipeline but limits handholding for complex assurance deliverables.
Which providers best support governance configuration, audit log patterns, and repeatable admin controls?
Logically and Blackbird AI are built for repeatable internal handling of results with configuration that supports consistent triage. Deloitte, EY, and NCC Group emphasize governance artifacts and controlled testing assumptions, which aligns with audit log needs but often comes through managed engagements rather than self-serve configuration.
Where does AI detection fall short when files lack required context or metadata for provenance checks?
Truepic and NCC Group can produce higher confidence when image or video evidence arrives with the needed input scope for provenance workflows, because missing or altered context reduces reliable attribution signals. Graphika also depends on relationship-relevant inputs, and without enough connected entities, entity risk modeling becomes less actionable than document-level scoring.

Tools reviewed

Primary sources checked during evaluation.

Referenced in the comparison table and product reviews above.

How to Choose the Right Ai Detection

Options differ most in how detection works on your real inputs and how the output behaves over time. Prioritize the label that matches your evidence needs: false-positive rate, false-negative rate, and explainability report.

False-positive rate control

Why it matters

A low false-positive rate control matters when teams must avoid flagging legitimate content. For ai detection, this directly affects trust in day-to-day review.

How to check

Run a small batch of your own past, human-authored text and compare how often it gets flagged. Ask for a configurable threshold or policy knob and test how flags change when you move it.

False-negative rate under detection load

Why it matters

A low false-negative rate matters when you must catch likely ai-crafted content rather than just samples that look obviously generated. It affects coverage of the category’s “misses.”

How to check

Use a mix of hard-to-detect samples from your workflow (short paragraphs, edits, and paraphrases) and see what remains unflagged. If the tool supports confidence-style outputs, check whether you can set a stricter policy and observe the trade-off.

Multimodal input coverage behavior

Why it matters

Multimodal input coverage behavior matters if you process more than text, because ai image detection and ai video detection often behave differently than ai-generated text detection. Mismatched handling leads to inconsistent enforcement.

How to check

In a demo, submit one file per supported type that matches your real format and note whether results are comparable in detail. Ask what happens when only some modalities are present (partial results, errors, or empty outputs).

Token-level highlighting consistency

Why it matters

Token-level highlighting consistency helps reviewers validate why content was flagged instead of relying on a single label. In ai detection workflows, inconsistent highlighting drives rework.

How to check

Try both a clean example and a borderline example and compare whether highlighted spans track meaningful regions like edited sentences. Ask whether the tool highlights before/after revisions and whether it supports sentence-level scoring.

Explainability report for reviewers

Why it matters

An explainability report matters because teams need actionable reasons, not only a result. For ai detection, this supports internal review standards and auditability.

How to check

Request an explainability report output for the same input across two thresholds. Check whether it references stable signals (e.g., per-segment scores) and whether it stays consistent when you re-run with the same text.

API and automation for enforcement

Why it matters

API and automation for enforcement matters when detections must run in your pipeline rather than a manual screen. Extensibility and configuration determine whether ai detection can be integrated with your review flow.

How to check

Ask for an API request/response example that includes input, policy settings, and returned outputs. Test a webhook or batch run (if available) and verify you can reproduce the same results from the UI settings.

Red flags

  • It flags nearly everything from your own human-authored samples
  • It offers results but no transparent explainability report to support review decisions
  • Token-level highlighting changes wildly between runs on the same text

Fit by situation

  • If you review many borderline submissions from the same team, prioritize false-positive rate control.
  • If you must catch subtle ai-crafted edits, prioritize false-negative rate under detection load.
  • If your workflow mixes text and other media, prioritize multimodal input coverage behavior.
Commissioned Research

Funding and competing interests

This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.

They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.

Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →

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