GITNUXSOFTWARE REVIEW
MediaTop 10 Best Automatic Video Tagging Software of 2026
Compare automatic video tagging software options with use cases and tradeoffs for teams planning metadata extraction and search.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
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Framework: Features 40% · Ease 30% · Value 30%
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Hive is the best pick for teams that need time-coded automatic tagging with review gates because it returns time-anchored label spans with clean metadata exports, while AnyClip is a strong alternative for media groups that want interactive moment-based tags with human-in-the-loop corrections.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Hive
Time-anchored tags that attach label spans to specific video offsets, supporting targeted review and correction.
Best for: Fits when teams need time-coded automatic tagging with review gates and API-driven metadata export.
AnyClip
Editor pickInteractive, time-referenced tags that link concept outputs to specific video moments for editorial navigation and retrieval.
Best for: Fits when media teams need segment-level tagging with review control and tight metadata mapping.
Google Cloud Video Intelligence API
Editor pickShot boundary detection and time-coded concept segments returned as structured annotations for downstream time-sliced indexing.
Best for: Fits when media teams need automated, time-coded concept and scene tags from VOD archives into a governed metadata pipeline.
Comparison Table
Hive
API-firstComputer vision API provider with automatic video tagging, classification, and moderation models.
Time-anchored tags that attach label spans to specific video offsets, supporting targeted review and correction.
Hive targets organizations that need repeatable automatic tagging plus review controls, rather than one-off transcription or generic object labels. Tag outputs are time-aware so tags can be anchored to video positions for editorial review and operational handoff. Confidence thresholds and review loops help manage the recall-precision tradeoff for multi-label tagging workflows.
A tradeoff is that better governance and higher throughput depend on configuring the pipeline and review flow to match a controlled vocabulary and validation needs. Hive fits VOD processing where batch ingestion runs overnight and analysts review only low-confidence or high-impact segments.
- +Time-coded tag outputs support frame-referenced review workflows
- +Confidence thresholds reduce false positives in downstream indexing
- +API-driven automation supports batch ingestion and metadata post-processing
- +Human-in-the-loop review supports active learning from corrections
- –High-quality results require upfront taxonomy alignment and tuning
- –Governance controls are only as effective as the configured review criteria
Media operations teams
Tag VOD assets for faster review
Less manual scrubbing time
Digital asset management teams
Enrich archives for search and filtering
Higher search relevance
Show 2 more scenarios
Compliance and brand safety
Route risky segments for review
Lower review workload
Confidence-based routing sends flagged segments to human review to control false positive rates.
Machine learning ops teams
Continuously improve tagging via corrections
Fewer repeat labeling errors
Human feedback can be used to tighten label behavior across new batches and similar assets.
Best for: Fits when teams need time-coded automatic tagging with review gates and API-driven metadata export.
AnyClip
enterpriseVideo intelligence platform that automatically tags moments and metadata in video content.
Interactive, time-referenced tags that link concept outputs to specific video moments for editorial navigation and retrieval.
AnyClip is designed for tagging that users can navigate within a video, with results delivered as time-referenced metadata that supports search and editorial review. The workflow typically uses automatic concept and activity signals, then routes a subset of items to human-in-the-loop review to reduce false positives. That combination fits teams that treat video metadata as an operational asset rather than a static transcription artifact.
A key tradeoff is that high-control taxonomy mapping requires governance work around tag definitions and review rules, especially when multiple editors or brands share a single library. AnyClip fits best when video assets need frequent re-tagging across large catalogs and when time-coded tags must be consistent enough for faceted retrieval and moderation.
- +Time-coded tags align to navigable video segments
- +Supports human-in-the-loop review to correct automatic labels
- +Exports metadata that can feed indexing and DAM workflows
- +Designed for interactive use cases that need segment-level retrieval
- –Taxonomy mapping needs governance to stay consistent
- –Tag precision can drop on niche concepts without curation
Media asset operations teams
Enrich large VOD catalogs
Faster search at moment level
Digital rights and compliance teams
Apply consistent content restrictions
Lower risk tagging errors
Show 2 more scenarios
Video editorial teams
Review and correct automatic labels
Higher retrieval relevance
Audit tag confidence by segment and revise taxonomy assignments during ongoing content workflows.
Platform integration teams
Index video for faceted search
Consistent metadata enrichment
Export segment metadata and map it into existing indexing fields for cross-asset filtering.
Best for: Fits when media teams need segment-level tagging with review control and tight metadata mapping.
Google Cloud Video Intelligence API
enterpriseCloud API that automatically detects labels, objects, faces, and scenes in video content.
Shot boundary detection and time-coded concept segments returned as structured annotations for downstream time-sliced indexing.
Google Cloud Video Intelligence API provides automated concept detection and object tagging on uploaded or stored video through a REST API workflow. It returns time-aligned results such as shot boundary detection, labels with confidence scores, and video metadata enrichment with spatial-temporal features.
The API supports both batch ingestion via long-running operations and frame-level findings needed for downstream search relevance. Tight integration with Google Cloud IAM and job output structures supports controlled automation pipelines for media asset management and knowledge workflows.
- +Time-aligned label results with confidence scoring for searchable metadata
- +REST API integration with long-running operations for batch tagging
- +Shot boundary detection output supports scene segmentation workflows
- +Google Cloud IAM alignment supports RBAC-controlled access to processing
- –Limited support for real-time stream tagging compared with live-focused tools
- –Not designed for custom taxonomy mapping or ontology alignment out of the box
Media platform data teams
Label concepts with time-aligned metadata
Faster clip retrieval
Cloud data engineers
Run batch tagging with job outputs
Automated enrichment pipeline
Show 1 more scenario
IAM-governed operations teams
Automate tagging inside access controls
Controlled automation
Use service permissions to trigger analysis and store structured metadata for downstream workflows.
Best for: Fits when media teams need automated, time-coded concept and scene tags from VOD archives into a governed metadata pipeline.
Valossa
enterpriseFinnish AI company providing automatic video content analysis and metadata tagging APIs.
Governed concept-to-tag mapping that turns model detections into organization-controlled, time-scoped metadata for review.
Valossa produces time-coded tags tied to the video timeline, which supports time-scoped review and downstream search filters. Concept detection and action recognition outputs can be mapped to a controlled vocabulary so teams get consistent labeling across batches. Integration depth is geared toward connecting tag results to existing media workflows via API interactions and connectors for common repositories.
- +Time-coded tags support timeline review and targeted retrieval
- +Taxonomy mapping helps keep concept labels consistent across archives
- +Automation-oriented workflow fits large batch enrichment operations
- +Integration targets media repositories used in content operations
- –Taxonomy setup work is required to make tags consistently meaningful
- –Timeline tagging quality depends on chosen concepts and review thresholds
- –Advanced tuning can add operational overhead for small teams
- –Less suited to pure low-latency real-time tagging needs
Media archive teams
Batch enrich video libraries with concepts
Less manual labeling work
Compliance and brand safety teams
Tag restricted concepts across assets
Lower review overhead
Show 2 more scenarios
Content operations teams
Speed editorial search by events
Faster content discovery
Use concept tags to filter for scenes and actions without watching full videos.
Data and media engineering
Integrate tagging output into workflows
More automated media pipelines
Send tag metadata to downstream systems so video operations can automate approvals and indexing.
Best for: Fits when teams enrich large video archives and need governed, time-coded concept tags for search.
Cloudinary
SMBMedia management platform with automatic video tagging via AI-driven content analysis add-ons.
Automatic video tagging delivered through Cloudinary’s transformation and delivery workflow via API and event callbacks.
Cloudinary is a media management service that can attach automatic video tagging to the rest of a video asset pipeline. Video analysis runs as part of Cloudinary’s processing workflow, so tags can be generated during ingestion and then stored alongside the derived assets Cloudinary produces.
- +Tight integration between upload processing and tag generation
- +REST API automation for post-processing and metadata retrieval
- +Consistent derived-asset handling simplifies batch ingestion workflows
- +Webhooks support event-driven tag delivery into downstream services
- –Tag taxonomy control needs custom mapping for controlled vocabularies
- –Throughput tuning can be constrained by the processing pipeline design
- –Human-in-the-loop review and active learning workflows need external tooling
- –Temporal granularity and confidence threshold controls are not exposed as fine-grained knobs
Media operations teams
Extract frames with timestamped labels
Time-coded metadata export
Content platform teams
Publish tagged videos from headless workflows
Less custom transcoding
Show 2 more scenarios
Web and app developers
Serve tagged media variants dynamically
Reduced manual exports
Request appropriately sized video or image variants through application delivery calls.
Backend integration developers
Sync tagging results via events
No polling required
React to processing and asset changes by triggering downstream API calls and webhooks.
Best for: Fits when teams already route media through Cloudinary and need automated tags to enrich search and DAM metadata.
VideoKen
SMBVideo intelligence platform that auto-indexes, tags, and segments video content for search and reuse.
Human-in-the-loop review with domain taxonomy mapping to improve tag precision over repeated batches.
VideoKen automatically tags videos by analyzing visual frames and associated signals to produce time-coded labels that support search and review workflows. The core workflow centers on concept detection and scene labeling with configurable confidence thresholding and batch ingestion for media libraries.
- +Produces time-coded tags aligned to detected scenes and concepts
- +Supports batch ingestion workflows for asset library enrichment
- +Offers configurable confidence thresholding to tune precision
- +Integrates into downstream metadata export and indexing steps
- –Tag set quality depends on taxonomy mapping coverage
- –Throughput and latency can require careful batch sizing
- –Requires governance discipline to manage label drift over time
- –Webhook and API post-processing coverage may need additional engineering
Media library curators
Generate time-coded scene tags in bulk
More searchable archives
DAM integration teams
Export metadata for indexing pipelines
Cleaner catalog metadata
Show 1 more scenario
Taxonomy mapping teams
Reduce false positives via review loops
Fewer incorrect tags
Use human review to refine domain vocabulary mapping and improve tagging accuracy.
Best for: Fits when media teams need automated time-coded tagging and metadata export into DAM, MAM, or search pipelines.
Amazon Rekognition Video
enterpriseAWS service for automated label detection, face search, and content moderation in video streams.
API-driven, time-aligned label output that can be wired directly into AWS metadata pipelines for immediate enrichment.
Amazon Rekognition Video automatically generates labels for video content using pre-trained concept detection models. It can run object and scene recognition over time and return confidence-scored results with time alignment suitable for time-coded tags.
- +Time-aligned label results support timestamp granularity for search and review
- +Concept and object detection cover common auto-classification needs
- +AWS-native integration fits event-driven ingestion and metadata enrichment
- +Confidence scores help tune false positive rate during triage
- –Taxonomy mapping from raw labels to controlled vocabulary needs custom logic
- –Advanced governance such as audit-ready change tracking requires additional pipeline design
- –Batch ingestion and post-processing add engineering effort for high throughput
- –Shot-level accuracy depends on video encoding and segmenting quality
Security video analytics teams
Create time-aligned object and scene labels
Faster investigation
AWS workflow automation teams
Integrate tagging into existing pipelines
Consistent metadata schema
Show 1 more scenario
Review and QA teams
Validate outputs with human-in-the-loop
Lower labeling errors
Use streaming-like job result patterns to support review before final metadata use.
Best for: Fits when AWS-based teams need automated, time-coded tagging of VOD assets into a searchable metadata layer.
DeepVA
enterpriseComputer vision platform for video analysis that extracts labels, scenes, objects, and content metadata automatically.
Time-coded concept tags combined with audio transcription context in a single tagging output for metadata enrichment workflows.
DeepVA is an automatic video tagging system focused on producing time-coded, multi-label metadata from video and audio signals. It handles concept detection style tags and combines visual evidence with audio transcription outputs to add context around spoken content.
- +Produces multi-label tags with timestamps for easier editorial review
- +Combines audio transcription with visual concepts in one output
- +Supports batch ingestion for large asset libraries
- +Exports structured results suitable for metadata enrichment
- –Tag confidence controls are limited, which raises false-positive risk
- –Dense tagging can increase review workload without prioritization controls
- –API post-processing requires custom mapping to match internal vocabularies
- –Throughput tuning can be necessary for high-volume ingestion
Media search product teams
Attach visual tags and audio context
Better moment-level relevance
Content operations teams
Batch enrich catalogs with structured tags
Consistent enrichment
Show 1 more scenario
Metadata standardization teams
Maintain uniform timestamp granularity
More uniform datasets
Keep consistent timestamp granularity across many assets for reliable downstream mapping.
Best for: Fits when media teams need automated, time-coded multi-label metadata from VOD batches and want consistent outputs for search.
Veritone
enterpriseAI platform with cognitive engines for automatic video transcription, tagging, and content indexing.
Model orchestration that chains multiple recognition steps into a single annotation workflow with time-coded results.
Veritone is an automatic video tagging system built around model orchestration, so inference outputs can be combined into workflow-ready annotations instead of only returning raw classifier labels. It supports time-coded tagging for video segments and can enrich assets with multi-modal signals like speech-to-text and OCR-style text extraction.
- +Orchestrates multiple AI outputs into structured, time-coded annotations
- +API-first integration supports automated tagging and downstream enrichment
- +Workflow configuration enables consistent tag generation across asset libraries
- +Multi-modal extraction covers audio and visual text signals
- –Advanced workflow behavior needs careful configuration discipline
- –Tag taxonomy mapping depends on aligning outputs to a controlled vocabulary
- –Higher throughput setups require attention to inference latency and batching strategy
- –Some recognition quality varies by source video codec and frame rate handling
Content operations teams
Generate time-coded tags at scale
More searchable collections
Security operations teams
Run camera recognition workflows
Evidence-ready results
Show 2 more scenarios
Legal and records teams
Support reviewable redaction checkpoints
Disclosure-ready outputs
Detect sensitive content and apply redaction while preserving review checkpoints for comparisons.
Security compliance teams
Trigger alertable events with evidence
Automated alerts
Apply zone rules and recognition steps to produce structured alerts with reviewable video evidence.
Best for: Fits when media teams need repeatable, automated tagging with API-driven orchestration across VOD libraries.
Twelve Labs
API-firstVideo understanding API that generates semantic tags and searchable metadata from visual, spoken, and contextual content.
Time-coded concept detection outputs align tags to specific moments for review workflows and retrieval.
Twelve Labs focuses on automatic visual concept tagging for large video sets, with model-driven outputs that include time-coded references to detected moments. The workflow centers on ingesting video and producing enriched tags for downstream search, compliance tagging, and review.
- +Time-coded concept results support targeted review and faster retrieval
- +API-first workflow fits automated tagging pipelines and metadata enrichment
- +Multi-label tagging output supports faceted search patterns
- +Batch processing pattern suits high-volume VOD ingestion
- –Tag taxonomy mapping to existing DAM or MAM taxonomies needs work
- –Operational controls for confidence thresholds require careful configuration
- –Throughput varies with video length and decoding settings
- –Complex governance workflows may require external review tooling
Editors and producers
Find story sections by time-coded concepts
Quicker clip selection
Digital asset managers
Batch-generate structured searchable metadata
More navigable archives
Show 2 more scenarios
Video research analysts
Compare activity patterns across datasets
Better dataset comparisons
Use extracted events and metadata as structured inputs for cross-video comparisons.
Media archivists
Search long recordings with natural language
Faster clip discovery
Find relevant moments in long-form video archives using concept-based metadata.
Best for: Fits when media teams need automated, time-referenced tagging with an API for downstream metadata enrichment.
Conclusion
After evaluating 10 automatic video tagging software, Hive 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.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Automatic Video Tagging Software
Automatic video tagging software turns uploaded or ingested video into time-aligned labels that can be exported into downstream indexing and media asset metadata. This guide covers Hive, AnyClip, Valossa, Google Cloud Video Intelligence API, Amazon Rekognition Video, Cloudinary, Veritone, Twelve Labs, VideoKen, and DeepVA.
The tools in this set differ most in how they anchor labels to video offsets, how they gate edits through human-in-the-loop review, and how they integrate tagging outputs into existing pipelines and metadata workflows. Hive leads with time-anchored tags tied to specific video offsets and API-driven metadata export, while Google Cloud Video Intelligence API and Amazon Rekognition Video focus on structured, confidence-scored, time-coded annotations.
Automatic Video Tagging Software that outputs time-coded labels for search, review, and metadata enrichment
Automatic video tagging software extracts concepts and scenes from video, attaches those labels to specific timestamps, and outputs metadata in forms teams can route to search, digital asset management, or media archive workflows. Hive, AnyClip, Valossa, Twelve Labs, VideoKen, and DeepVA emphasize time-coded tags that support timeline review, while Google Cloud Video Intelligence API and Amazon Rekognition Video deliver structured annotations with confidence scoring for time-sliced indexing.
Teams also choose based on automation and integration shape, such as REST API integration with batch tagging and long-running operations in Google Cloud Video Intelligence API, or AWS-native wiring for Amazon Rekognition Video into existing enrichment layers. Governance and correction workflows vary too, with tools like Valossa and Hive combining governed concept-to-tag mapping with review gates that reduce false positives in downstream retrieval. Output granularity also differs, with DeepVA combining time-coded multi-label concept tags with audio transcription context in the same tagging output for metadata enrichment workflows.
Evaluation criteria for automatic video tagging outputs and control
Automatic video tagging only becomes actionable when labels are time-referenced and returned with a structure teams can route to review, indexing, or asset metadata enrichment. The standout differences in this set are how labels attach to video offsets, how edits are gated through human-in-the-loop review, and how each system exposes automation via API output or pipeline callbacks.
Time-anchored tags with reviewable offsets
Hive and AnyClip produce time-referenced tags that attach label spans to specific video moments so editors can review and correct precisely where the model is wrong.
Governed concept-to-tag mapping for consistency
Valossa and Hive both focus on keeping concept-to-tag mapping consistent across archives by using controlled review criteria tied to time-coded outputs.
API-driven batch tagging for VOD enrichment
Google Cloud Video Intelligence API and Amazon Rekognition Video provide structured, time-coded annotations via REST API integration that fits batch ingestion for VOD metadata enrichment.
Orchestration across multiple recognition steps
Veritone chains multiple recognition steps into one annotation workflow with time-coded results, while Twelve Labs concentrates on time-coded concept detection aligned to review moments.
Integration into media delivery or event-driven workflows
Cloudinary links tag generation to its upload and transformation workflow and uses event callbacks plus REST API automation for downstream metadata retrieval.
Audio-visual context merged into one tagging output
DeepVA combines time-coded concept tags with audio transcription context in the same output so metadata enrichment and review can happen without stitching separate systems.
Choose by automation surface, governance depth, and timestamp precision
The selection path starts with the tagging granularity and how edits are handled after inference, because time-anchored tags can support either lightweight correction or a structured review gate. Then the pipeline fit matters, since REST API integration supports batch VOD enrichment while event callbacks or delivery-time processing change how tagging scales with media ingestion.
Pick an offset model aligned to editorial workflow
If correction happens at the exact time span where the model labeled a moment, Hive and AnyClip fit because their outputs are designed around time-referenced review. If the priority is timeline review for governed concept tags across large archives, Valossa is better aligned to time-scoped metadata enrichment.
Decide whether governance is a product feature or a pipeline job
If controlled mapping and governed review criteria are central to keeping tag meaning consistent, choose Valossa or Hive because their workflows emphasize concept-to-tag governance. If governance must be implemented as custom logic on top of raw labels, Google Cloud Video Intelligence API and Amazon Rekognition Video require taxonomy mapping work outside the default mapping behavior.
Select the integration shape that matches ingestion and routing
For VOD batch pipelines that rely on REST calls and long-running operations, use Google Cloud Video Intelligence API or Amazon Rekognition Video. For teams that route assets through Cloudinary and want tags triggered during upload or transformation, Cloudinary integrates into a delivery workflow with API and event callbacks.
Match throughput and revision workload to batch sizing reality
If operational confidence controls can be configured and review volume must be constrained, Hive and Twelve Labs focus on configuration around confidence and time-coded outputs. If throughput and latency require careful batch sizing, VideoKen can fit batch enrichment but needs tuning to keep review workload manageable.
Choose orchestration or single-pass enrichment based on output coverage
For repeatable multi-step tagging where multiple AI outputs are chained into one workflow, Veritone suits orchestration across recognition steps. For multi-label tagging that also brings speech context into one combined output, DeepVA reduces the need for separate transcription integration.
Who benefits from automatic video tagging with time-coded outputs
Automatic video tagging fits teams that need search relevance and editorial retrieval from large video libraries without manually labeling every segment. The strongest fit depends on whether tagging results must be corrected in a governed timeline workflow or produced mainly as API-ready metadata for downstream indexing layers.
Media archives and DAM or MAM operators enriching large VOD libraries
Valossa and Hive align well with time-coded concept tags that support timeline review and archive search, while Google Cloud Video Intelligence API and Amazon Rekognition Video support batch metadata enrichment via structured time-coded annotations.
Editorial teams that review and correct tags at exact moments
Hive and AnyClip attach labels to specific video offsets so human-in-the-loop review can correct false positives and reduce downstream indexing errors.
AWS and cloud pipeline teams building enrichment layers from API calls
Amazon Rekognition Video and Google Cloud Video Intelligence API integrate through REST API pipelines that deliver confidence-scored, time-aligned annotations for VOD metadata routing.
Organizations with strict taxonomy alignment requirements for concept consistency
Valossa and Hive both require taxonomy alignment and tuning so concept-to-tag mapping stays meaningful across archives and review gates.
Teams that need combined audio transcription and visual concept metadata
DeepVA produces time-coded multi-label tags and includes audio transcription context in the same tagging output for review and search-ready enrichment.
Common pitfalls when buying Automatic Video Tagging Software
Many failed rollouts come from mismatching tagging outputs to downstream governance and review processes. Other failures come from assuming that time-coded tags and controlled vocabulary mapping work out of the box without taxonomy tuning and pipeline logic.
Treating raw model labels as final tag taxonomy without mapping
Amazon Rekognition Video and Google Cloud Video Intelligence API provide structured labels with time alignment, but taxonomy mapping into a controlled vocabulary requires custom logic to prevent inconsistent tag meaning.
Underestimating governance setup for consistent concept tags across archives
Hive and Valossa can keep time-scoped tags consistent, but governance only works when taxonomy alignment and configured review criteria match the organization’s tagging standards.
Ignoring how batch sizing affects latency and review workload
VideoKen supports batch ingestion workflows for asset library enrichment, but throughput and latency can require careful batch sizing to avoid review backlogs.
Overlooking confidence control limits that increase false positives
DeepVA focuses on dense time-coded multi-label tagging plus transcription context, but limited confidence controls can raise false-positive risk and increase editorial review effort.
Assuming delivery-time tagging will automatically match DAM metadata conventions
Cloudinary integrates tagging into its transformation and delivery workflow with API and callbacks, but controlled vocabulary and taxonomy control still needs custom mapping to fit DAM search and metadata conventions.
How We Selected and Ranked These Tools
We evaluated Hive, AnyClip, Valossa, Google Cloud Video Intelligence API, Amazon Rekognition Video, Cloudinary, Veritone, Twelve Labs, VideoKen, and DeepVA on features, ease, and value using the cards’ feature scores and ease scores. Features counted for 40% because time-coded outputs, review gating, and integration mechanisms determine whether automatic video tagging becomes actionable for search and metadata enrichment.
Ease and value counted for 30% each because pipeline setup speed and operational effort affect whether teams can sustain batch ingestion and correction loops. Hive ranked highest because time-anchored tags are designed for frame-referenced review workflows with API-driven metadata export and because configurable confidence thresholds reduce false positives that harm indexing relevance.
Frequently Asked Questions About Automatic Video Tagging Software
How do Hive and AnyClip attach tags to specific video moments for review and indexing?
Which tools provide shot boundary detection and time-aligned concept segments as structured output?
What breaks if a tagging workflow needs controlled vocabulary mapping instead of raw model labels?
How do batch ingestion and post-processing differ across Amazon Rekognition Video, Twelve Labs, and Veritone?
Which products integrate best with enterprise IAM and automated pipelines without building custom inference orchestration?
How do human-in-the-loop review and active learning-style loops reduce false positives during tagging?
What data model and schema concerns arise when emitting time-coded tags to a DAM or MAM system?
How do Veritone and DeepVA differ when audio context is required alongside visual tags?
Where do latency and throughput constraints show up when tagging must scale to many concurrent assets?
Tools reviewed
Primary sources checked during evaluation.
cloudinary.comaws.amazon.comReferenced in the comparison table and product reviews above.
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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