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Top 10 Best Data Entry Scanning Software of 2026

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Top 10 Best Data Entry Scanning Software of 2026

Shortlist data entry scanning software with an editorial comparison of tools like IBM Datacap, DocuClipper, and SimpleIndex for document capture workflows.

20 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

AI search agents find sources, extract figures with provenance, and aggregate written reviews. They also transcribe 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

Data entry scanning software matters when paper and image records must turn into structured fields that systems can ingest with consistent schemas. This comparison helps analysts and operators weigh document capture, OCR extraction, validation rules, and integration paths so teams can choose tooling that fits their automation workload and data model requirements.

SimpleIndex is the best pick for SMB operations teams that need repeatable document indexing with controlled output, thanks to its configurable verification workflow that gates extracted fields before final records, whereas IBM Datacap fits enterprises needing forms-based capture with validation and routing decisions for batch handoff.

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

SimpleIndex

Configurable verification workflow that gates extracted fields before final record output.

Best for: Fits when operations teams need controlled, repeatable document indexing with human review for low-confidence fields.

2

IBM Datacap

Editor pick

Forms-based workflow configuration that couples extracted fields with validation and routing decisions.

Best for: Fits when enterprises need high-control forms capture with validation gates and system handoff across batch intake.

3

DocuClipper

Editor pick

Human-in-the-loop field verification for extracted values, reducing retyping after OCR errors.

Best for: Fits when operations teams need repeatable scan-to-fields capture with verification for imperfect documents.

Comparison Table

1
SimpleIndexBest overall
SMB
9.2/10
Overall
2
enterprise
8.8/10
Overall
3
vertical specialist
8.7/10
Overall
4
8.4/10
Overall
5
API-first
8.3/10
Overall
6
7.9/10
Overall
7
API-first
7.6/10
Overall
8
7.0/10
Overall
9
6.8/10
Overall
10
6.6/10
Overall
#1

SimpleIndex

SMB

Document scanning and indexing software that captures metadata from scanned files and exports structured records.

9.2/10
Overall
Features9.2/10
Ease of Use9.0/10
Value9.4/10
Standout feature

Configurable verification workflow that gates extracted fields before final record output.

SimpleIndex is built for scanning-to-index workflows that route documents through configurable extraction rules and then into verification steps. Capture runs in batches with duplex handling and multi-page documents so operators can process large jobs with fewer manual handoffs. Field mapping is driven by configuration that defines where values are taken from and how records are assembled. The administrative surface supports role-based access and operational controls that keep indexing tasks aligned with team processes.

A tradeoff is that extraction accuracy depends on the quality of the source documents and the alignment between configured field rules and the document layout. Straight-through processing rate drops when forms vary heavily or when low-quality scans reduce the reliability of character boundaries. SimpleIndex fits well for organizations that need repeatable forms processing with human-in-the-loop validation for exceptions.

Pros
  • +Template-driven field mapping supports consistent forms processing
  • +Batch scanning and verification reduces manual rework on exceptions
  • +Structured field export supports downstream integration and indexing
  • +Access controls help separate scanning, indexing, and review roles
Cons
  • –Extraction accuracy drops when document layouts vary beyond configured rules
  • –Configuration requires workflow and field-mapping discipline to avoid drift
  • –Complex multi-document routing can require careful operational setup
  • –Advanced AI document understanding is limited compared with cloud document AI
Use scenarios
  • Accounts payable operations

    Index invoices from scanned mail

    Fewer posting errors in batch runs

  • HR document management

    Process standardized employee forms

    Consistent onboarding data capture

Show 2 more scenarios
  • Branch back-office teams

    Capture client applications at scale

    Faster case file indexing

    Document templates drive extraction, then exceptions are routed for human validation.

  • Compliance and record control

    Scan-to-archive with index fields

    More traceable document indexing

    Captures are archived with associated structured fields so retrieval follows governed metadata.

Best for: Fits when operations teams need controlled, repeatable document indexing with human review for low-confidence fields.

#2

IBM Datacap

enterprise

Document capture software that scans, recognizes, and validates data from paper and image-based records.

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

Forms-based workflow configuration that couples extracted fields with validation and routing decisions.

IBM Datacap targets document-driven data entry workflows where extracted fields must be reviewed, corrected, and then sent to enterprise systems.

Its core workflow configuration supports capture steps, extraction rules, and human approval paths tied to extraction confidence outcomes.

Batch processing and document separation features help manage multi-document jobs in high-volume intake environments.

Integration capabilities support sending extracted results and metadata to downstream applications that store, index, or process records.

Pros
  • +Workflow-driven forms processing with field-level verification hooks
  • +Batch capture support with document separation and blank page handling
  • +Integration options for passing extracted fields into external systems
  • +Human-in-the-loop validation tied to extraction confidence
Cons
  • –Admin setup for capture and workflow components adds implementation overhead
  • –Performance tuning is needed to maintain throughput across large batches
  • –Customization effort increases for complex fixed-layout and variable-layout forms
  • –Advanced enhancements often depend on add-ons or deeper platform configuration
Use scenarios
  • Finance and lending operations teams

    Extract fields from bank statements

    Consistent structured statement data

  • Back-office records teams

    Digitize scanned document backlogs

    Searchable structured backlog data

Show 1 more scenario
  • Data quality operations teams

    Route low-confidence fields to staff

    Fewer unreviewed extraction errors

    Uses confidence outcomes to route uncertain extraction to operators for correction before use.

Best for: Fits when enterprises need high-control forms capture with validation gates and system handoff across batch intake.

#3

DocuClipper

vertical specialist

OCR software that extracts transaction data from scanned bank statements, invoices, receipts, and financial documents.

8.7/10
Overall
Features8.7/10
Ease of Use8.8/10
Value8.6/10
Standout feature

Human-in-the-loop field verification for extracted values, reducing retyping after OCR errors.

DocuClipper is positioned for scan-to-workflow use where teams need consistent extraction rules across common forms and document types. The product workflow emphasizes preprocessing and structured field output, then routes extracted values into a verification stage for human-in-the-loop corrections. Batch capture supports throughput needs when volume is steady and document layouts repeat. Configuration depth matters most when forms vary by source or when field-level overrides must follow a defined rule set.

A key tradeoff is that better results depend on maintaining extraction mappings and field definitions as documents drift. DocuClipper is a strong fit when operations teams receive recurring paper submissions and need CSV-style field outputs for import into business systems. It is less ideal when documents are highly unstructured and change layout daily without an update cycle.

Pros
  • +Batch capture designed for repeatable form intake
  • +Field-level verification supports human-in-the-loop correction
  • +Configurable extraction rules reduce per-document manual edits
  • +Structured output format supports direct downstream import
Cons
  • –Extraction quality depends on keeping mappings aligned to template changes
  • –Requires workflow setup effort before high-volume throughput
Use scenarios
  • Accounts payable teams

    Invoice intake from paper remittances

    Fewer manual data entry cycles

  • Insurance operations

    Claim forms from mixed sources

    Faster claims data completion

Show 2 more scenarios
  • Healthcare admin teams

    Patient forms and consent packets

    Reduced keyboard entry workload

    Converts submitted pages into structured fields for intake databases.

  • Logistics and billing teams

    Shipping documents and remittance slips

    More consistent back-office updates

    Processes document batches and exports extracted values for operational records.

Best for: Fits when operations teams need repeatable scan-to-fields capture with verification for imperfect documents.

#4

ABBYY FlexiCapture

enterprise

Enterprise document capture software that extracts structured data from scanned forms, invoices, IDs, and mixed document batches.

8.4/10
Overall
Features8.4/10
Ease of Use8.5/10
Value8.3/10
Standout feature

Exception handling that routes low-confidence fields into validation steps without breaking batch context.

ABBYY FlexiCapture is an on-premise data entry scanning system built for repeatable document capture and controlled extraction workflows. It combines zonal data extraction with model-based field confidence and human-in-the-loop validation so exceptions can be routed and corrected without redoing whole batches. Batch scanning support and batch-level export to common formats help teams move from captured documents to downstream systems without manual transcription.

Pros
  • +Strong zonal data extraction with field-level confidence scoring
  • +Workflow routing supports human-in-the-loop validation for low-confidence fields
  • +Good batch scanning throughput for high-volume document operations
  • +Well-suited for configuration-driven templates and repeatable capture
Cons
  • –Document template configuration requires capture-design time and tuning
  • –Automation via API can be limited by project scope compared with general-purpose ETL tools
  • –Governance features can require disciplined role design and process ownership
  • –Integration work is needed to align outputs with custom downstream schemas
Use scenarios
  • Finance and lending operations teams

    Extract recurring fields from statements

    Repeatable statement field extraction

  • Back-office digitization teams

    Convert archived scans to data

    Structured data from backlogs

Show 2 more scenarios
  • Operations data quality teams

    Correct uncertain extractions first

    Controlled exception handling

    Routes low-confidence fields to operators so downstream systems receive validated values.

  • Lenders and loan operations teams

    Organize borrower income documents

    Organized borrower data for review

    Extracts fields from income documents and related forms to support lender review workflows.

Best for: Fits when teams need controlled, exception-aware document extraction with repeatable capture templates.

#5

Nanonets

API-first

AI OCR platform that captures structured data from scanned documents, receipts, invoices, IDs, and forms.

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

Field-level confidence handling routes uncertain values into review instead of exporting them blindly.

Nanonets targets workflows where documents must be converted into consistent fields, such as invoices, receipts, and other fixed business forms. Document handling includes extraction settings, validation rules, and an approval step for questionable fields. Automation hooks support pushing results to external systems and triggering follow-on steps after extraction.

Pros
  • +Human-in-the-loop review gates low-confidence field outputs
  • +API and automation hooks support push-based integration into workflows
  • +Field-level extraction configuration supports multiple document types
  • +Export-friendly outputs help drive scan-to-workflow processes
Cons
  • –Document-type configuration requires ongoing iteration as volumes change
  • –Complex multi-page layouts can need extra workflow tuning
  • –Governance controls are more workflow-centric than enterprise policy-centric
  • –External system mapping work is needed for consistent downstream schemas
Use scenarios
  • Accounts payable teams

    Invoice capture with field verification

    Fewer manual re-keying cycles

  • Procurement operations

    Receipt and PO reconciliation

    Faster exception handling

Show 2 more scenarios
  • Customer operations teams

    Form-based intake from scans

    Lower data-entry workload

    Inbound forms are converted into structured records with validation for missing fields.

  • Systems integration teams

    Automated document-to-app workflows

    More consistent record creation

    Extracted results trigger API-driven updates in CRM and case systems after approval.

Best for: Fits when operations teams need document field extraction with review queues and API-driven handoff.

#6

Kofax TotalAgility

enterprise

Document automation platform that captures data from scanned documents and routes it into business systems.

7.9/10
Overall
Features7.9/10
Ease of Use7.9/10
Value7.9/10
Standout feature

Field-level verification workflow that ties extraction results to review queues and controlled rework paths.

Kofax TotalAgility processes scanned documents into structured business data using configurable capture workflows with document understanding and routing. It combines forms processing, document classification, and human-in-the-loop validation to control field-level accuracy before data lands in business systems.

Pros
  • +Human-in-the-loop validation enables controlled exception handling before export
  • +Configurable extraction and routing supports mixed document sets in one workflow
  • +Audit-oriented review steps help standardize how fields are verified
  • +Extensibility supports integration patterns for scan-to-workflow routes
Cons
  • –Workflow configuration can require specialist time for complex extraction rules
  • –Advanced document understanding often depends on well-prepared templates and labeling
  • –Batch throughput tuning can be deployment sensitive in high-volume captures
  • –Field-level adjustments can become difficult to maintain across many document variants
Use scenarios
  • Finance and lending operations teams

    Capture bank statement transaction fields

    Validated transaction field capture

  • Back-office records teams

    Digitize document queues to fields

    Searchable structured backlog intake

Show 1 more scenario
  • Lenders and loan operations teams

    Extract borrower document details

    Borrower data organized for review

    Processes income, tax, and identity documents into organized fields for application processing.

Best for: Fits when organizations need governed document capture with validation checkpoints and structured extraction into back-office systems.

#7

FormX

API-first

API-first OCR extraction platform for scanned receipts, invoices, IDs, and other structured business documents.

7.6/10
Overall
Features7.6/10
Ease of Use7.6/10
Value7.6/10
Standout feature

Confidence-driven human-in-the-loop review that targets only fields needing verification.

FormX is a data entry scanning system built for extracting fields from forms and document images into usable records. It focuses on configurable capture pipelines that combine automated extraction with human-in-the-loop checks for low-confidence fields.

Pros
  • +Human review workflow for low-confidence extracted fields
  • +API-oriented automation for pushing results into downstream systems
  • +Batch processing suited to recurring form capture
  • +Field-level controls for tuning extraction outcomes
Cons
  • –Limited visibility into extraction internals versus enterprise document AI suites
  • –Higher effort to reach stable performance across varied template layouts
  • –Less suited to high-volume TWAIN and scanner driver deployments
  • –May require configuration work for multi-step capture workflows
Use scenarios
  • Data quality operations teams

    Review low-confidence extracted fields

    Fewer unreviewed extraction errors

  • Back-office digitization teams

    Convert scan batches into records

    Structured data from document backlogs

Show 2 more scenarios
  • Finance and lending operations teams

    Extract structured data from statements

    Consistent structured statement queues

    Extracts recurring statement and transaction fields into consistent data objects for review.

  • Lenders and lending operations teams

    Structure borrower intake documents

    Organized borrower data for processing

    Extracts fields from pay stubs, tax forms, and identity documents for lending review steps.

Best for: Fits when teams need form and invoice-style field extraction with API-driven handoff and human validation.

#8

Docsumo

SMB

Document AI platform that extracts data from scanned PDFs, statements, invoices, and forms with validation workflows.

7.0/10
Overall
Features7.0/10
Ease of Use7.0/10
Value7.0/10
Standout feature

Human-in-the-loop field verification driven by confidence scores for exception handling.

Docsumo targets data entry scanning by extracting structured fields from invoices and forms rather than only producing OCR text.

Pros
  • +Confidence-scored extractions support targeted human review of low-confidence fields
  • +Invoice-focused extraction workflows handle common line-item and header patterns
  • +Batch capture reduces effort for high-volume scanning and ingestion queues
  • +Export-friendly field outputs fit downstream reconciliation and indexing steps
Cons
  • –Complex document layouts may need more training and field-level verification
  • –Automation depth can depend on connected workflow components rather than extraction alone
Use scenarios
  • Finance and lending operations teams

    Extract transaction fields from bank docs

    Consistent structured transaction data

  • Back-office records operations teams

    Digitize paper and archived records

    Searchable structured backlog data

Show 2 more scenarios
  • Operations teams managing exceptions

    Reduce re-keying with routed reviews

    Fewer manual re-keying tasks

    Routes extraction exceptions based on confidence so operators correct only problematic fields.

  • Lenders and lending operations teams

    Organize borrower income and identity data

    Organized borrower records

    Extracts fields from income documents and identity materials to support application processing workflows.

Best for: Fits when teams need invoice and forms extraction with human validation and batch throughput.

#9

Scan123

SMB

Document scanning and indexing software that captures fields from paper records using OCR, barcode, and validation rules.

6.8/10
Overall
Features6.8/10
Ease of Use6.8/10
Value6.8/10
Standout feature

Operator-driven field mapping with preprocessing controls to stabilize extraction on mixed-quality batch scans.

Scan123 digitizes paper forms by turning scanned images into structured fields using configurable capture and extraction rules. It supports document-to-data workflows for batch scanning scenarios, including duplex inputs and multi-page handling.

Pros
  • +Batch-friendly capture workflow for multi-page document sets
  • +Image preprocessing options like deskew and despeckling for cleaner extraction
  • +Field mapping controls for consistent structured output
  • +Export output designed for handoff into downstream data entry steps
Cons
  • –Advanced automation and integration depth is limited compared with API-first tools
  • –Governance features like role-based access and audit logs are not a prominent focus
  • –Complex forms processing may require iterative rule tuning
  • –Throughput tuning across large volumes depends on careful document preparation
Use scenarios
  • Back-office digitization teams

    Convert scanned queues into fields

    Structured data from backlog scans

  • Finance and lending operations teams

    Extract structured data from statements

    Consistent structured statement data

Show 2 more scenarios
  • Data quality operations teams

    Maintain consistent outputs across batches

    More consistent extracted fields

    Applies deskew, despeckling, and blank-page handling to reduce inconsistent extraction outputs.

  • Lenders and lending operations teams

    Organize borrower documents for review

    Organized borrower intake data

    Extracts fields from borrower documents so application teams can review organized record data.

Best for: Fits when teams need dependable scanned form to structured output for batch document entry without deep engineering.

#10

FileCenter Receipts

SMB

Desktop-focused scanning and OCR software that turns paper receipts and similar documents into searchable digital records.

6.6/10
Overall
Features6.6/10
Ease of Use6.7/10
Value6.5/10
Standout feature

Receipt-specific indexing that keeps extracted fields tied to the stored document record for review and correction.

FileCenter Receipts focuses on receipt capture and data entry for expense and accounting workflows, with an emphasis on turning scans into structured fields for downstream use. The software supports batch scanning and scan-to-archive style capture, then routes documents into indexed storage with extraction-ready fields. Administrative controls center on folder and permissions structure, while automation focuses on consistent capture, filing, and validation steps rather than developer-style pipeline building.

Pros
  • +Receipt-oriented field templates reduce re-entry during expense capture
  • +Batch capture and multipage filing support high-volume workflows
  • +Folder and permission structure helps keep documents separated
  • +Document previews speed up human verification for low-confidence fields
Cons
  • –Fewer integration options than document AI platforms with broad OCR APIs
  • –Automation depth is limited for custom field logic and routing
  • –Advanced extraction controls depend on structured templates
  • –No clear publishable API surface for external workflow orchestration
Use scenarios
  • Accounting operations teams

    Turn receipt scans into fields

    Extraction-ready receipt data

  • Back-office digitization teams

    Archive batches with extracted fields

    Indexed receipt records

Show 2 more scenarios
  • Data quality operations teams

    Validate extracted values before use

    Fewer incorrect accounting entries

    Supports consistent capture and filing steps so downstream workflows use validated receipt fields.

  • Finance teams with high-volume intake

    Standardize receipt capture pipelines

    Consistent receipt processing

    Applies folder and permissions structure to keep receipt intake organized across multiple operators.

Best for: Fits when teams need receipt capture, human-checked fields, and consistent filing without custom pipelines.

Conclusion

After evaluating 10 data entry scanning software, SimpleIndex 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
SimpleIndex

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 Data Entry Scanning Software

How do DocuClipper and Nanonets differ in how they route low-confidence fields for review?
DocuClipper uses human-in-the-loop field verification so only extracted values below confidence thresholds trigger corrections. Nanonets routes uncertain fields into review through field-level confidence handling so batches can export partially verified records instead of forcing full manual rework.
Which tool pairs best with existing enterprise capture stacks when APIs and event-driven handoffs are required?
IBM Datacap exposes integration via APIs and event-driven routing so extracted fields can flow into case management, ECM, and data ingestion services. DocuClipper also supports downstream import workflows, but Datacap is built around enterprise handoff patterns that span intake, validation, and routing.
What integration path works for batch scanning hardware using TWAIN, ISIS, or WIA drivers?
SimpleIndex is designed to include scanning hardware integration and then apply template-based field extraction to batch ingestion. Scan123 also supports batch document capture workflows and preprocess controls, and it targets mixed-quality form scans where operator settings stabilize extraction.
How does ABBYY FlexiCapture preserve batch context when exception handling reroutes fields?
ABBYY FlexiCapture routes low-confidence fields into validation steps without breaking batch context, so operators correct exceptions while other pages keep their association to the same batch. Kofax TotalAgility uses capture workflow checkpoints and controlled rework paths that tie field verification to review queues rather than restarting capture at the page level.
When do scan preprocessing controls matter most for Scan123 and FileCenter Receipts?
Scan123 makes preprocessing part of operator-run extraction by applying deskew, despeckling, and blank-page detection to stabilize mixed-quality inputs. FileCenter Receipts focuses on receipt capture and scan-to-archive filing, so preprocessing serves consistency, but its primary control surface centers on folder structure, permissions, and receipt indexing.
What breaks if forms processing outputs do not match the receiving system data model in Kofax TotalAgility?
Kofax TotalAgility ties document understanding and field-level verification to governed workflow paths, so mismatched field mappings can prevent correct placement into back-office systems. ABBYY FlexiCapture can handle exceptions inside the batch, but it still depends on configured extraction templates that align with downstream record structures.
How do SimpleIndex and Docsumo handle template-based versus workflow-driven extraction configuration?
SimpleIndex emphasizes template-based field extraction with a verification workflow that gates extracted fields before final record output. Docsumo uses an extraction pipeline that produces confidence-scored field outputs for human-in-the-loop validation, with invoice capture as a central workflow focus.
Which tool is better for organizations that need scan-to-archive plus indexed storage linked to the source document record?
FileCenter Receipts keeps extracted fields tied to the stored document record so review and correction map back to the archived receipt. SimpleIndex supports scan-to-archive behaviors and repeatable indexing, but FileCenter Receipts is receipt-specific and keeps indexing aligned to receipt handling workflows.
How does FormX differ from IBM Datacap in its approach to human validation and API-driven handoff?
FormX uses confidence-driven human-in-the-loop review that targets only fields needing verification and then exposes results through an API layer for system handoff. IBM Datacap couples forms-based workflow configuration with validation gates and routing via APIs and events, which fits enterprise capture stacks that require centralized workflow control across batch intake.

Tools reviewed

Primary sources checked during evaluation.

Referenced in the comparison table and product reviews above.

How to Choose the Right Data Entry Scanning Software

We separate data entry scanning options by how reliably they turn scanned pages into usable fields, how they handle messy documents, and how much automation you can control with your workflow. Focus on criteria like extraction quality and throughput, because those show up in real capture jobs, not marketing.

Extraction quality on real samples

Why it matters

Extraction quality on real samples decides whether you get correct fields the first time or need heavy cleanup. For this category, small mistakes in captured text or structure quickly become re-entry work.

How to check

Bring 3–5 documents you actually scan and run the same import twice in the demo. Compare output fields against your expected values, especially names, dates, totals, and addresses.

Automation controls in workflow

Why it matters

Automation controls in workflow determine whether the tool only reads pages or also routes results into the next steps you run daily. Better automation means fewer manual clicks between capture and entry.

How to check

Ask for a demo that takes you from scan to completed record in one flow. Look for rules you can set for when fields populate, when outputs are saved, and how reruns behave.

Throughput and processing latency

Why it matters

Throughput and processing latency affect daily turnaround when you scan many pages. If results lag or processing pauses, operators spend time waiting or re-scanning.

How to check

Time a run on a multipage sample set similar to your typical batch size. Note whether the tool reports progress clearly and how long it takes from scanning to usable output.

Handling of mixed document types

Why it matters

Handling of mixed document types matters when you scan different forms, receipts, or letters in the same batch. The tool should avoid failing or producing scrambled fields when the page layout changes.

How to check

Test with a mixed stack that includes at least two noticeably different layouts. Check whether field extraction stays stable and whether it labels outputs so you can tell what was captured.

Output formats and field mapping

Why it matters

Output formats and field mapping decide how neatly results land in the places you enter data. If mappings are rigid or outputs are inconsistent, your team must reformat or redo fields.

How to check

Verify the available output targets in the demo and export one sample to your expected destination type. Confirm you can map extracted fields to your column or field names and that repeats/edits preserve structure.

Red flags

  • Extraction looks good on one page but falls apart on a mixed stack.
  • The workflow stops early, forcing manual steps before fields are usable.
  • Exports don’t preserve field structure or mapping you set in the demo.

Fit by situation

  • If most of your time goes to correcting misread fields, prioritize extraction quality on real samples.
  • If you need capture to drive the next steps with minimal operator clicks, prioritize automation controls in workflow.
  • If you scan batches with mixed layouts, prioritize handling of mixed document types.
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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