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Top 10 Best Professional Automation Software of 2026

GITNUXSOFTWARE REVIEW

AI In Industry

Top 10 Best Professional Automation Software of 2026

Compare professional automation software options like Nintex, Pipedream, and MuleSoft, with tradeoffs for teams planning workflow and integration automation.

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

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 our reviews. Editorial policy

Professional automation software matters when enterprises need repeatable workflow orchestration, integration through APIs, and controlled execution under audit and governance. This comparison helps operators and technical evaluators weigh build time, extensibility, and security controls across approaches ranging from process mapping to event-driven integrations and case-centric orchestration, including platforms such as MuleSoft.

Nintex is the best overall choice for enterprises that need controlled, reusable workflow automation because its components standardize custom logic across process families, while Pipedream is the better fit for API-first teams building event-driven automations with code-level control inside one workflow graph.

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

Nintex

Reusable workflow components that standardize custom logic across many processes and environments.

Best for: Fits when enterprises need controlled workflow automation with reusable components and integration-ready orchestration.

2

Pipedream

Editor pick

Workflow steps can mix custom JavaScript code with integration actions and still keep the same trigger-to-output execution model.

Best for: Fits when teams need event-driven automation with code-level control over API workflows.

3

MuleSoft

Editor pick

API-led management with RAML-backed contracts tied to Mule runtime deployment and governance controls.

Best for: Fits when enterprises need governed API-led automation across hybrid systems and multiple teams.

Comparison Table

1
NintexBest overall
enterprise
9.4/10
Overall
2
API-first
8.8/10
Overall
3
API-first
8.7/10
Overall
4
enterprise
8.4/10
Overall
5
API-first
8.2/10
Overall
6
7.9/10
Overall
7
7.4/10
Overall
8
7.0/10
Overall
9
6.9/10
Overall
10
6.5/10
Overall
#1

Nintex

enterprise

Process management and automation platform providing workflow mapping, forms, and document generation capabilities.

9.4/10
Overall
Features9.4/10
Ease of Use9.5/10
Value9.3/10
Standout feature

Reusable workflow components that standardize custom logic across many processes and environments.

Nintex is commonly used to automate document-centric workflows and operational approvals where business users define steps while IT controls integration endpoints and permissions. Workflow execution tracks state per instance and supports operational patterns like retries, error paths, and human task assignments for long-running processes. Nintex’s integration surface includes API-based connectivity to systems of record plus webhook-style triggers for event-driven start conditions.

A key tradeoff is that advanced governance and large-scale reuse depend on disciplined environment and configuration management for workflow artifacts and shared components. Nintex fits teams that need controlled orchestration across multiple applications, such as routing claims intake to specialized teams and updating records in CRM and case systems.

Pros
  • +Visual workflow designer supports complex approvals and long-running cases
  • +Reusable workflow components reduce duplication across process families
  • +Integration options support API connectivity and event-driven triggers
  • +Execution state helps operators trace progress and handle errors
Cons
  • –Reusable components require governance to prevent configuration drift
  • –Complex integrations can increase workflow depth and testing time
  • –Some enterprise automation needs rely on custom extensions and connectors
  • –Operational tuning for high throughput needs ongoing monitoring
Use scenarios
  • IT automation teams

    Standardizing process orchestration across apps

    Fewer bespoke automations

  • Operations and workflow owners

    Routing approvals and case tasks

    Shorter cycle time

Show 2 more scenarios
  • Enterprise system integration teams

    Event-driven workflow starts

    Faster handoffs

    Integrations trigger new workflow instances when external systems emit events.

  • Compliance and process governance leads

    Auditable execution for regulated processes

    Improved traceability

    Teams review per-instance execution paths and error handling outcomes for oversight.

Best for: Fits when enterprises need controlled workflow automation with reusable components and integration-ready orchestration.

#2

Pipedream

API-first

Developer-focused integration platform combining event-driven workflows with code-level control and serverless execution.

8.8/10
Overall
Features8.8/10
Ease of Use8.9/10
Value8.7/10
Standout feature

Workflow steps can mix custom JavaScript code with integration actions and still keep the same trigger-to-output execution model.

Pipedream executes event-driven automations by running custom code alongside prebuilt integrations triggered from APIs and webhooks. It offers a workflow editor that routes inputs through steps such as HTTP requests, SDK calls, and data transforms, then emits results to downstream systems.

The platform’s API surface centers on programmatic workflow creation and execution, which supports extending beyond built-in connectors. It also includes operational controls for managing secrets and runtime behavior across projects.

Pros
  • +Event-driven workflows start from webhooks and schedule triggers
  • +Code and integrations run together within one workflow graph
  • +Secrets are scoped to environments to reduce credential sprawl
  • +Strong HTTP and API interaction coverage for custom systems
Cons
  • –Complex state and long-running flows require careful design
  • –Concurrency control needs explicit configuration to avoid bursts
  • –Governance features like RBAC and audit trails are less enterprise-mature
  • –Thick SaaS-specific connectors can be uneven across categories
Use scenarios
  • IT operations and support teams

    Route incident alerts to ticket tools

    Incidents reach correct process

  • Developers and platform teams

    Process webhook events with routing

    Responsive actions from events

Show 2 more scenarios
  • AI application developers

    Call APIs after model outputs

    AI decisions trigger tools

    Pass AI outputs through code and trigger operational API actions inside an event-driven flow.

  • Sales operations teams

    Enrich leads before CRM updates

    More complete lead records

    Fetch enrichment data from external APIs, then update sales and marketing records from workflow results.

Best for: Fits when teams need event-driven automation with code-level control over API workflows.

#3

MuleSoft

API-first

API-led integration platform providing Anypoint Platform for connecting applications, data, and devices with automation.

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

API-led management with RAML-backed contracts tied to Mule runtime deployment and governance controls.

MuleSoft is an enterprise automation and integration platform focused on API-first connectivity and process orchestration. It centralizes integration contracts through RAML and deploys Mule applications that can be triggered by HTTP, webhooks, schedulers, or message events.

Its data handling centers on consistent transformations with repeatable components, which helps large organizations standardize integration logic across teams. MuleSoft also provides governance controls for access, deployment, and auditability across environments.

Pros
  • +API-led governance using reusable RAML contracts across integration teams
  • +Strong orchestration via Mule runtime flows with consistent error handling
  • +Hybrid deployment options for cloud connected services and on-prem systems
  • +Centralized monitoring of runtimes and connectors for faster incident triage
Cons
  • –Automation depends on designing flows and connectors in the Mule runtime model
  • –Governed environments require disciplined setup to avoid broken deployments
  • –Cross-team changes to shared contracts can slow down release cycles
  • –Operational overhead rises with multiple environments and CI pipelines
Use scenarios
  • Integration engineers

    Trigger async workflows from system events

    Workflows react to events

  • API architects

    Standardize API contracts across teams

    Consistent API exposure

Show 2 more scenarios
  • Enterprise integration teams

    Connect cloud apps to on-prem services

    Hybrid system workflows connected

    Orchestrate interactions between cloud applications and on-premises services in one process flow.

  • IT operations teams

    Trace flows across runtime environments

    Better visibility into health

    Monitor execution, investigate errors, and trace transactions that span multiple systems.

Best for: Fits when enterprises need governed API-led automation across hybrid systems and multiple teams.

#4

Appian

enterprise

Low-code automation platform unifying process orchestration, case management, and RPA within a single environment.

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

Appian case management models complex, evolving work with consistent process state and UI behavior.

Appian targets process-centric automation where business workflows must call back-end services and enforce approval logic. The system exposes integration points such as REST APIs for starting processes and interacting with process artifacts, plus event-driven triggers used to route work. A strong fit appears when teams need consistent workflow execution across many departments and want shared components for forms, routing, and case handling.

Pros
  • +End-to-end workflow orchestration with process execution controls
  • +REST APIs support process starts and data interactions
  • +Reusable component approach for forms and process UI
  • +Governance features for roles and execution traceability
Cons
  • –High-scale performance depends on disciplined process and data design
  • –Complex cases require strong Appian configuration knowledge
  • –Integration projects can require more API engineering than expected
  • –UI and process changes can slow after governance hardening
Use scenarios
  • Operations transformation teams

    Standardize request-to-resolution workflows

    Fewer handoffs, faster resolution

  • Enterprise integration engineering

    Expose processes via service endpoints

    Clean contract-based integrations

Show 2 more scenarios
  • Compliance and governance teams

    Provide traceable automation execution

    More explainable automation

    Apply role-based access and use execution history to audit workflow outcomes.

  • Customer support organizations

    Run SLA-aware agent assist flows

    More consistent agent decisions

    Build human-in-the-loop case stages tied to service calls and escalation rules.

Best for: Fits when enterprises need governed workflow automation integrated to many systems.

#5

Camunda

API-first

Open-source process orchestration engine automating complex business workflows using BPMN standards.

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

BPMN execution with persisted instance state enables reliable progression and recovery for long-running workflows.

Camunda’s core value comes from BPMN execution with a runtime that persists process state and supports long-running flows. A task model lets applications claim, complete, and reassign work items while the engine tracks process variables through each step. Integration depth is driven by a documented API for starting and controlling process instances, plus connectors for common enterprise needs such as identity context and system integration points.

Pros
  • +BPMN-driven engine with durable state for long-running processes
  • +Task management APIs for claiming and completing work items
  • +Extensible integration via external workers and connector patterns
  • +Clear process visibility through instance and execution history
Cons
  • –BPMN modeling and variable design adds initial engineering overhead
  • –Operational complexity rises with multi-environment deployments and scaling
  • –Advanced customization often requires deeper engine and runtime knowledge
  • –More setup effort than low-code canvas workflow tools
Use scenarios
  • Enterprise operations teams

    Claims processing with approvals

    Fewer lost or duplicated cases

  • Integration engineering teams

    Order orchestration across systems

    Higher consistency across services

Show 1 more scenario
  • Workflow platform owners

    Onboarding with audit-friendly steps

    Repeatable onboarding operations

    Reusable process definitions manage variable flows and task lifecycles across environments.

Best for: Fits when enterprises need durable BPMN workflows with task routing and external system callbacks.

#6

Airtable Automation

SMB

Workflow automation inside a relational database and spreadsheet interface for building and triggering business processes.

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

Workflow logic built from Airtable field values, then applied to create or update records and related external systems.

Airtable Automation extends Airtable’s record-centric model with workflow triggers, action steps, and third-party integrations. It is distinct for how it maps automation inputs to fields, then uses those fields to drive conditional logic, updates, and external API calls.

Pros
  • +Field-aware triggers and actions map directly to Airtable records and views
  • +Conditional steps reduce automation sprawl by keeping logic inside one workflow
  • +Many common integrations available as direct actions without custom code
  • +Permissions propagate through workspace access, keeping record operations consistent
Cons
  • –Complex multi-system orchestration needs custom API calls and extra engineering
  • –Debugging failed steps can require re-checking execution history and input fields
  • –High-volume throughput depends on rate limits of connected services and APIs
  • –Governance controls are limited compared with enterprise workflow engines
Use scenarios
  • Teams managing process stages

    Run status changes on cards

    Process tracked with context

  • Research teams and analysts

    Link evidence and review statuses

    Traceable evidence tracking

Show 2 more scenarios
  • Operations planning teams

    Track approvals and schedules

    Shared plan with handoffs

    Maintain work orders with owners, approvals, dates, and capacity inputs through connected records.

  • Small business sales teams

    Coordinate pipeline and follow-ups

    Configurable client database

    Organize contacts, companies, deals, and tasks without adopting a fixed CRM schema.

Best for: Fits when teams need field-driven workflow automation inside Airtable with external API actions.

#7

Activepieces

SMB

Open-source no-code automation platform with a growing connector library and self-hosting support.

7.4/10
Overall
Features7.4/10
Ease of Use7.4/10
Value7.4/10
Standout feature

Custom piece development lets teams add new workflow activities without changing the core workflow engine.

Activepieces focuses on workflow orchestration through a low-code builder with a large catalog of integrations and a consistent execution model across connectors. Its core strength is an automation API surface that supports webhook triggers, REST-style actions, and custom pieces for extending beyond existing connectors.

Pros
  • +Extensible connector system supports custom pieces for nonstandard integrations
  • +Webhook triggers and API-driven actions fit event-driven orchestration patterns
  • +Environment-scoped configuration helps keep dev and production runs aligned
  • +Reusable components reduce duplication across teams and workflows
Cons
  • –Advanced governance features like deep RBAC controls can require extra setup discipline
  • –Headless execution operations depend on configured runners or deployment choices
  • –Complex multi-step workflows need careful error paths to avoid silent failures
  • –Some niche platforms rely on community or custom connectors for parity
Use scenarios
  • AI automation experimenters

    Use prompts and pass AI results

    AI-supported classification steps

  • Automation teams

    Share standardized reusable components

    Less duplicated integration logic

Show 2 more scenarios
  • SMB operations teams

    Route tasks by business rules

    Consistent handling of decisions

    Use conditions and branches to route records or notifications based on operational criteria.

  • Operations and IT teams

    Schedule recurring API jobs

    Repeatable API jobs

    Run scheduled API calls, polling flows, and data exchanges on defined intervals.

Best for: Fits when teams need governed workflow automation with strong integration extensibility and API-first triggers.

#8

Apache Airflow

API-first

Open-source workflow orchestration for scheduling and monitoring data pipelines using directed acyclic graphs.

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

Task instances track complete run state transitions and surface logs per step in the web UI.

Apache Airflow is a workflow orchestration system built around Python-defined Directed Acyclic Graphs that run scheduled and event-driven data pipelines. It distinguishes itself with a rich scheduler plus worker execution model, detailed task dependency tracking, and a UI that surfaces run state, retries, and logs per task.

Pros
  • +Task-level state history with clear dependency, retries, and log links
  • +Python DAG code enables versioned workflows and reusable operators
  • +Extensible operator and hook model supports many external systems
  • +Scheduler and worker separation supports scaling across executors
Cons
  • –Operational complexity rises quickly with non-default executors
  • –DAG correctness and idempotency require discipline in task code
  • –Large DAG graphs can slow parsing and increase UI overhead
  • –Fine-grained RBAC and audit coverage depend on deployment and auth setup
Use scenarios
  • IT operations teams

    Replace job chains with DAGs

    Centralized scheduling and history

  • Site reliability teams

    Run health checks with conditional actions

    Traceable service checks

Show 2 more scenarios
  • Integration and operations teams

    Coordinate file processing steps

    Ordered processing with retries

    Move and transform files through dependent tasks with retries and per-task execution logs.

  • Data reliability teams

    Validate data before downstream work

    Earlier detection of issues

    Schedule data availability and quality checks and trigger downstream actions based on results.

Best for: Fits when teams need code-defined workflow orchestration with strong scheduling, dependency management, and extensible integrations.

#9

Bardeen

SMB

Browser extension-based automation tool with AI-assisted workflow generation and scraper capabilities.

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

Task capture with an automation editor that converts recorded interactions into maintainable workflow steps.

Bardeen captures cross-application tasks and turns them into repeatable automations without building full RPA bots from scratch. Its core workflow builder centers on a task capture and a visual automation editor that generate callable steps tied to real user actions.

Pros
  • +Task capture converts user actions into repeatable automation steps quickly
  • +Workflow editor supports reusable building blocks for multi-step processes
  • +Integrations cover common SaaS workflows with connector-based field mapping
  • +Execution history supports troubleshooting by showing what ran per step
Cons
  • –Advanced headless automation requires more design than UI-only workflows
  • –Governance controls rely more on workflow organization than granular RBAC depth
Use scenarios
  • Business operations teams

    Route spreadsheet inputs to apps

    More consistent request handling

  • Teams with approval workflows

    Pause for human approval steps

    Oversight with repeatable execution

Show 2 more scenarios
  • Analysts and client reporting teams

    Refresh recurring reports automatically

    Less effort to refresh

    Collect information from dashboards or websites and send results into subsequent reporting steps.

  • Operations and admin teams

    Automate repetitive form entry

    Faster recurring browser tasks

    Reduce copy-and-paste work by filling forms and updating records across browser applications.

Best for: Fits when teams need fast, connector-driven automations across SaaS tools without building traditional RPA bots.

#10

Parabola

SMB

Visual workflow builder focused on data automation, transformation, and spreadsheet replacement pipelines.

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

Parabola’s data transformation workflow model makes spreadsheet-like parsing, validation, and enrichment the primary automation primitive.

Parabola is a low-code workflow automation tool focused on data preparation and operational handoffs between systems. Workflows run on a visual canvas that turns spreadsheet-like inputs into structured transformations, routing, and API-driven actions.

Pros
  • +Visual workflow canvas that turns messy inputs into consistent structured outputs.
  • +Connector-driven actions reduce custom integration work for common SaaS targets.
  • +Reusable components help standardize parsing, validation, and routing steps.
  • +Clear separation between data transforms and outbound actions improves maintainability.
Cons
  • –Governance depth and RBAC granularity are weaker than enterprise orchestration suites.
  • –Complex multi-step enterprise workflows can require careful test coverage to avoid hidden edge cases.
Use scenarios
  • Finance and operations teams

    Reconcile recurring invoice files

    More consistent reconciled data

  • Business operations teams

    Schedule recurring report preparation

    Less manual report preparation

Show 2 more scenarios
  • Data operations teams

    Transform CSV files end-to-end

    Repeatable file processing

    Ingest CSV and similar files, apply transformations, and export reshaped outputs without extensive scripting.

  • Sales operations teams

    Clean and standardize CRM exports

    Consistent records for import

    Reshape, validate, and enrich exported lead or customer records for import or downstream processing.

Best for: Fits when teams need visual data-to-action automation across SaaS apps with minimal custom code and fast iteration.

Conclusion

After evaluating 10 professional automation software, Nintex 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
Nintex

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 Professional Automation Software

Which tool is better for BPMN-style long-running workflow orchestration with persisted state, Camunda or Appian?
Camunda runs BPMN models with durable execution so instance state persists across restarts and task progression survives failures. Appian focuses on governed workflow orchestration with case management models that keep process state and UI behavior consistent for enterprise work, but its differentiator is case modeling rather than BPMN engine semantics.
How do enterprises connect automation workflows to external systems with APIs and event triggers in MuleSoft and Pipedream?
MuleSoft builds integration contracts and deploys API-led services that trigger from HTTP, webhooks, schedulers, or message events inside the Mule runtime. Pipedream executes event-driven automations by running custom code steps with API and webhook triggers, then emits outputs through downstream steps in the same workflow run.
How does SSO and access control typically work for governed deployments in Appian and Nintex?
Appian ties governed runtime behavior to roles and execution context, which restricts who can start, control, and view workflow executions. Nintex uses governance controls for deployments and audit visibility so organizations can manage releases and track what ran across environments.
What breaks if data model alignment is inconsistent when automating record-based workflows in Airtable Automation and Parabola?
Airtable Automation maps automation inputs to Airtable fields, so schema changes that rename fields or alter data types can break conditional logic and record updates. Parabola relies on spreadsheet-like inputs transformed into structured outputs, so inconsistent column formats or missing validation steps can cause enrichment and routing to produce incorrect downstream payloads.
When should an enterprise choose workflow orchestration with reusable components in Nintex instead of custom integration logic in Activepieces?
Nintex fits when reusable workflow components standardize custom logic across many processes and environments under governance controls. Activepieces fits when extensibility comes from developing custom pieces that add new workflow activities without changing the core engine, while connectors and a consistent execution model handle most orchestration needs.
How does data migration and workflow portability get handled when moving automations between environments in Camunda and Apache Airflow?
Camunda persists workflow instances and exposes an API surface for managing tasks and state transitions, which supports moving control over long-running processes while preserving execution context. Apache Airflow keeps orchestration logic as Python-defined DAGs with configuration-driven deployments so the scheduler and workers can be redeployed with different environment settings while retaining task dependency behavior.
Where does exception handling and recovery differ between Camunda and Apache Airflow when tasks fail mid-run?
Camunda’s BPMN execution model uses persisted instance state so workflows can recover to the correct progression after interruptions and retries. Apache Airflow tracks run state transitions and retries per task instance with logs surfaced in its UI, so operators can diagnose failures at the task boundary rather than as a single persisted process state.
How does extensibility work if an organization needs custom steps beyond prebuilt connectors in Activepieces and Bardeen?
Activepieces supports custom piece development so teams can add new workflow activities through its extensibility mechanism while keeping the same webhook-triggered execution model. Bardeen focuses on task capture and a visual editor that converts recorded interactions into callable steps tied to user actions, so extensibility is built around maintainable automation steps rather than adding brand-new connector types.
Which tool is more appropriate for data preparation and operational handoffs where spreadsheets are the primary input, Parabola or Bardeen?
Parabola is built around transforming structured inputs on a visual canvas into validated outputs, routing decisions, and API-driven actions, so spreadsheet-like parsing and enrichment stay central. Bardeen centers on task capture from cross-application user actions and connector-driven moves between SaaS systems, so it is less suited to deep transformation pipelines starting from tabular data.

Tools reviewed

Primary sources checked during evaluation.

Referenced in the comparison table and product reviews above.

How to Choose the Right Professional Automation Software

We look for differences that show up when you try real automations: how the platform handles process state, how it lets teams control what runs, and how reliably it logs outcomes under failure. Those details show up most in Automation recovery, Deployment controls, and Audit log quality.

State tracking through retries

Why it matters

State tracking through retries determines whether your automation keeps context when something goes wrong. If it loses state, you get duplicate work or partial updates that are hard to fix.

How to check

Build a demo workflow that fails once (for example, force a step to return an error) and then re-run it. Ask the vendor to show how it records the current stage and what it does next—restart, resume, or rollback.

Automation recovery on failure

Why it matters

Automation recovery on failure matters because professional automation usually needs predictable behavior during exceptions. Recovery that’s vague turns small errors into repeated manual fixes.

How to check

Trigger at least two failure modes in a test flow, such as invalid input and a temporary external outage. Confirm whether the tool retries, stops, or reroutes—and where you can adjust the rule for each case.

Deployment controls for changes

Why it matters

Deployment controls for changes decide how safely updates reach production runs. Better controls reduce “version drift” where different runs use different logic.

How to check

On the demo, look for a way to promote a workflow from a test space to a production space. Ask what happens to in-flight runs when you publish an update, and whether you can keep a specific version for a period.

Audit log for every run

Why it matters

Audit log for every run is how teams verify what happened without guessing. Strong logs include run context, inputs/outputs references, and clear failure reasons.

How to check

Run a short automation and then open the run history. Check that you can see timestamps, step-level results, and a readable error message; ask whether logs export cleanly for reporting.

RBAC and provisioning for operators

Why it matters

RBAC and provisioning for operators control who can change automations and who can only run or view results. Without it, teams either over-share access or slow down approvals.

How to check

Ask for a demo with at least three roles (viewer, operator, admin). Confirm which actions each role can take, and whether you can add/remove users and keep permissions consistent across environments.

Red flags

  • Failure recovery is unclear or only works when someone manually edits the run.
  • You can’t trace a run end-to-end in the audit log.
  • Promoting changes overwrites in-flight runs without a clear version policy.

Fit by situation

  • If you need to resume work after intermittent errors, prioritize state tracking through retries.
  • If you run critical flows that must behave predictably during exceptions, prioritize automation recovery on failure.
  • If multiple teams update and operate automations, prioritize RBAC and provisioning for operators.
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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