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AI In IndustryTop 10 Best Bot Creator Software of 2026
Compare bot creator software options like Rasa, Botpress, and Voiceflow with tradeoffs for building, deploying, and managing chatbots.
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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Rasa is the best choice for teams that need a controllable orchestration layer with custom NLU, since its reusable action server pattern keeps business logic outside the model and wired to REST webhooks for each conversation step, whereas Voiceflow fits SMBs who want a component-style visual flow that stays editable and can hand off to webhook actions.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Rasa
The Rasa action server pattern lets business logic run outside the model and stay reusable across conversations.
Best for: Fits when teams need a controllable bot orchestration layer with custom NLU and external action routing.
Botpress
Editor pickCode and webhook actions run at specific flow points, enabling deterministic orchestration between UI steps and external systems.
Best for: Fits when teams need visual control plus webhook-driven integrations for production chatbots.
Voiceflow
Editor pickComponent-based flow building and deployment packaging that keeps dialogue logic editable and reusable across projects.
Best for: Fits when teams need a visual conversation flow editor with webhook-driven orchestration.
Comparison Table
Rasa
developerOpen-source framework for building contextual AI assistants.
The Rasa action server pattern lets business logic run outside the model and stay reusable across conversations.
Rasa targets teams that need a code-first chatbot framework with explicit control over the dialogue engine. It combines an NLU pipeline for intent classification and entity extraction with a conversation model for multi-turn dialogue management.
Bot orchestration supports action hooks that call external services through webhooks, which fits event-driven and tool-calling patterns. Rasa also exposes connectors for message channels and keeps conversation data structured for analytics and debugging.
- +Conversation behavior is controlled in code and training artifacts
- +Action hooks map directly to external service calls via REST webhooks
- +Message channel adapters support consistent logic across channels
- +Fallback behavior and conversation analytics help debug real user paths
- –Production readiness requires engineering effort for deployment and scaling
- –Complex NLU quality often needs custom training and iterative annotation
- –Omnichannel operations need extra connector configuration per channel
- –Advanced guardrails require additional policy work and integrations
Product and platform engineering teams
Reuse intent and dialogue across channels
Shared conversational behavior across channels
Organizations with domain-specific assistants
Customize NLU for specialized business terms
Assistants tailored to specialized vocabulary
Show 2 more scenarios
Organizations with strict deployment controls
Run assistant with controlled runtime behavior
Greater control over runtime behavior
Teams can manage how the dialogue engine runs to align with internal operational and data handling requirements.
Customer service and support operations teams
Automate multi-turn support journeys
More consistent automated handling
Multi-turn dialogue management classifies requests and triggers actions to handle common issues consistently.
Best for: Fits when teams need a controllable bot orchestration layer with custom NLU and external action routing.
Botpress
developerOpen-source conversational AI platform with visual bot builder.
Code and webhook actions run at specific flow points, enabling deterministic orchestration between UI steps and external systems.
Botpress centers on a conversation flow editor that models dialogue steps as nodes connected by execution paths, which helps enforce deterministic routing. The system supports action execution via webhooks and custom code so message handling can call external services and return structured results. Channel connectivity is handled through adapter-style integration points so the same bot logic can reach different front ends.
A key tradeoff is that the visual flow becomes harder to maintain as logic expands into many edge cases and branching paths. Botpress fits best when teams want governed conversation structure with external system calls for user authentication, ticket creation, or knowledge lookups.
- +Visual flow editor maps execution paths clearly for complex dialogues
- +REST webhook integration supports external actions with structured payloads
- +Conversation analytics and transcript export support iterative flow tuning
- +Custom code hooks cover cases that do not fit standard actions
- –Large branching graphs can become difficult to refactor safely
- –Complex governance requires disciplined versioning and release practices
Customer support operations
Route tickets from chat to CRM
Higher deflection with fewer handoffs
Revenue operations teams
Qualify leads with structured conversation steps
Cleaner pipeline data for sales
Show 1 more scenario
Enterprise IT helpdesk
Request access using authenticated backend workflows
Faster request handling
Bot steps ask for details then call authenticated endpoints to start and track provisioning requests.
Best for: Fits when teams need visual control plus webhook-driven integrations for production chatbots.
Voiceflow
SMBVisual canvas for designing, prototyping, and building conversational AI.
Component-based flow building and deployment packaging that keeps dialogue logic editable and reusable across projects.
Voiceflow creates conversational AI flows with a visual builder that compiles into deployable bot logic. It supports dialogue orchestration with branching, stateful variables, and multi-channel message adapters.
The workflow layer integrates webhooks for external actions and connects content sources through knowledge components for retrieval-based responses. Voiceflow also provides conversation analytics and exportable transcripts to validate bot behavior across iterations.
- +Visual flow editor maps dialogue state and branching without code
- +Webhook actions enable external systems and custom logic routing
- +Conversation analytics track outcomes and revisit decisions during tuning
- +Reusable components speed maintenance across related bot flows
- –Advanced NLU pipeline controls are limited versus code-first frameworks
- –Complex omnichannel setups require careful configuration of channel adapters
Product and developer teams
Trigger backend operations via webhooks
Agent can trigger business actions
Service operations and contact center teams
Design handoff when the bot fails
More controlled transitions to humans
Show 2 more scenarios
Contact center and telephony teams
Build voice flows for inbound calls
Structured voicebot routing for calls
Voice flow design supports call handling, routing, intent capture, and controlled fallback paths.
Software product teams
Embed an assistant inside product apps
Embedded assistant aligned to workflows
In-app conversational flows help users navigate features, answer questions, and complete tasks.
Best for: Fits when teams need a visual conversation flow editor with webhook-driven orchestration.
Chatfuel
SMBVisual chatbot builder for Facebook Messenger and Instagram.
Conversation flow editor that drives branching and webhook actions from a single visual configuration surface.
Chatfuel’s core workflow uses a conversation flow editor that connects chat steps to triggers and conditions, so most bot behavior can be configured without writing code. External integrations typically happen through webhook calls that pass user context to downstream services and route responses back into the flow. Conversation analytics help measure containment and understand where users drop or stall inside the configured routes.
- +Visual flow editor speeds up building multi-branch chat experiences
- +Webhook-based actions integrate external systems without separate backend coding
- +Conversation analytics support bot iteration using real chat outcomes
- +Channel-focused deployment reduces time spent on connection plumbing
- –Deep custom dialogue orchestration is harder than in code-first frameworks
- –Complex fallback and exception handling needs careful flow design
- –Data-driven personalization beyond captured attributes can require extra webhooks
- –Governance controls may lag behind enterprise RBAC and audit workflows
Marketing operations teams
Lead qualification through guided chat flows
Higher bot-to-lead handoff rate
Customer support teams
Order status assistant with escalations
Fewer tickets for routine checks
Show 2 more scenarios
Operations teams
Internal request intake bot
Standardized intake and faster processing
Collect structured inputs in flows and call internal services through webhooks for fulfillment.
Growth teams
Campaign-driven onboarding in chat
Quicker iteration on engagement flows
Deploy new message sequences per campaign and track conversation results in analytics dashboards.
Best for: Fits when teams need fast visual flow automation with webhook actions and measurable chat outcomes.
ManyChat
SMBChatbot platform for Messenger, Instagram, SMS, and WhatsApp.
Flow steps can call webhooks directly, which makes backend actions part of the same conversational sequence.
ManyChat centers on a flow builder where nodes define message steps, delays, branching logic, and webhook calls to external systems. ManyChat’s execution model supports event-driven interactions when users message the bot, plus scheduled and trigger-based workflows that send follow-ups. The automation surface includes action routing to webhooks so bot steps can create, update, or query data in other services.
- +Visual flow editor for branching, delays, and webhook-triggered actions
- +Human handoff tools for live chat situations with ongoing conversations
- +Channel-focused bot deployment so flows can reach multiple chat surfaces
- +Webhook integration supports custom business logic outside the editor
- –Complex dialogue state needs webhook or external orchestration work
- –Advanced NLP control is limited compared with intent and entity tooling
Customer support teams
Deflect FAQs and escalate to agents
Faster resolution with clear handoff
Ecommerce operations teams
Order status and shipping updates
Lower support volume for order queries
Show 2 more scenarios
Revenue operations teams
Lead qualification via conditional messaging
More accurate lead routing
Branching questions gather details and call CRM webhooks to update records.
Community managers
Event reminders and rules-based engagement
Higher repeat engagement
Trigger-based messages and conditional paths guide users to resources and next actions.
Best for: Fits when messaging-led bot workflows need quick iteration plus webhook integration.
Pandorabots
developerChatbot hosting and development platform using AIML.
AIML-style pattern response rules with session handling for predictable multi-turn conversations.
Pandorabots is a bot creator focused on programmable conversational agents with an API-first workflow and scriptable dialogue behavior. It supports bot sessions, message handling endpoints, and server-side conversation logic so external services can act on user messages.
- +API-first bot interaction model for building custom chat front ends
- +Scriptable conversational responses with deterministic pattern matching
- +Session-aware conversation handling for multi-turn flows
- +Conversation transcript support for reviewing behavior across runs
- –Dialogue creation can feel code-centric without a modern flow editor
- –Automation around handoff, RAG, and guardrails requires external integration work
- –NLU feature set is narrower than managed intent and entity builders
- –Scaling conversational throughput needs careful endpoint and state design
Customer support automation teams
Route tickets via scripted conversation steps
Faster triage and consistent replies
Community moderation operators
Detect policy-violating phrases with patterns
Reduced escalation to humans
Show 2 more scenarios
Healthcare appointment coordinators
Collect availability using dialogue logic
Lower no-show appointment rates
Maintains conversation sessions and returns structured interactions for downstream scheduling.
Product research conversation teams
Run transcript reviews for iterations
Improved containment and resolution
Supports evaluation loops that refine dialogue behavior based on conversation transcripts.
Best for: Fits when deterministic, pattern-based conversational behavior needs tight control and external system orchestration.
Kore.ai
enterpriseEnterprise conversational AI platform for building virtual assistants.
Built-in bot orchestration that connects dialog flow routing directly to action handlers exposed via webhook and API calls.
Kore.ai targets enterprise conversational AI builder workflows that need operational controls, not only chat UI design.
- +Admin RBAC and audit logging support controlled multi-team bot operations
- +Conversation flow orchestration ties dialog logic to external API and webhook actions
- +NLU intent and entity configuration supports predictable classification and extraction
- +Omnichannel adapters help the same bot logic run across messaging surfaces
- –Advanced routing and handoff workflows require careful configuration discipline
- –Webhook-based integrations can become complex when many action variants share state
- –Iterating NLU models often needs an extra review loop before production rollout
- –Large bot projects can feel heavy without a clear modular structure for flows
Automation developers and business owners
Convert requests into structured actions
Interactions initiate operational steps
Enterprise customer service teams
Resolve routine questions with virtual agents
Routine requests resolved automatically
Show 2 more scenarios
Knowledge management teams
Answer from approved enterprise content
Answers tied to approved information
Curated knowledge grounding produces controlled responses based on policies and procedures.
Employee service, HR, and IT teams
Guide employees through repeatable workflows
Fewer manual handling events
Consistent service workflows answer internal questions and direct guided request steps.
Best for: Fits when enterprise teams need governed bot orchestration with API-driven actions and multi-admin control.
Cognigy
enterpriseConversational AI automation platform for enterprise contact centers.
Agent-style bot orchestration with routing and action execution tied to each step in the conversation flow.
Cognigy positions its bot creator around an agent-style orchestration model that connects conversation routing to execution steps. Its conversation flow editor is designed for end-to-end bot building with configurable actions, integrations, and channel adapters for deployment.
- +Strong orchestration model that couples routing decisions with execution steps
- +REST webhook API supports custom actions and external workflow calls
- +Operational reporting with conversation transcripts for debugging and QA review
- +Extensibility supports integrating custom services into bot flows
- –Conversation design can become complex for large multi-path flows
- –External NLU integration requires careful alignment of intents and entities
- –Channel setup and adapter configuration can add upfront engineering time
- –Governance controls demand disciplined versioning across environments
Digital support and CX teams
Deploy scripted chat and messaging agents
Consistent automated handling
Customer service operations teams
Automate multi-turn service calls
Routine calls handled automatically
Show 2 more scenarios
Conversation analysts and quality teams
Tune performance using transcript review
Improved dialogue coverage
Transcript export and call analytics help identify intent gaps and optimize dialogue coverage.
Contact center and telephony teams
Modernize menu-driven IVR with intents
More flexible caller self-service
Intent-based voice conversations route callers through business workflows instead of fixed phone trees.
Best for: Fits when mid-size teams need orchestration-heavy bots with external system actions and strong conversation traceability.
Botsify
SMBChatbot builder for websites, Facebook, and WhatsApp.
Webhook-backed action routing that turns conversation decisions into external workflow calls without code changes to the bot flow.
Botsify is a bot creator solution that uses a conversation flow editor to design intent-driven chat experiences. It connects conversational logic to external actions through a webhook and automation layer for event-triggered responses.
- +Flow-based builder reduces friction for assembling multi-step conversation paths
- +Webhook-driven actions support connecting the bot to existing services
- +Analytics and transcript export support post-deployment iteration
- +Channel deployment options reduce rework when serving end users
- –Advanced NLU customization options are limited versus framework-level builders
- –Tool-calling style integrations depend on webhook wiring for each action
- –Complex branching logic can become hard to manage at scale
- –Governance controls like fine-grained RBAC are not central to the workflow
Customer support operations teams
Automate repetitive intent-based support replies
Faster resolution for common issues
Marketing automation teams
Qualify leads through chat intent flows
Higher quality lead handoffs
Show 2 more scenarios
Product operations teams
Drive event-based onboarding through bots
Reduced onboarding friction
Use webhook-connected responses to react to user events and guide setup steps across channels.
Operations analysts and QA
Review bot transcripts and analytics
Fewer broken intents in production
Export transcripts and monitor conversation outcomes to improve flow coverage and reduce failure paths.
Best for: Fits when teams need a visual bot builder with webhook automation for a specific use case.
ChatBot
SMBChatbot builder for websites, Messenger, and Slack.
Built-in webhook-driven action steps inside the bot flow reduce the need for custom bot orchestration code.
ChatBot from chatbot.com targets conversational AI builder workflows with a visual bot designer and a deployment path for chat interfaces. It pairs a conversation flow editor with automation hooks like webhooks and scripted actions for connecting external systems.
- +Visual conversation flow editor speeds up bot iteration without full code ownership
- +Webhook and action integrations cover common external system calls
- +Conversation transcript viewing supports QA of dialogue outcomes
- +Publish configuration supports multiple chat surfaces from one bot design
- –NLU depth for complex intent sets can feel limited versus developer-first frameworks
- –Automation logic can become hard to trace as flow graphs grow
- –Guardrail and moderation controls require careful manual setup discipline
- –Advanced orchestration patterns need external services to fill gaps
Customer support operations teams
Deflect routine tickets with guided chat flows
Fewer tickets, faster first responses
Revenue operations teams
Qualify leads and sync CRM records
Higher lead conversion rates
Show 2 more scenarios
IT automation engineers
Integrate bots with internal services
Reduced manual workflow work
Builders connect conversation steps to external APIs using webhook calls and custom action logic.
Marketing teams
Publish consistent bots across channels
Consistent messaging across touchpoints
Teams reuse the same conversation logic while validating transcripts from each chat surface.
Best for: Fits when teams need a visual bot builder with webhook-driven integrations and transcript-based QA.
Conclusion
After evaluating 10 bot creator software, Rasa 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 Bot Creator Software
Bot creator software includes a conversation flow editor, webhook-driven action steps, and an orchestration layer for routing dialogue decisions to external systems. This buyer guide covers Botpress, Chatfuel, ManyChat, Rasa, Voiceflow, Kore.ai, Cognigy, ChatBot, Botsify, and Pandorabots.
The standout differences show up in how each tool binds conversation steps to external execution. Botpress and Chatfuel emphasize deterministic orchestration from visual flows with webhook actions at specific points, while Rasa shifts business logic into reusable action servers that run outside the model. The guide also tracks where builders hit friction, such as refactoring branching graphs in Botpress and production scaling work in Rasa.
What Bot Creator Software controls in a conversational workflow
Bot creator software is the tooling that turns dialogue design into executable behavior by coupling a bot designer UI or flow editor to action endpoints and conversation state. It typically includes visual branching or a code-led dialogue engine plus REST webhook integration so conversation decisions can trigger external workflows.
Botpress pairs a visual flow editor with webhook actions positioned at flow points so execution is traceable across complex dialogue paths. Chatfuel also uses a single visual configuration surface for branching and webhook actions, but it shifts deeper orchestration complexity into careful flow design as graphs grow. Rasa differentiates further by making action server logic reusable across conversations through REST webhook style action hooks, which places more deployment responsibility on the team building the bot.
Bot Creator Software controls that determine orchestration quality
Bot creator software becomes production-ready when the flow editor and action layer make execution points explicit so external calls map to specific conversational turns. Builder teams also need enough automation and traceability to prevent fixes from breaking unrelated branches.
Flow-to-execution binding at specific points
Botpress runs webhook actions at defined flow points so orchestration stays deterministic as dialogue graphs grow. Chatfuel also binds branching and webhook actions from one visual configuration surface, but larger exception paths require tighter flow design to keep intent routing understandable.
Reusable action layer outside the dialogue engine
Rasa uses an action server pattern so business logic runs outside the model and can be reused across conversations via action hooks. This split makes execution modular, while Rasa shifts deployment and scaling responsibility to the engineering layer that hosts the action server.
Admin governance for multi-team bot operations
Kore.ai includes admin RBAC and audit logging, which supports controlled operations when multiple teams publish and modify bots. Cognigy couples routing decisions with execution steps for traceability, but large multi-path designs can become complex enough to require strict governance around changes.
Webhook-first integration workflow for external systems
ManyChat lets flow steps call webhooks directly so backend actions become part of the same conversational sequence. Botsify similarly uses webhook-backed action routing, but action execution quality depends on wiring each workflow action endpoint as the flow grows.
Conversation traceability as a first-class workflow
Cognigy’s agent-style orchestration ties routing and action execution to each step, which makes conversation step-level traceability a core design feature. ChatBot also emphasizes transcript-based QA, and the webhook-driven action steps reduce custom orchestration code even as large flow graphs can make tracing harder.
Choose by orchestration style, integration surface, and operations control
Bot creator software choices succeed when the execution model matches the team’s operational habits for releases, incident response, and external workflow maintenance. Visual flow tools fit teams that want execution binding to stay in the designer, while code-led frameworks fit teams that want logic to stay in a reusable service.
Pick the execution binding model that matches change control
If production reliability depends on making webhook execution points explicit inside the flow editor, Botpress matches that model by running code and webhook actions at specific flow points. If the team prefers a single visual configuration surface and can maintain flow design discipline as branching expands, Chatfuel also binds branching and webhook actions from the visual editor.
Decide whether business logic should live in an action server
If business logic must be reusable across conversations and kept outside the model, Rasa fits because the action server pattern moves logic into REST-driven action hooks. If dialogue logic must stay editable and reusable across projects in a packaged flow workflow, Voiceflow fits because component-based flow building stays tied to editable dialogue logic and webhook-driven orchestration.
Match integration workflow to how teams wire backend actions
If backend actions should execute as part of the same conversational sequence, ManyChat supports direct flow steps that call webhooks. If the bot’s automation needs are narrower and workflow calls can be mapped as distinct webhook actions from a visual builder, Botsify supports webhook-driven action routing without changing bot flow code.
Select governance features based on who publishes and who reviews changes
If multiple admins and teams require controlled publishing with traceable changes, Kore.ai provides admin RBAC plus audit logging so operations stay governed. If traceability is more valuable than governance tooling, Cognigy’s routing and action execution model ties each step to execution, and conversation design complexity becomes the main constraint.
Plan for production scaling and channel complexity early
If production readiness requires engineering work for deployment and scaling, Rasa’s setup and iteration loop around training and deployment becomes a key consideration. If deployment spans many channels, Voiceflow notes complex omnichannel setups as a configuration workstream because channel adapter configuration needs careful alignment.
Who benefits from each Bot Creator Software approach
Teams should choose tools where the orchestration style matches how they build and run bots. The cards show distinct fit cases based on whether execution stays inside a flow graph, moves into reusable action services, or requires enterprise governance controls.
Teams building production chatbots with deterministic flow execution and webhook actions
Botpress fits teams that want visual flow control plus code and webhook actions run at specific flow points. The deterministic binding reduces ambiguity when fixing complex dialogue paths.
Growth and automation teams that need rapid visual flow iteration with measurable outcomes
Chatfuel fits when building multi-branch chat experiences quickly using its conversation flow editor plus webhook-based actions. Teams gain a single configuration surface but must design carefully for complex fallback and exception handling.
Engineering teams that need reusable action logic outside the dialogue engine
Rasa fits teams that want business logic in an action server pattern so action hooks map directly to REST webhooks. The tradeoff is engineering effort for deployment and scaling.
Enterprise bot programs that require RBAC and audit logging for multi-admin operations
Kore.ai fits teams that manage multiple admins and need governed bot orchestration. RBAC plus audit logging align with controlled multi-team bot operations, and webhook-driven actions tie routing to external API calls.
Mid-size teams building orchestration-heavy bots with step-level traceability
Cognigy fits when routing and execution must be tied to each step for stronger conversation traceability. The tradeoff is that large multi-path flows can become complex enough to require structured conversation design.
Common Bot Creator Software pitfalls that break orchestration
Most failures come from mismatching orchestration complexity to the tool’s refactoring and deployment model. Flow graphs that expand without a release discipline create fragile execution paths, and action-server patterns fail when ownership for deployment is unclear.
Refactoring large branching graphs without a release process
Botpress warns that large branching graphs can become difficult to refactor safely, so versioning and release discipline must be planned for. Chatfuel has similar risks at scale because deep orchestration becomes harder than code-first frameworks.
Treating action servers as optional when business logic must be reusable
Rasa requires engineering effort for deployment and scaling, and skipping that operational ownership leads to unstable production behavior. The modular action server pattern only helps when action hooks and external service calls remain reliably hosted.
Assuming advanced routing and handoff workflows work without configuration discipline
Kore.ai notes that advanced routing and handoff workflows require careful configuration discipline, especially when action variants share state. Without structured configuration, webhook integrations can become complex enough to slow debugging.
Overloading visual flows for deep orchestration without planning for tracing
ChatBot highlights that automation logic can become hard to trace as flow graphs grow, even when webhook-driven steps reduce custom orchestration code. As flows expand, transcript-based QA must be paired with disciplined flow structure.
How We Selected and Ranked These Tools
We evaluated bot creator software cards across features, ease, and value, then used the reported overall score to rank. Features carried a 40% weight, while ease and value each carried a 30% weight to reflect builder speed and operational payoff.
We gave Botpress the highest priority because its overall score and the tool’s flow-anchored webhook action model directly support deterministic orchestration in complex dialogues. We kept the ordering consistent with the relative overall scores, where Chatfuel and ManyChat lead after Botpress for visual flow and webhook action usability.
Frequently Asked Questions About Bot Creator Software
Which tool fits teams that need deterministic orchestration with a flow editor plus code-level hooks?
How do REST webhook actions get wired into conversational steps across Botpress, Chatfuel, and ManyChat?
When should a team choose Rasa over Microsoft Copilot Studio or Google Dialogflow for NLU and dialogue-state control?
What breaks if a builder relies on a visual workflow tool but needs a reusable action server pattern?
Which platform is strongest for governed multi-admin bot operations with RBAC and audit logging?
How do transcript export and conversation analytics support QA loops in Voiceflow, Botpress, and Cognigy?
When a bot must hand off to a human agent, where does the handoff logic usually live in ManyChat versus Kore.ai?
What integration approach is most practical when the bot must publish to multiple chat channels from one configuration?
Where does prompt-injection risk management belong when using a bot creator that supports knowledge components or RAG-style retrieval?
Tools reviewed
Primary sources checked during evaluation.
Referenced 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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