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QwryAI
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Quick StartWelcome to QwryAI

Get started with QwryAI and discover how to build an AI support assistant trained on your business data.

Quick StartBuild Your First AI Agent

Create a support agent from your website, review its knowledge, and publish it to your site.

Quick StartBest Practices

Use these practices to improve answer quality, reduce failed handoffs, and keep your chatbot useful over time.

Quick StartResponse Quality

Improve how accurately and helpfully your chatbot answers customers.

AI Agent ManagementPlayground

Test and optimize your chatbot before deployment.

AI Agent ManagementData Sources

Add, train, and maintain the knowledge your agent uses to answer customer questions.

AI Agent ManagementDeploy

Add your chatbot to a website or ecommerce storefront.

AI Agent ManagementSettings

Choose the Copilot model, set a rate limit, and block spam and countries.

AI Agent ManagementEmail Settings

Configure email handoff and reply behavior for chatbot support workflows.

AI Agent ManagementLead Forms

Collect customer details directly inside the chat experience.

AI Agent ManagementIntegrations

Connect QwryAI with external platforms and business tools.

AI Agent ManagementDeveloper & Agent Access

Connect QwryAI to external AI tools, MCP clients, scripts, and CLI workflows.

AI Agent ManagementBackstage

Ask questions about your agent and workspace in a dedicated internal conversation.

AI Agent ManagementInstructions

Define your agent's behavior, guardrails, model, and response style.

AI Agent ManagementProcedures

Write a repeatable customer process with instructions, conditions, and actions.

AI Agent ManagementAction approvals

Review the exact input before an action performs a business write.

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AI Performance & Quality

Models Comparison

Choose the model behavior that best fits your support use case.

Choose a model for your workflow

Select a model from the current model picker in Instructions or Settings. Model availability and limits depend on the deployment and plan; the picker is the current source of available choices.

Compare on the same questions

CriterionCheck
AccuracyDoes the answer match the source and avoid invented details?
ActionsDoes the model select the correct action and collect the required inputs?
LatencyMeasure the complete customer-visible response.
CreditsCompare actual usage for the same set of tasks.
StyleCheck tone, length, and language against your instructions.

Run a comparison

  1. Use a representative set of product questions, follow-ups, unknowns, and action requests.
  2. Run each set in fresh conversations with the same sources and instructions.
  3. Change the model and repeat the same scenarios.
  4. Compare outcomes and usage before selecting the model for production.

Token limits and instructions

Keep the response token limit large enough for the expected answer or tool result. A more capable model cannot supply a missing policy or repair a disconnected action. Correct the source or workflow first.