AI glossary for content assistants
Plain-English definitions of 13,917 AI terms for branded assistant teams.
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13,917 terms. Open one for definitions and related concepts.
Iframe Embedding
Iframe embedding displays a chatbot inside an HTML iframe element, providing simple integration with complete isolation from the host page.
Script Tag Embedding
Script tag embedding adds a chatbot to a website by including a JavaScript snippet that loads and initializes the chat widget on the page.
NPM Package (Chatbot)
An NPM chatbot package provides installable modules for integrating chatbot functionality into JavaScript projects through the npm registry.
Chatbot Testing
Chatbot testing validates that a chatbot responds correctly, handles edge cases, and provides a good user experience across all scenarios.
Conversation Testing
Conversation testing validates multi-turn chatbot interactions to ensure flows, context handling, and transitions work correctly end-to-end.
Regression Testing (Chatbot)
Regression testing for chatbots verifies that changes to the bot configuration or knowledge base do not break previously working functionality.
A/B Testing (Chatbot)
A/B testing for chatbots compares two chatbot configurations with real users to determine which performs better on key metrics.
Sandbox Environment
A sandbox environment is an isolated testing space where chatbot changes can be tested without affecting the live production chatbot.
Staging Environment
A staging environment is a production-like chatbot deployment used for final validation before changes go live to real users.
Version Control (Chatbot)
Version control for chatbots tracks changes to bot configuration, knowledge, and flows over time, enabling history review and reverting.
Rollback (Chatbot)
A chatbot rollback reverts the bot to a previous working configuration when a recent change causes problems or performance degradation.
Triggered Messages
Triggered messages are automated chatbot messages sent when specific user behaviors or conditions are detected on a website.
Exit Intent (Chatbot)
Exit intent detection triggers a chatbot message when a user shows signs of leaving the page, such as moving the cursor toward the browser close button.
Page Targeting
Page targeting displays different chatbot configurations, messages, or behaviors based on which page the visitor is currently viewing.
URL Targeting
URL targeting uses URL patterns to determine which chatbot configuration to display, enabling page-specific chatbot behavior.
Visitor Segmentation
Visitor segmentation groups website visitors by characteristics or behavior to deliver personalized chatbot experiences to each segment.
Returning Visitor
A returning visitor is a user who has previously interacted with the website or chatbot, enabling personalized continuation of their experience.
New Visitor
A new visitor is a first-time website user who has no prior chatbot interaction history, requiring introductory guidance and onboarding.
Scroll Trigger
A scroll trigger activates a chatbot message when the user scrolls to a specific point on the page, indicating content engagement.
Time Trigger
A time trigger activates a chatbot message after the user has been on a page for a specified duration, indicating sustained interest.
Click Trigger
A click trigger opens the chatbot or sends a specific message when the user clicks a designated element on the page.
Event Trigger
An event trigger activates chatbot behavior in response to custom application events like purchases, form submissions, or errors.
Conditional Logic
Conditional logic enables chatbots to make decisions and branch conversation paths based on user responses, data, or external conditions.
Branching Logic
Branching logic creates multiple conversation paths in a chatbot flow, directing users down different routes based on their choices or data.
Variable (Chatbot)
A chatbot variable stores data collected during conversation, such as user names or preferences, for use later in the interaction.
Custom Attribute
A custom attribute is a user-defined data field that stores additional information about visitors or conversations beyond standard properties.
User Profile (Chatbot)
A chatbot user profile stores persistent information about a visitor across conversations, enabling personalized and contextual interactions.
Conversation Tag
A conversation tag is a label applied to chat conversations for categorization, filtering, and analytics purposes.
Conversation Label
A conversation label is a classification marker used to organize and filter chatbot conversations by type, status, or business category.
Conversation Priority
Conversation priority ranks chatbot conversations by urgency to ensure the most critical issues receive attention first.
Chatbot Training
Chatbot training is the process of teaching a chatbot to respond accurately by providing it with knowledge, examples, and behavioral guidelines.
Training Data (Chatbot)
Training data for chatbots includes the documents, FAQs, and example conversations used to teach the bot what it should know and how to respond.
Training Phrase
A training phrase is an example user utterance used to teach traditional chatbots to recognize a specific intent or topic.
Intent Training
Intent training teaches a chatbot to recognize user goals by providing labeled examples of how users express each intent.
Entity Training
Entity training teaches a chatbot to extract specific data values like names, dates, and product names from user messages.
Conversation Log
A conversation log is a complete record of all messages exchanged between users and the chatbot, used for analysis and improvement.
Conversation Export
Conversation export allows downloading chatbot conversation data in various formats for external analysis, compliance, or backup purposes.
Chatbot Import
Chatbot import enables loading external data into a chatbot platform, including knowledge bases, configurations, and training data.
Bulk Upload
Bulk upload enables uploading multiple files or data records to a chatbot platform at once, streamlining initial setup and content updates.
CSV Import
CSV import loads structured data from comma-separated value files into a chatbot platform for FAQ lists, product data, or user information.
API Import
API import uses the chatbot platform's API to programmatically load and sync data, enabling automated and scheduled content updates.
Knowledge Base (Chatbot)
A chatbot knowledge base is the collection of documents and data that the chatbot references to answer user questions accurately.
Document Bot
A document bot is a chatbot that answers questions by searching and extracting information from uploaded documents and files.
Website Bot
A website bot is a chatbot that learns from your website content, answering questions based on information from your web pages.
PDF Bot
A PDF bot is a chatbot specialized in answering questions from uploaded PDF documents, making dense documents conversational.
Help Center Bot
A help center bot is a chatbot integrated with a help center or documentation site, providing conversational access to support articles.
Chatbot Security
Chatbot security encompasses the measures protecting chatbot systems and user data from unauthorized access, attacks, and data breaches.
Data Encryption (Chatbot)
Data encryption for chatbots protects conversation data and user information by encoding it so only authorized parties can access it.
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What is InsertChat?
InsertChat is a white-label AI assistant for your website. Train it, brand it, publish it, and learn from visitor questions.
How does InsertChat use my website content?
Connect approved pages, docs, videos, FAQs, policies, and other sources. InsertChat turns them into source-backed answers and next steps.
Can I control the assistant's tone and sources?
Yes. Choose its sources, tone, welcome message, and prompts so it stays on brand.
How does InsertChat stay accurate?
Answers use approved content and source links. Analytics show unclear or missing answers so you can improve coverage.
Can it collect leads or route support questions?
Yes. InsertChat can collect details, qualify intent, add context, and send chats to the right inbox, CRM, workflow, or person.
Can I control how the assistant behaves?
Yes. Control prompts, model choice, tool access, and the branded assistant experience so behavior stays consistent.
Which AI models can I use?
InsertChat supports multiple model providers. Choose each assistant's model for quality, speed, and cost, or use BYOK.
Can I pick different models for different workflows?
Yes. Use a faster model for common questions and a stronger model for complex reasoning. InsertChat supports that balance per conversation.
Where can I deploy an assistant?
Use a widget, embed, full-page assistant, custom domain, in-app embed, or API. Reuse one setup across surfaces.
Do I need coding skills?
No. Build and deploy AI assistants using our visual builder. The embed code is one line of JavaScript.
Can I customize the branding and UI?
Yes. Customize the assistant name, logo, colors, welcome message, suggested prompts, tone, domain, and white-label presentation.
Can I use my own domain?
Yes. Custom domains are supported, typically via enterprise options.
Does InsertChat support voice?
Yes. Voice dictation and text-to-speech let users speak instead of type.
Does InsertChat support vision?
Yes. Enable vision for assistants when images help clarify a request or context.
What tools and integrations are supported?
Zendesk, HubSpot, Shopify, WooCommerce, calendar booking, web search, Perplexity, and webhooks for your own systems.
Can I control which tools the assistant is allowed to use?
Yes. Tool access is controlled per assistant so you enable only what you need.
Can the agent hand off to a human?
Yes. Configure human handoff so the agent escalates when needed. Full conversation history is passed along.
Do you provide analytics?
Yes. Track chats, leads, feedback, top questions, unanswered questions, most-used sources, and content gaps.
Is it mobile friendly?
Yes. The widget and embeds work well on desktop and mobile with no separate experience needed.
What's the fastest path to a successful deployment?
Start with one assistant and a small set of high-value sources. Iterate using real questions from analytics.
What is the fastest way to get started?
Create an account. Connect one key source. Ask a test question, brand the assistant, then publish it on one page.