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.
Federated Chunk Selection
Federated Chunk Selection names a federated approach to chunk selection that helps retrieval and search teams move from experimental setup to dependable operational practice.
Federated Corpus Filtering
Federated Corpus Filtering is an federated operating pattern for teams managing corpus filtering across production AI workflows.
Federated Query Routing
Federated Query Routing is a production-minded way to organize query routing for retrieval and search teams in multi-system reviews.
Federated Context Budgeting
Federated Context Budgeting is a production-minded way to organize context budgeting for retrieval and search teams in multi-system reviews.
Federated Retrieval Scoring
Federated Retrieval Scoring names a federated approach to retrieval scoring that helps retrieval and search teams move from experimental setup to dependable operational practice.
Federated Passage Matching
Federated Passage Matching names a federated approach to passage matching that helps retrieval and search teams move from experimental setup to dependable operational practice.
Federated Snippet Selection
Federated Snippet Selection describes how retrieval and search teams structure snippet selection so the workflow stays repeatable, measurable, and production-ready.
Federated Knowledge Refresh
Federated Knowledge Refresh names a federated approach to knowledge refresh that helps retrieval and search teams move from experimental setup to dependable operational practice.
Federated Evidence Tracing
Federated Evidence Tracing names a federated approach to evidence tracing that helps retrieval and search teams move from experimental setup to dependable operational practice.
Federated Query Expansion
Federated Query Expansion names a federated approach to query expansion that helps retrieval and search teams move from experimental setup to dependable operational practice.
Federated Retrieval Auditing
Federated Retrieval Auditing names a federated approach to retrieval auditing that helps retrieval and search teams move from experimental setup to dependable operational practice.
Federated Context Stitching
Federated Context Stitching is a production-minded way to organize context stitching for retrieval and search teams in multi-system reviews.
Federated Search Calibration
Federated Search Calibration is a production-minded way to organize search calibration for retrieval and search teams in multi-system reviews.
Federated Document Hydration
Federated Document Hydration describes how retrieval and search teams structure document hydration so the workflow stays repeatable, measurable, and production-ready.
Federated Recall Tuning
Federated Recall Tuning is an federated operating pattern for teams managing recall tuning across production AI workflows.
Federated Noise Filtering
Federated Noise Filtering is a production-minded way to organize noise filtering for retrieval and search teams in multi-system reviews.
Federated Intent Routing
Federated Intent Routing is a production-minded way to organize intent routing for retrieval and search teams in multi-system reviews.
Federated Signal Weighting
Federated Signal Weighting is an federated operating pattern for teams managing signal weighting across production AI workflows.
Federated Hybrid Matching
Federated Hybrid Matching describes how retrieval and search teams structure hybrid matching so the workflow stays repeatable, measurable, and production-ready.
Federated Corpus Segmentation
Federated Corpus Segmentation is a production-minded way to organize corpus segmentation for retrieval and search teams in multi-system reviews.
Federated Evidence Coverage
Federated Evidence Coverage names a federated approach to evidence coverage that helps retrieval and search teams move from experimental setup to dependable operational practice.
Grounded Retrieval Pipeline
Grounded Retrieval Pipeline is an grounded operating pattern for teams managing retrieval pipeline across production AI workflows.
Grounded Evidence Ranking
Grounded Evidence Ranking describes how retrieval and search teams structure evidence ranking so the workflow stays repeatable, measurable, and production-ready.
Grounded Result Fusion
Grounded Result Fusion names a grounded approach to result fusion that helps retrieval and search teams move from experimental setup to dependable operational practice.
Grounded Source Attribution
Grounded Source Attribution is a production-minded way to organize source attribution for retrieval and search teams in multi-system reviews.
Grounded Chunk Selection
Grounded Chunk Selection names a grounded approach to chunk selection that helps retrieval and search teams move from experimental setup to dependable operational practice.
Grounded Corpus Filtering
Grounded Corpus Filtering is an grounded operating pattern for teams managing corpus filtering across production AI workflows.
Grounded Query Routing
Grounded Query Routing is a production-minded way to organize query routing for retrieval and search teams in multi-system reviews.
Grounded Context Budgeting
Grounded Context Budgeting is a production-minded way to organize context budgeting for retrieval and search teams in multi-system reviews.
Grounded Retrieval Scoring
Grounded Retrieval Scoring names a grounded approach to retrieval scoring that helps retrieval and search teams move from experimental setup to dependable operational practice.
Grounded Passage Matching
Grounded Passage Matching names a grounded approach to passage matching that helps retrieval and search teams move from experimental setup to dependable operational practice.
Grounded Snippet Selection
Grounded Snippet Selection describes how retrieval and search teams structure snippet selection so the workflow stays repeatable, measurable, and production-ready.
Grounded Knowledge Refresh
Grounded Knowledge Refresh names a grounded approach to knowledge refresh that helps retrieval and search teams move from experimental setup to dependable operational practice.
Grounded Evidence Tracing
Grounded Evidence Tracing names a grounded approach to evidence tracing that helps retrieval and search teams move from experimental setup to dependable operational practice.
Grounded Query Expansion
Grounded Query Expansion names a grounded approach to query expansion that helps retrieval and search teams move from experimental setup to dependable operational practice.
Grounded Retrieval Auditing
Grounded Retrieval Auditing names a grounded approach to retrieval auditing that helps retrieval and search teams move from experimental setup to dependable operational practice.
Grounded Context Stitching
Grounded Context Stitching is a production-minded way to organize context stitching for retrieval and search teams in multi-system reviews.
Grounded Search Calibration
Grounded Search Calibration is a production-minded way to organize search calibration for retrieval and search teams in multi-system reviews.
Grounded Document Hydration
Grounded Document Hydration describes how retrieval and search teams structure document hydration so the workflow stays repeatable, measurable, and production-ready.
Grounded Recall Tuning
Grounded Recall Tuning is an grounded operating pattern for teams managing recall tuning across production AI workflows.
Grounded Noise Filtering
Grounded Noise Filtering is a production-minded way to organize noise filtering for retrieval and search teams in multi-system reviews.
Grounded Intent Routing
Grounded Intent Routing is a production-minded way to organize intent routing for retrieval and search teams in multi-system reviews.
Grounded Signal Weighting
Grounded Signal Weighting is an grounded operating pattern for teams managing signal weighting across production AI workflows.
Grounded Hybrid Matching
Grounded Hybrid Matching describes how retrieval and search teams structure hybrid matching so the workflow stays repeatable, measurable, and production-ready.
Grounded Corpus Segmentation
Grounded Corpus Segmentation is a production-minded way to organize corpus segmentation for retrieval and search teams in multi-system reviews.
Grounded Evidence Coverage
Grounded Evidence Coverage names a grounded approach to evidence coverage that helps retrieval and search teams move from experimental setup to dependable operational practice.
Hybrid Retrieval Pipeline
Hybrid Retrieval Pipeline is a production-minded way to organize retrieval pipeline for retrieval and search teams in multi-system reviews.
Hybrid Evidence Ranking
Hybrid Evidence Ranking names a hybrid approach to evidence ranking that helps retrieval and search teams move from experimental setup to dependable operational practice.
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Product FAQ
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.