Personalized Hybrid Matching describes how retrieval and search teams structure hybrid matching so the workflow stays repeatable, measurable, and production-ready.

Open page

Personalized Corpus Segmentation

Personalized Corpus Segmentation is a production-minded way to organize corpus segmentation for retrieval and search teams in multi-system reviews.

Open page

Personalized Evidence Coverage

Personalized Evidence Coverage names a personalized approach to evidence coverage that helps retrieval and search teams move from experimental setup to dependable operational practice.

Open page

Policy-Aware Retrieval Pipeline

Policy-Aware Retrieval Pipeline describes how retrieval and search teams structure retrieval pipeline so the workflow stays repeatable, measurable, and production-ready.

Open page

Policy-Aware Evidence Ranking

Policy-Aware Evidence Ranking is a production-minded way to organize evidence ranking for retrieval and search teams in multi-system reviews.

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Policy-Aware Result Fusion

Policy-Aware Result Fusion is an policy-aware operating pattern for teams managing result fusion across production AI workflows.

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Policy-Aware Source Attribution

Policy-Aware Source Attribution names a policy-aware approach to source attribution that helps retrieval and search teams move from experimental setup to dependable operational practice.

Open page

Policy-Aware Chunk Selection

Policy-Aware Chunk Selection is an policy-aware operating pattern for teams managing chunk selection across production AI workflows.

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Policy-Aware Corpus Filtering

Policy-Aware Corpus Filtering describes how retrieval and search teams structure corpus filtering so the workflow stays repeatable, measurable, and production-ready.

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Policy-Aware Query Routing

Policy-Aware Query Routing names a policy-aware approach to query routing that helps retrieval and search teams move from experimental setup to dependable operational practice.

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Policy-Aware Context Budgeting

Policy-Aware Context Budgeting names a policy-aware approach to context budgeting that helps retrieval and search teams move from experimental setup to dependable operational practice.

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Policy-Aware Retrieval Scoring

Policy-Aware Retrieval Scoring is an policy-aware operating pattern for teams managing retrieval scoring across production AI workflows.

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Policy-Aware Passage Matching

Policy-Aware Passage Matching is an policy-aware operating pattern for teams managing passage matching across production AI workflows.

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Policy-Aware Snippet Selection

Policy-Aware Snippet Selection is a production-minded way to organize snippet selection for retrieval and search teams in multi-system reviews.

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Policy-Aware Knowledge Refresh

Policy-Aware Knowledge Refresh is an policy-aware operating pattern for teams managing knowledge refresh across production AI workflows.

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Policy-Aware Evidence Tracing

Policy-Aware Evidence Tracing is an policy-aware operating pattern for teams managing evidence tracing across production AI workflows.

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Policy-Aware Query Expansion

Policy-Aware Query Expansion is an policy-aware operating pattern for teams managing query expansion across production AI workflows.

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Policy-Aware Retrieval Auditing

Policy-Aware Retrieval Auditing is an policy-aware operating pattern for teams managing retrieval auditing across production AI workflows.

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Policy-Aware Context Stitching

Policy-Aware Context Stitching names a policy-aware approach to context stitching that helps retrieval and search teams move from experimental setup to dependable operational practice.

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Policy-Aware Search Calibration

Policy-Aware Search Calibration names a policy-aware approach to search calibration that helps retrieval and search teams move from experimental setup to dependable operational practice.

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Policy-Aware Document Hydration

Policy-Aware Document Hydration is a production-minded way to organize document hydration for retrieval and search teams in multi-system reviews.

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Policy-Aware Recall Tuning

Policy-Aware Recall Tuning describes how retrieval and search teams structure recall tuning so the workflow stays repeatable, measurable, and production-ready.

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Policy-Aware Noise Filtering

Policy-Aware Noise Filtering names a policy-aware approach to noise filtering that helps retrieval and search teams move from experimental setup to dependable operational practice.

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Policy-Aware Intent Routing

Policy-Aware Intent Routing names a policy-aware approach to intent routing that helps retrieval and search teams move from experimental setup to dependable operational practice.

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Policy-Aware Signal Weighting

Policy-Aware Signal Weighting describes how retrieval and search teams structure signal weighting so the workflow stays repeatable, measurable, and production-ready.

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Policy-Aware Hybrid Matching

Policy-Aware Hybrid Matching is a production-minded way to organize hybrid matching for retrieval and search teams in multi-system reviews.

Open page

Policy-Aware Corpus Segmentation

Policy-Aware Corpus Segmentation names a policy-aware approach to corpus segmentation that helps retrieval and search teams move from experimental setup to dependable operational practice.

Open page

Policy-Aware Evidence Coverage

Policy-Aware Evidence Coverage is an policy-aware operating pattern for teams managing evidence coverage across production AI workflows.

Open page

Precision-Tuned Retrieval Pipeline

Precision-Tuned Retrieval Pipeline describes how retrieval and search teams structure retrieval pipeline so the workflow stays repeatable, measurable, and production-ready.

Open page

Precision-Tuned Evidence Ranking

Precision-Tuned Evidence Ranking is a production-minded way to organize evidence ranking for retrieval and search teams in multi-system reviews.

Open page

Precision-Tuned Result Fusion

Precision-Tuned Result Fusion is an precision-tuned operating pattern for teams managing result fusion across production AI workflows.

Open page

Precision-Tuned Source Attribution

Precision-Tuned Source Attribution names a precision-tuned approach to source attribution that helps retrieval and search teams move from experimental setup to dependable operational practice.

Open page

Precision-Tuned Chunk Selection

Precision-Tuned Chunk Selection is an precision-tuned operating pattern for teams managing chunk selection across production AI workflows.

Open page

Precision-Tuned Corpus Filtering

Precision-Tuned Corpus Filtering describes how retrieval and search teams structure corpus filtering so the workflow stays repeatable, measurable, and production-ready.

Open page

Precision-Tuned Query Routing

Precision-Tuned Query Routing names a precision-tuned approach to query routing that helps retrieval and search teams move from experimental setup to dependable operational practice.

Open page

Precision-Tuned Context Budgeting

Precision-Tuned Context Budgeting names a precision-tuned approach to context budgeting that helps retrieval and search teams move from experimental setup to dependable operational practice.

Open page

Precision-Tuned Retrieval Scoring

Precision-Tuned Retrieval Scoring is an precision-tuned operating pattern for teams managing retrieval scoring across production AI workflows.

Open page

Precision-Tuned Passage Matching

Precision-Tuned Passage Matching is an precision-tuned operating pattern for teams managing passage matching across production AI workflows.

Open page

Precision-Tuned Snippet Selection

Precision-Tuned Snippet Selection is a production-minded way to organize snippet selection for retrieval and search teams in multi-system reviews.

Open page

Precision-Tuned Knowledge Refresh

Precision-Tuned Knowledge Refresh is an precision-tuned operating pattern for teams managing knowledge refresh across production AI workflows.

Open page

Precision-Tuned Evidence Tracing

Precision-Tuned Evidence Tracing is an precision-tuned operating pattern for teams managing evidence tracing across production AI workflows.

Open page

Precision-Tuned Query Expansion

Precision-Tuned Query Expansion is an precision-tuned operating pattern for teams managing query expansion across production AI workflows.

Open page

Precision-Tuned Retrieval Auditing

Precision-Tuned Retrieval Auditing is an precision-tuned operating pattern for teams managing retrieval auditing across production AI workflows.

Open page

Precision-Tuned Context Stitching

Precision-Tuned Context Stitching names a precision-tuned approach to context stitching that helps retrieval and search teams move from experimental setup to dependable operational practice.

Open page

Precision-Tuned Search Calibration

Precision-Tuned Search Calibration names a precision-tuned approach to search calibration that helps retrieval and search teams move from experimental setup to dependable operational practice.

Open page

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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.