How to Build a Customer Support Chatbot From Your Documentation

Most software companies already have the answers to 90% of user support tickets written down. They live inside API references, GitHub READMEs, and help center tutorials.

The issue isn't missing information—it's discoverability. When a user is stuck in the middle of an onboarding flow, they don't want to browse through a nested documentation hierarchy. They want an immediate, contextual answer.

In this tutorial, we will walk through how to transform your existing documentation into an autonomous, citation-backed AI customer support chatbot using Basegent.

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Step 1: Ingesting Your Source Content

Rather than manually copying and pasting text into prompts, connect your existing source of truth.

Basegent supports several ingestion methods:

• Public URL / Sitemap Crawl: Provide your documentation domain (e.g., https://docs.yourcompany.com/sitemap.xml).
• Markdown & MDX Repositories: Sync markdown files directly from GitHub or local directories.
• OpenAPI & Swagger Schemas: Upload your REST API specification to enable endpoint-aware reasoning.

Once ingested, the crawler splits your documents into semantic fragments, computes dense vector embeddings, and stores them in a searchable knowledge base.

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Step 2: Testing Queries and Verifying Citations

Before exposing the agent to end customers, navigate to the Query playground in your Basegent cockpit.

Test real questions from your support history:

• "How do I authenticate webhook signatures in Node.js?"
• "What is the rate limit for free tier users?"
• "How do I invite teammates with read-only permissions?"

Notice that each generated answer includes clickable citations to the exact source page and paragraph. If an answer is inaccurate, you don't adjust prompt weights—you simply edit the source document in the Wiki editor.

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Step 3: Embedding the Widget Into Your Next.js or React App

Basegent provides an official React library with pre-built styling, theme synchronization, and shadow DOM isolation.

Install the package:

npm install @basegent/react

Embed the component into your root layout or application shell:

import { BasegentWidget } from "@basegent/react"

export default function Layout({ children }: { children: React.ReactNode }) {
  return (
    <html>
      <body>
        {children}
        <BasegentWidget
          token={process.env.NEXT_PUBLIC_BASEGENT_TOKEN!}
          position="bottom-right"
          theme="system"
          greeting="Hi there! Ask me anything about our API or features."
        />
      </body>
    </html>
  )
}

The widget runs client-side in an isolated shadow DOM to guarantee that your application's CSS frameworks (Tailwind, Vanilla CSS) will not conflict with the chat interface.

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Step 4: Connecting the Human Escalation Fallback

What happens when an inquiry requires human judgement?

Basegent automatically detects when an inquiry cannot be resolved with high confidence. It captures the user's intent, packages the full conversation history, and forwards the ticket to your team via:

• The unified Inbox dashboard
• Webhook notifications to your internal Slack or Discord channels
• Email alerts to your support escalation queue

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Summary

By connecting your documentation directly to an AI support agent:

1. Customers get instant, 24/7 answers with zero waiting time.
2. Every answer links directly to verified source documentation.
3. Your human team only handles novel, high-leverage issues.

Start building your AI support agent today at basegent.space.