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Cross-industry

Document & Knowledge Base Q&A Chatbot

Turn your PDFs, docs, URLs and YouTube content into an instant-answer bot. NIVA indexes your content into a private vector DB and answers only from what you teach it.

knowledge base chatbot document Q&A bot RAG chatbot PDF chatbot private vector database chatbot
24/7
Automated Coverage
0
Developers Required
5
Automation Steps
4
Measurable Outcomes
The Problem
Organisations hold answers in scattered PDFs, manuals, web pages and videos that nobody can search effectively, so the same questions get asked repeatedly.

How NIVA Handles This Automatically

NIVA indexes PDFs, DOCX, URLs, plain text and YouTube transcripts into a private, per-bot vector database. The most relevant chunks are retrieved and injected into every answer, with no cross-contamination between bots and per-source update control.

Live Conversation Flow
1
Upload PDFs, docs, URLs or YouTube transcripts
2
NIVA chunks, embeds and indexes into a private collection
3
Visitor or staff asks a question
4
Relevant chunks retrieved and injected into the answer
5
Update a single source any time; index refreshes

Step-by-Step: What Happens Inside the Chat

Upload PDFs, docs, URLs or YouTube transcripts
NIVA chunks, embeds and indexes into a private collection
Visitor or staff asks a question
Relevant chunks retrieved and injected into the answer
Update a single source any time; index refreshes

Before NIVA vs. With NIVA

Real differences your team will feel from day one — not theoretical benchmarks.

Area Before NIVA With NIVA
Content searchability Scattered, unsearchable Instant Q&A
Answer grounding Generic AI Only your content
Source control Bulk re-upload Per-source updates
Data isolation Mixed Private per bot

How NIVA Powers This

Under the hood this maps to NIVA's documented engines. The persona engine handles tone and routing for cross-industry, drawing on a library of pre-trained personas so the agent speaks the language of the domain from day one rather than being trained from scratch. The flow engine runs the conditional steps in the sequence shown above, branching on the visitor's answers so each person follows the path that fits their situation. The smart-form engine surfaces structured fields at the moment intent appears, capturing clean data inline instead of bouncing the user to a separate form. Cross-session memory preserves context so returning users are recognised and never asked to repeat themselves. Finally, webhooks push the completed interaction into your systems of record, and per-persona tool calls can read live data from your APIs mid-conversation wherever an endpoint exists. None of these steps requires writing code; they are assembled in the no-code admin and embedded with a single script tag.
Who Is This For

This works in two directions. As a public-facing agent it engages prospects and customers directly on your website or app; as an internal tool it answers staff questions and runs intake against internal systems. The same engines power both, and which direction you deploy depends only on which knowledge sources and systems you connect. Many cross-industry operators start public-facing for the traffic and conversion upside, then reuse the same build internally.

In practical terms, the shift looks like this: on content searchability, you move from scattered, unsearchable to instant q&a; on answer grounding, you move from generic ai to only your content; on source control, you move from bulk re-upload to per-source updates; on data isolation, you move from mixed to private per bot. Each of these is a direct consequence of moving the interaction from a manual, human-gated channel to an always-available conversational one that still escalates to a person when the situation genuinely needs it.

NIVA Engines Persona Engine Flow Engine Smart Forms RAG Knowledge Webhooks
Implementation Note

Implementation reality: the conversational layer, routing, data capture, and system handoff are all within NIVA's documented no-code capabilities. Where this use case reads or writes live data, it assumes the relevant API or webhook endpoint exists on your side; that integration is the one piece worth scoping before launch. Start with the knowledge base and flow, then layer in live API calls once the core experience is proven.

Frequently Asked Questions

What is a document & knowledge base q&a chatbot?
It is a NIVA-powered conversational agent in which nIVA indexes PDFs, DOCX, URLs, plain text and YouTube transcripts into a private, per-bot vector database. It runs on your website or app and works around the clock without adding headcount.
How does NIVA build this without code?
You select a pre-trained Cross-industry persona, connect your knowledge sources, and assemble the flow and forms in the no-code admin. The example flow on this page can be replicated step by step, and the bot embeds with a single script tag.
Can it connect to our existing systems?
Yes. Completed conversations fire webhooks into your CRM, booking system, or ticketing tool, and per-persona tool calls can read live data from your APIs during the conversation where an endpoint exists.
How quickly can we go live?
Because the Cross-industry persona is pre-trained and the engines are no-code, a working version of this use case can be configured and embedded quickly, then refined against real conversations.
Ready to Deploy

Build This for Your Business in Hours, Not Months

No developers. No long setup. NIVA gives you every engine shown on this page — persona routing, flow automation, smart forms, and webhooks — as a ready-to-configure platform.