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Most clients want a voice chatbot but have no idea how to connect one to their actual knowledge base without months of setup. This AI automation solves that by wiring ElevenLabs voice AI directly to structured knowledge graphs, so you can deliver a talking, answering, embeddable chatbot without building a complex vector pipeline from scratch.
What This Automation Does
- Receives voice input from an ElevenLabs conversational agent embedded on a client website and routes it to an orchestrating AI agent
- Selects the correct knowledge graph expert automatically based on what the user asked, pulling accurate answers from InfraNodus GraphRAG
- Returns a high-quality spoken response to the user via ElevenLabs without the client ever leaving their site
- Lets you update or expand the client's knowledge base without rebuilding the entire workflow from scratch
Tools Used
- n8n
- ElevenLabs
- OpenAI
- Google Gemini
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Join Contra Free →How To Build It
Wire ElevenLabs to receive voice prompts
Configure a webhook endpoint so that every voice message sent through the ElevenLabs conversational agent is captured and passed into the automation in real time.
Set up InfraNodus knowledge graph experts
Create separate InfraNodus knowledge graphs for each subject area the client needs covered, then register each graph as a callable tool so the orchestrating agent knows which expert to consult.
Configure the orchestrating AI agent
Instruct the central AI agent, powered by OpenAI or Google Gemini, to analyse the incoming voice prompt and select the most relevant knowledge graph tool before generating a response.
Enable conversation memory across turns
Attach a memory buffer so the agent retains context across multiple exchanges in a single session, allowing follow-up questions to resolve correctly without repeating earlier context.
Return the spoken response to ElevenLabs
Send the agent's text answer back through the webhook response so ElevenLabs converts it to speech and plays it to the end user, completing the full voice interaction loop.
Pitfalls
- ElevenLabs webhook timeouts are tight: if the AI agent takes too long to query a knowledge graph and compose a response, the voice interaction drops mid-conversation. Keep knowledge graphs lean and test response latency before delivery.
- Knowledge graph drift breaks answer quality: if a client's product, pricing, or policy changes and the InfraNodus graphs are not updated, the agent will confidently give outdated answers. Build a monthly update cadence into any retainer agreement.
- OAuth tokens for connected services expire silently: credentials for OpenAI, Google Gemini, or InfraNodus can lapse without visible errors during setup, only failing at runtime in front of the client. Test all auth connections against a live prompt before handoff.
FAQ
Can I build this without coding?
Yes. The entire workflow is configured through visual interfaces in n8n and ElevenLabs, and InfraNodus knowledge graphs are built by pasting or importing documents, not writing code. Basic familiarity with webhooks helps but is not required.
How long does it take?
A single-knowledge-base version can be delivered in under two hours once you have access to the client's content. Multi-expert setups with three or more knowledge graphs typically take a full day including testing and embedding on the client's site.
What can I charge?
This workflow is well-suited to a project fee for the initial build plus a monthly retainer for knowledge base maintenance and monitoring. The retainer angle is strong because clients cannot easily update InfraNodus graphs themselves without guidance.
Which tool is required vs optional?
ElevenLabs and n8n are required: ElevenLabs handles the voice layer and n8n orchestrates everything. OpenAI and Google Gemini are interchangeable as the language model, and InfraNodus can be swapped for another GraphRAG provider if a client already has one.
This is original DigiNo analysis. The underlying automation pattern is a community workflow template – view the original on n8n.
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