This workflow turns your WhatsApp number into an intelligent AI-powered Sales Agent that answers product queries using real data extracted from a PDF brochure. It loads a product brochure via HTTP Request, converts it into embeddings using OpenAI, stores them in an in-memory vector store and allows the AI Agent to provide factual answers to users via WhatsApp. Non-text messages are filtered and only text queries are processed. This makes the workflow ideal for building a lightweight chatbot that understands your product documentation deeply.
This workflow converts a product brochure (PDF) into a searchable knowledgebase using LangChain vector embeddings. Incoming WhatsApp messages are processed and if the message is text, the AI Sales Agent uses OpenAI + the vector store to produce accurate, brochure-based answers.
The AI responds naturally to customer queries, supports conversation memory across the session and retrieves information directly from the brochure when needed. Non-text messages are filtered out to maintain clean conversational flow.
The workflow is fully modular: you can replace the PDF, modify AI prompts, plug into CRM systems or extend it into a broader sales automation pipeline.
This workflow is ideal for:
To run this workflow successfully, you need:
Optional:
Upload the workflow JSON provided.
The workflow uses two WhatsApp send nodes:
Add your WhatsApp credentials to both.
In get Product Brochure (HTTP Request):
url parameter with your own PDFUse the Manual Trigger ("When clicking ‘Test workflow’") to:
You must run this once after importing the workflow.
Add your OpenAI API Key to the following nodes:
OpenAI Chat ModelOpenAI Chat Model1Embeddings OpenAIEmbeddings OpenAI1Inside AI Sales Agent, you can edit the system message to match:
Once activated, WhatsApp users can chat with your AI Sales Agent.
Here are common customization options:
Change the URL in get Product Brochure
or
Upload your own file via other nodes.
Edit the systemMessage inside AI Sales Agent:
Modify Handle Message Types switch logic to allow:
Inside the textBody of response nodes.
Swap vectorStoreInMemory with:
By updating the vector store node.
You can extend this workflow with:
Add OpenAI translation nodes before agent input.
Send user queries and chat logs into:
Use embeddings similarity to suggest products.
Connect to Stripe or Shopify APIs.
Log chats into Airtable / Postgres for analysis.
Here are some practical uses:
Product Inquiry Chatbot
Customers ask about specs, pricing, or compatibility.
Digital Catalog Assistant
Converts PDF brochures into interactive WhatsApp search.
Sales Support Bot
Reduces load on human sales reps by handling common questions.
Internal Knowledge Bot
Teams access manuals, training documents, or service guides.
Event/Product Launch Assistant
Provides instant details about newly launched items.
And many more similar use cases where an AI-powered WhatsApp assistant is valuable.
| Issue | Possible Cause | Solution |
|---|---|---|
| WhatsApp messages not triggering workflow | Wrong webhook URL or inactive workflow | Ensure webhook is correct & activate workflow |
| AI replies are empty | Missing OpenAI credentials | Add OpenAI API key to all AI nodes |
| Vector store not populated | Manual trigger not executed | Run the Test Workflow trigger once |
| PDF extraction returns blank text | PDF is image-based | Use OCR before text splitting |
| “Unsupported message type” always triggers | Message type filter misconfigured | Check conditions in Handle Message Types |
| AI not using brochure data | VectorStore tool not linked properly | Check connections between Embeddings → VectorStore → AI Agent |
If you need help setting up, customizing or extending this workflow, feel free to reach out to our n8n automation developers at WeblineIndia. We can help with