> ## Documentation Index
> Fetch the complete documentation index at: https://devdocs.aiconnected.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# legacy funnelChat conversation flow sample

> Got it — you're referring to your funnelChat philosophy : “The best funnel is the one leads don’t know they’re in.” And the goal is to collect lead data pass...

<Info>
  Normalized for Mintlify from `knowledge-base/aiconnected-apps-and-modules/modules/funnelChat/legacy-funnelChat-conversation-flow-sample.mdx`.
</Info>

Got it — you're referring to your **funnelChat philosophy**:

*“The best funnel is the one leads don’t know they’re in.”*

And the goal is to **collect lead data passively and naturally** during a rich, helpful, Perplexity-style conversation — *not through obvious form-filling behavior.*

Let’s reframe the solution around this **invisible lead capture** idea.

***

## **✅ funnelChat Lead Capture – Passive & Conversational Flow**

Instead of direct prompts like “What’s your name?”, the AI should:

1. **Guide the conversation like a helpful research assistant.**

2. **Casually prompt for info** only when it feels *relevant* or *earned.*

3. **Use AI tools to extract structured data** from unstructured, free-flowing dialogue.

***

### **🧠 How to Implement This in n8n**

#### **1. Define Passive Goals for the Agent**

In the AI Agent node:

`{`\
`"persona": "An ultra-helpful, respectful research assistant who offers solutions without pressure.",`\
`"goal": "Guide the user through an insightful conversation and, where natural, capture their name, email, and business interest for follow-up.",`\
`"tone": "Engaged, light, helpful — never pushy or robotic.",`\
`"rules": [`\
`"Do not directly ask for contact info unless it arises naturally.",`\
`"If the user asks a detailed question, you may offer to send more info by email.",`\
`"Make email or name collection feel like a service, not a requirement."`\
`]`\
`}`

***

#### **2. Enable Soft Prompts During Key Moments**

Examples of prompts the AI can drop **only after giving value**:

* *“I actually wrote a short summary of this last week — want me to send it over?”*

* *“If you’d like a downloadable version of these steps, I can email it to you. Totally optional.”*

* *“Happy to turn this into a checklist — want me to email it over?”*

These feel **like bonuses**, not asks.

***

#### **3. Extract Lead Fields Invisibly**

Use OpenAI / Claude / Regex to extract:

| Field      | Extraction Approach                                                            |
| ---------- | ------------------------------------------------------------------------------ |
| **Name**   | “My name is...” / Email signature / Greeting                                   |
| **Email**  | Detect using regex or prompt: *“Can I send this to your email?”*               |
| **Intent** | Inferred from multi-turn topic (“I’m trying to solve…” / “My business needs…”) |

You don’t need a form — just **pass messages through a parsing node** (e.g. JS + LLM) every 2–3 turns.

***

#### **4. Save to Memory + CRM Silently**

Once fields are inferred:

* Store them into memory

* Update user context

* Save lead quietly in background (e.g. Airtable, GHL, Supabase)

If user returns later, the agent can say:

“Welcome back! Last time we were talking about your sales funnel. Want to pick up from there?”

***

#### **5. Show Empathy, Not Automation**

When it’s time to ask for info, don’t "collect," just *offer help.*

✔️ Instead of:

“Please provide your email.”

Try:

“Would you like a quick PDF of these strategies? I can email it to you.”

Even if they say “Sure,” that’s enough to extract and store — without breaking immersion.

***

### **⚙️ n8n Node Tips**

* **LLM Entity Extractor**: Custom JS node or OpenAI node to pull name/email from chat stream.

* **Memory Check**: `If` node to detect what’s missing and route the conversation softly.

* **Lead Saver**: Background webhook or HTTP Request node that pushes to your CRM.

* **Re-engagement Prompt**: Use stored memory to tailor follow-ups if user returns.