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N8N implementation

 

1. High-level architecture (mental model)

Chatbot = 4 brains working together

  1. LLM (reasoning + conversation)

  2. RAG (facts about Small Group)

  3. Conversation State (what we know about user)

  4. Conversion Logic (when to push booking)

n8n orchestrates all 4.

2. Knowledge you will store (RAG collections)

Create 4 small, sharp knowledge bases (don’t overdo it).

KB-1: Company & Trust

  • What is Small Group

  • What problems you solve

  • Founder background

  • Why you’re credible

  • How you work

  • NDA, ownership, engagement model

KB-2: AI Automation

  • What AI automation means (in plain English)

  • Examples:

    • CRM automation

    • Lead follow-ups

    • WhatsApp / email / voice bots

    • Internal ops automation

    • AI agents

  • What you don’t do (important for trust)

KB-3: Software Development

  • MVPs

  • SaaS

  • Internal tools

  • Web/mobile apps

  • When custom software is needed vs automation

KB-4: Process & Next Steps

  • How discovery works

  • How calls work

  • What happens after call

  • Typical timelines

  • Pricing philosophy (not numbers)

Each doc chunk should answer one question, not paragraphs.

3. Core system prompt (passed to LLM)

This is non-negotiable. This is what makes it feel human and focused.

You are the AI assistant for Small Group, a team that does BOTH:
1) AI automation (agents, workflows, ops automation)
2) Custom software development (MVPs, internal tools, SaaS)

Your goals, in priority order:
1) Clearly understand the user’s problem
2) Answer trust and capability questions honestly
3) Decide whether AI automation, custom software, or a mix is appropriate
4) Move qualified users toward booking a call

Rules:
- Never oversell
- If something is unclear, ask ONE sharp clarifying question
- If the problem is real and non-trivial, suggest a call
- Do not push booking too early
- Sound like a smart founder, not sales copy
- Use retrieved knowledge as ground truth
- If info is missing, say so transparently

4. Conversation control prompt (dynamic, injected)

This is updated on every message:

Known user info:
- Problem summary: {{problem_summary}}
- Industry: {{industry}}
- Company size: {{company_size}}
- Urgency level: {{urgency}}
- Budget signal: {{budget_signal}}
- Trust level: {{trust_level}}

Your task:
- Either deepen problem understanding
- Or answer a trust/capability question
- Or move toward booking a call if criteria are met

5. Qualification logic (VERY important)

Only push for a call if 2 of 3 are true:

  • Clear business problem

  • Problem cannot be solved with a simple tool

  • User shows intent (cost, timeline, “how”, “can you”)

This logic lives in n8n, not the LLM alone.

6. n8n workflow (node-by-node)

1. Webhook / Chat Trigger

  • Input: user message, session_id

  • Output: raw user text

2. Session Memory (Redis / DB)

  • Fetch conversation state:

    • problem_summary

    • trust_level (0–3)

    • qualification_score (0–3)

    • last_intent

3. Intent Classifier (LLM – cheap model)

Classify message into:

  • TRUST_QUESTION

  • AI_AUTOMATION_QUERY

  • SOFTWARE_QUERY

  • PROBLEM_STATEMENT

  • PRICING_TIMELINE

  • GENERAL_CHAT

Store result.

4. RAG Retriever

Based on intent:

  • TRUST → KB-1

  • AI automation → KB-2

  • Software → KB-3

  • Process / next steps → KB-4

Use:

  • Top 3–5 chunks

  • Semantic similarity

  • Small chunk size (300–500 tokens)

5. Main LLM Response Node

Inputs:

  • System prompt

  • Conversation control prompt

  • Retrieved RAG context

  • User message

Outputs:

  • Natural response

  • Implicit signal: should_ask_question, should_suggest_call

6. Problem Extractor (LLM or rules)

Extract and update:

  • Problem summary (1–2 lines)

  • Industry

  • Company size

  • Urgency (low / medium / high)

Update session memory.

7. Qualification Scorer (IF + Function node)

Increment score if:

  • User describes workflow pain

  • Mentions scale, team, cost, or time

  • Asks “how would you”, “can you build”, “what’s the approach”

8. Call Suggestion Gate

IF:

  • qualification_score ≥ 2

  • trust_level ≥ 1

→ allow booking CTA

Else:

  • continue conversation

9. Booking CTA Generator

Soft, founder-style CTA:

Examples:

  • “This sounds like something we should look at properly — want to hop on a quick call?”

  • “Hard to give a clean answer without context. A 20-min call might save you weeks.”

Attach:

10. Human Handoff (optional)

If user explicitly asks:

  • “Can I talk to someone”

  • “Who will handle this”

→ notify Slack / email with:

  • Conversation summary

  • Problem summary

  • Qualification score

7. What NOT to do (hard advice)

  • ❌ Don’t dump pricing in chatbot

  • ❌ Don’t act like “AI-only” or “dev-only”

  • ❌ Don’t ask 10 discovery questions

  • ❌ Don’t force Calendly on first 3 messages

8. Why this works (founder logic)

  • Feels like talking to a thinking founder

  • AI automation is positioned as leverage, not buzzword

  • Software is positioned as when automation isn’t enough

  • Booking feels like the natural next step, not a funnel trick


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