u/atul_k09

📍 Location: WFH

💼 Experience: Minimum 1 Year

🏫 Industry: AI Automation & Tech Services
We are building custom AI sales assistants and automation systems for local SMEs, and we need a driven sales professional to help us expand.

🚀 Open Position:
• Business Development Executive (B2B Sales)

✨ What We’re Looking For:
• 1+ year of proven experience in B2B sales, field sales, or telecalling.
• A solid track record of scheduling meetings and successfully closing deals.
• Strong communication skills to pitch modern AI tech simply to business owners.
• A self-starter motivated by a high-earning structure (Base Salary ₹8k-12k + Flat 20% Bonus on sales above ₹50k).
• Preferred: Hands-on experience with modern CRMs and Lead Gen software (e.g., Apollo.io, HubSpot, Zoho, or LinkedIn Sales Navigator).

📧 Interested? Send your resume via DM.

Let’s scale the future of AI automation together!

My family runs a clothing store in Jaipur. Like most small retail shops in India, their entire customer interaction happens on WhatsApp.

Every day, my brother was handling the same messages manually:

• "Kya available hai?" (What's available?)
• "Budget 5000 hai, kya dikhao ge?" (Budget 5000, what can you show me?)
• The same category and budget questions from 20 different people.
• Customers waiting 30 minutes for a product link, giving up, and going elsewhere.

He was running Instagram to bring leads in. The leads were coming. But there was nothing on the other end to handle them. Just a phone and one person replying to everything.

I'd been learning n8n and building small AI workflows for a while. I thought: this is exactly the problem automation is supposed to solve.

What I didn't expect was how long it would take.

Version 1 was embarrassing. A basic webhook that sent a canned reply. Fine for testing, useless for real customers.

The real problem hit around version 3. A customer sends "hi", the agent greets them, they say they want something, the agent jumps straight to asking for their name and budget. Same customer messages the next day. The agent has no idea who they are.

No memory. No routing. No sense of where a customer is in their journey.

I started over properly.

The final system: 44 nodes, 2 AI agents

Entry layer (before AI even runs):

Every incoming WhatsApp message passes through a filter first:

• Is this from the store's own number? Ignore.
• Is it from a group chat? Ignore.
• Did the customer send "START" or "STOP"? Route separately.
• Is this number on an exclusion list (Friend/STOP role in Google Sheets)? Block.

Only after all of that does the message go anywhere useful. This alone cut a lot of noise.

The status router (the part that took the most time):

Before any agent runs, the system fetches the customer's current status from Google Sheets. That status is one of:

• New Lead
• Follow-up
• Order Booking
• Product Not Found
• Complaint

Status is "Order Booking"? The message goes directly to the Order Booking Agent, skipping the main agent completely. Customer sends exactly "PP" (short for "price please")? Also routes to the Order Agent, but in a price-lookup mode.

Everything else goes to the Main Sales Agent.

Getting this routing right took weeks. The edge cases were brutal. A customer mid-order should not be re-greeted by the main agent. A customer who just confirmed "Haan" (yes) and is waiting for order details should not get the intent detection flow again. It sounds obvious when I say it. It is not obvious when you're building it.

The Main Sales Agent (8 stages):

One AI agent, one long system message, 8 stages of a real sales conversation:

1. Greeting (once only, never repeated mid-conversation)
2. Intent Detection (no lead capture until buying intent is clear)
3. Product Availability (searches Pinecone vector store before answering)
4. Lead Capture (Name, City, Budget, Category, Occasion)
5. Product Link Sharing (max 3 links per message, fetched from Google Sheets by Category + Budget)
6. Order Intent Handoff (the agent sets status to "Order Booking" and stops, never confirms itself)
7. Price Query (real price pulled from Item Price sheet by Item Code, never assumed)
8. FAQ + Human Handoff (Pinecone search for policy questions, STOP keyword exits the flow)

Two things the main agent can never do: confirm an order and make up a price. If it doesn't have the price, it says so. Order confirmation only happens in the next agent.

The Order Booking Agent:

A separate dedicated agent. Takes over once the customer is ready to buy.

Collects: Item Code, delivery date, any special preferences. Displays an order summary. Waits for the customer to type "FINAL". Only then does it write the order to the Orders sheet.

It also handles a "PP Mode" where customers jump straight to price inquiry by sending "PP", get the exact price from the sheet, and can then confirm or exit.

The business notification system:

When the main agent says something like "team aapse jald contact karegi" (team will contact you soon), a third agent picks up the output, pulls the full customer record and any order details from Google Sheets, and sends a structured summary directly to the store's WhatsApp number. The owner gets the full picture immediately without hunting for context.

Tech Stack:

• n8n (self-hosted) for orchestration
• OpenAI GPT-4o for both agents
• Pinecone for FAQ vector search
• Google Sheets as the database (Leads, Orders, Product Catalog, Item Prices)
• WhatsApp Cloud API for messaging
• Shared buffer memory window across all three agents

It's been running with real customers for a few weeks. Not flawless. The AI still occasionally asks for something it already has. But the main flow works, and my brother is no longer stuck on WhatsApp for hours every day.

The thing that surprised me most: the AI was not the hard part. Designing the state machine was. Knowing which agent should handle a message, what that customer already told us, and what happens when they switch context mid-conversation is a much harder problem than writing a good system prompt.

If I were starting over, I'd draw the routing logic on paper before touching n8n at all.

Attaching screenshots of the workflow canvas below. Happy to answer questions on specific nodes or decisions.

What would you have done differently?