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Flagship Case Study

AI Lead Automation Platform

Designed to help recruitment businesses respond instantly, qualify leads automatically, and reduce manual work using conversational AI and workflow automation.

Project Overview

Role
Solution Designer & AI Automation Developer
Project Type
Business Automation Platform
Industry
HR & Recruitment
Status
Prototype completed

The Problem

While discussing website development with a recruitment consultancy, I discovered they were losing potential clients because WhatsApp enquiries often went unanswered outside working hours.

The company also had no simple way to organise leads or track conversations without manually responding to every enquiry.

The challenge wasn't simply answering messages—it was creating an intelligent workflow that could respond instantly, collect GDPR consent, capture customer information, and notify the business automatically.

The Solution

I designed an AI-powered lead automation workflow that combines conversational AI, WhatsApp messaging, workflow automation, and lightweight data management.

Instead of acting as a simple chatbot, the platform guides customers through a structured conversation, collects GDPR consent, captures lead information, stores it securely, and automatically notifies the business whenever a qualified lead is generated.

System Design

Architecture

  1. User
  2. WhatsApp
  3. Twilio API
  4. n8n Automation
  5. OpenAI
  6. Lead Qualification
  7. Google Sheets
  8. Email Notification

Key Features

  • AI Conversations

    OpenAI-powered contextual replies.

  • GDPR Consent

    Collect consent before storing customer information.

  • Lead Qualification

    Automatically capture structured customer details.

  • Google Sheets Integration

    Lightweight administration for businesses.

  • Email Notifications

    Notify administrators immediately when new leads arrive.

  • Data Deletion

    Users can delete their data using a simple command.

Engineering Decisions

  • 01

    Why OpenAI?

    Strong conversational quality and efficient token usage.

  • 02

    Why Twilio?

    Meta Business verification became a deployment blocker, so Twilio provided a faster and more reliable production path.

  • 03

    Why Google Sheets?

    Simple, accessible, cost-effective, and perfectly suited to the client’s operational needs.

Biggest Challenge

The most difficult part wasn't building the AI workflow. It was obtaining production access to the WhatsApp Business API.

Initially I attempted to integrate directly with Meta Business Platform. Business verification requirements, compliance issues, and API restrictions delayed deployment.

Rather than abandoning the project, I redesigned the architecture using Twilio's WhatsApp API, allowing the system to achieve the same business outcome with a more practical deployment strategy.

Lessons Learned

  • Business-first thinking

    Technology follows business needs.

  • Deployment matters

    Building software is only part of delivering a solution.

  • APIs have constraints

    Technical architecture must adapt to external platforms.

  • Automation creates value

    The greatest impact often comes from removing repetitive manual work.

Technology Stack

  • OpenAI
  • Twilio
  • n8n
  • Google Sheets API
  • REST APIs
  • Prompt Engineering
  • Workflow Automation

Interested in how I designed this system?

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