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I built an n8n workflow that automatically enriches company data from website content using OpenAI & Google Sheets.
The workflow automatically:
Fetches company website content
Cleans and processes website data
Analyzes company information using AI
Identifies the company industry
Determines the target audience
Identifies whether the company is B2B or B2C
Generates the company value proposition
Updates the enriched data in Google Sheets
Saves hours of manual company research
Perfect for sales teams, marketing teams, lead generation, agencies, businesses, and automation enthusiasts.
Comment "ENRICH" and I'll send you the workflow.
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Shopify Abandoned Cart Recovery Automation 🛒
Ever wondered how many potential orders an online store could miss when customers add products to their cart but leave without completing checkout?
I built an abandoned cart automation workflow using Activepieces, Shopify, Google Gemini, Gmail, and WhatsApp Business.
⚙️ How It Works
🛒 Cart Trigger: Detects an abandoned checkout event from Shopify.
🔀 Conditional Routing: Routes the workflow based on configured conditions.
🤖 AI-Powered Messaging: Gemini generates a personalized reminder message.
💬 WhatsApp Reminder: Sends a cart reminder through WhatsApp Business.
📧 Email Reminder: Sends a follow-up email to encourage the customer to complete checkout.
💡 How It Can Help E-commerce Businesses
Remind customers about products left in their carts.
Reduce repetitive manual follow-up work.
Make customer communication more consistent.
Create an opportunity to recover potentially lost sales.
Tools Used: Activepieces | Shopify | Google Gemini | Gmail | WhatsApp Business
Project Type: Personal Automation Project
I built this workflow to demonstrate how automation can support abandoned cart recovery for Shopify and D2C businesses.
Interested in automating your store's customer follow-ups? Let's connect.
The 30–60 minute delay then 24-hour follow-up is a sensible cadence. The part I would harden next is the stop conditions: once the cart converts or the customer replies, every channel should cancel, and WhatsApp especially needs a clear opt-in trail before the first send....
Multi-Model Social Media Agent — Automated Content Workflows
Built by QuantEve Technologies | AI Automation & Full-Stack Engineering
Designed and developed an AI-powered social media workflow that streamlines content research, drafting, evaluation, approval, publishing, and performance analysis.
Rather than relying on a single model for every task, the system uses a multi-model workflow to organize content operations into manageable stages, with persistent state and human review where needed.
Core Capabilities
Content Research: Organizes topics and research inputs into a structured content workflow.
AI-Assisted Drafting: Generates social media content for platforms such as X and LinkedIn.
Multi-Model Processing: Supports model-provider alternatives and fallback handling for more resilient workflows.
Draft Evaluation: Adds an evaluation stage to review and improve content before it moves forward.
Human Approval: Keeps content review separate from publishing so drafts can be checked before external actions.
Workflow Persistence: Uses SQLite-backed queues and stored state to track content as it moves through the system.
Performance Feedback: Organizes publishing analytics and feedback to inform future content decisions.
Engineering Focus
The system brings AI model orchestration, application logic, persistent workflow state, and social media operations together in one maintainable automation pipeline.
Technology Stack
Python / Node.js (depending on the deployed component) · LLM APIs · REST APIs · SQLite · Workflow Automation · Social Platform Integrations
Built by QuantEve Technologies to demonstrate how AI can automate repetitive content operations while keeping the workflow organized, reviewable, and controllable.
Keeping content review separate from publishing, with state in SQLite queues, is a sensible setup. When a model provider fails mid-run and the fallback kicks in, does the queue retry just that stage with the next model, or does the draft restart from research?
🚀 Is Your Customer Support Team Spending Too Much Time on Repetitive Tasks?
Customer enquiries, delayed responses, manual ticket handling, and disconnected systems can slow down business operations.
I build AI Business Assistant solutions using n8n, AI Agents, and API integrations.
Here's what the solution can automate:
✅ Customer enquiry classification and response drafting
✅ Priority-based routing and escalation
✅ Ticket creation and tracking
✅ CRM updates and team notifications
✅ Human-in-the-loop approvals for complex cases
✅ Integration with existing business tools
The goal: Faster response handling, less repetitive work, and better operational visibility.
I've built working AI assistant and customer support automation demonstrations and am available for custom implementation and subcontracting projects.
Are you a business owner, operations manager, or automation agency looking to automate customer support?
📩 DM me with your requirements. I'll share a relevant workflow demo and discuss a fixed-price implementation.
#n8n #AIAgents #CustomerSupportAutomation #BusinessAutomation #AIIntegration
For the human-in-the-loop approvals on complex cases, what decides when a ticket gets escalated: a confidence score from the classification step, or rules like keywords and customer tier? That threshold is usually where these n8n flows either save time or create review noise.