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Nobody wants to log into another portal just to click "approve." That single friction point was quietly costing one of my clients days on every invoice.
Their finance managers kept ignoring notification emails because approving meant logging into an internal platform, finding the invoice, and clicking through a few screens. Invoices piled up while everyone waited on one signature.
The fix: an email-based approval flow built in Make.com. The approver gets an email with the vendor, amount, and line items already visible, plus two buttons — Approve or Reject. One click fires a webhook, the decision gets recorded, and the invoice moves forward automatically. No portal, no password.
Quick tip if you're building something similar: if an approval step requires someone to log in just to say yes or no, that step is usually removable. Put the decision and the context directly in the notification, then handle the edge cases — someone clicking twice, a link expiring, a reply-by-text instead of a click — with idempotency checks and expiring tokens.
Result: approval time dropped from 3-4 days average to under 4 hours.
The friction in most approval workflows isn't the process. It's the login.
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Hi everyone!
A lead fills out a form on your website. What happens in the next 5 minutes
decides whether they become a customer — or forget you exist.
THE PROBLEM
Leads sit in a mailbox or a spreadsheet until someone notices them. By then,
78% of buyers have already contacted a competitor.
THE SOLUTION
Lead Machine — an n8n pipeline that handles every inbound lead automatically,
from form submission to follow-up email in about 30 seconds, 24/7:
webhook → validation → duplicate check → Google Sheets → welcome email
→ Telegram alert to the manager → automatic follow-up in 24h
Every step is plain n8n nodes: no AI keys, no token fees, no monthly SaaS.
Validation rules, email texts, follow-up delay — all editable in the n8n UI.
It runs on YOUR infrastructure: credentials stay with you.
LIVE DEMO — send a test lead and watch the pipeline:
https://dvc-shared.dvc-ai.workers.dev/t/leadmachine-demo
Full case study on my profile.
Amazing
AI-Powered Gmail Inbox Assistant: Turning Email Overload into an Organized Workflow
A busy inbox can become a productivity problem.
For a VA or business owner handling a high volume of emails, manually reading every message, deciding its priority, summarizing it, determining what action is needed, and organizing it takes time.
And when everything sits in one inbox, important messages can easily get lost among routine notifications and newsletters.
I built an automation to solve that problem.
The Solution
I created an AI-powered Gmail inbox assistant that captures, analyzes, organizes, and records incoming emails automatically.
The workflow starts when a new email arrives in Gmail.
New Gmail → AI analyzes the email → Information is recorded in Google Sheets → Zapier Paths route the email → Gmail label is applied
The AI extracts useful information from each email, including:
Priority – how urgently the email needs attention
Email summary – a concise explanation of what the message is about
Recommended action – what should be done next
Service/category – used to determine how the email should be handled
The information is then captured in a Google Sheet, creating a structured record that can be reviewed, tracked, and used for further workflows.
From there, Zapier Paths use the classification to route the email to the appropriate action.
For example:
Urgent email → AI identifies the priority and summarizes the message → information is recorded in Google Sheets → Urgent Path runs → Gmail applies the Urgent label.
WorkApp/Sites notification → AI identifies the category → details are recorded in Google Sheets → WorkApp/Sites Path runs → the corresponding Gmail label is applied.
Newsletter → AI classifies and summarizes it → information is recorded in Google Sheets → Newsletter Path runs → the Newsletters label is applied.
What Problem Does This Solve?
This automation addresses several repetitive administrative tasks at once.
Instead of manually opening emails, interpreting them, recording important details, and organizing them, the workflow handles the first layer of processing automatically.
It helps create:
Less manual sorting
Emails are categorized automatically.
Better visibility
The Google Sheet provides a structured overview of incoming messages, their priority, summaries, and recommended actions.
Faster prioritization
Users can identify emails requiring attention without reading through the entire inbox first.
A searchable record
Important email information is captured in one place instead of remaining buried inside an inbox.
How Businesses Can Use It
The workflow can be adapted to different businesses and inboxes.
For example, a company could create categories such as:
Client enquiries | Leads | Support requests | Finance | Internal communication | Urgent | Newsletters
The Google Sheet can then become a simple operational dashboard or the starting point for additional automations.
For example, a business could later extend the workflow to:
Urgent email → notify the responsible team member
Client enquiry → create a lead in a CRM
Follow-up required → create a task
Support request → route to the appropriate team
This makes the workflow more than an inbox organizer. It can become part of a wider business process automation system.
Tools Used
Gmail – captures incoming emails and applies labels
AI by Zapier – analyzes and classifies email content
Google Sheets – stores the email priority, summary, recommended action, and category
Zapier Paths – applies conditional logic and routes emails based on their classification
What This Project Demonstrates
My approach to automation is simple:
Find the repetitive problem first. Then build the system around it.
This project started with a common administrative problem—too much email and too much manual sorting—and turned it into a workflow that can analyze information, structure it, make routing decisions, and trigger actions automatically.
I'm building my skills in AI automation with a VA mindset: understanding the business problem first, then creating practical systems that reduce manual work and improve how information flows.
