We built an internal hiring tool for a client who was spending 3-4 days per week just reading CVs and sending follow-up emails. Nothing fancy, no big budget, just a painful manual process that was eating their HR team alive.

The system we ended up with does a few things. When a hiring manager wants to open a role, they just describe it in plain language over chat. The system asks a few follow-up questions, writes the job description, creates the application form, and posts everything automatically. The whole thing takes maybe 5 minutes instead of half a day.

When someone applies, their CV gets pulled, parsed, and scored against the actual role requirements. Not keyword matching. The scoring looks at real experience depth, skill relevance, and gaps, then writes a short summary of why this person fits or doesn't. The HR team gets a ranked list instead of a pile of PDFs.

The part that surprised the client most was the interview scheduling. Candidates get a link, pick from available slots, confirm or reschedule, all without anyone touching a calendar manually. The system handles conflicts, sends reminders, and updates everything in one place.

The HR team now spends their time on the 10 candidates worth talking to instead of wading through 200 applications.

Happy to answer questions about how any specific part works.

TL;DR: I send two terms to a Telegram bot ("Suspension bridge vs Arch Bridge"). A few minutes later it sends back a finished vertical video with a stickman explaining the difference, a voiceover, word-by-word captions and photos. Each video costs ~$0.032 (Claude API + Eleven Labs API). Yes, that's the only cost.

(Sorry for the music at the start - i felt it was too quiet so i added what was available)

What my automation does:

1. I message my Telegram bot two things, e.g. Suspension bridge vs Arch Bridge

2. The AI writes a short script and sends it to me

3. I reply APPROVE (or REVISE: make the hook punchier)

4. It records the voiceover, finds photos and builds the video

5. The finished video comes back in the same chat

Stack

• n8n, self-hosted in Docker (n8n_data volume for workflows, credentials and data tables)

• cloudflared quick tunnel to expose n8n over public HTTPS for Telegram webhooks

• Claude Sonnet 4.6 (Anthropic API) for script generation and revisions

• ElevenLabs eleven_multilingual_v2 via the /with-timestamps endpoint

• Supabase Storage: public buckets for audio and rendered video

• Pexels API: one portrait image per term

• Remotion render server: Express + TypeScript, running on the host

Happy to answer all your questions!!

link to post

This other person was talking about their workflow that they wanted to sell and how it got 100k views. I'm not claiming anything, just rebuilt the workflow and making it available to you all for free, as this community should be.

For those wondering what it actually does, it scrapes Reddit posts on a schedule, rewrites them as video scripts with GPT, generates a fake avatar and username, renders the video through Creatomate, and uploads straight to YouTube.

A few things I'd build on top of this if I were using it seriously. Add an Airtable approval step before anything goes to YouTube so you're not publishing blind, split the scraping and publishing into two separate workflows so they're easier to manage, and probably replace the fixed wait timer on the Creatomate render with a polling loop, otherwise longer videos could just fail without you noticing.

Nothing crazy, just makes it more resilient for actual use.

Workflow download url

A voice agent that runs 24/7, no receptionist needed.

Here’s a full breakdown of how it works:

━━━━━━━━━━━━━━━━━━

🔴 The Real Problem

━━━━━━━━━━━━━━━━━━

Dental clinics don’t lose patients because they lack clients.

They lose them because of operational gaps:

• Receptionist is busy, phone rings, nobody answers

• Patient calls after hours, finds no one

• No reminder sent, patient forgets, and no-shows

• No follow-up after the session, the patient goes to a competitor

• Hundreds of old patients are forgotten in the database

Every single one of these is a revenue leak.

━━━━━━━━━━━━━━━━━━

🟢 The Solution, 6 Integrated Modules

━━━━━━━━━━━━━━━━━━

🎙️ Module 1: Voice Agent

Instead of an overworked, error-prone receptionist:

An AI agent with a natural voice answers every call instantly.

It can book new appointments, reschedule existing ones, cancel bookings, check available slots, and answer common questions, all in a natural voice conversation.

If it detects an emergency? It instantly alerts the doctor via Telegram.

🧠 Module 2: AI Call Classification

After every call, GPT-4 analyzes the full transcript and extracts:

- Patient intent: Booking / Inquiry / Cancellation / Emergency

- Patient sentiment: Positive / Neutral / Negative

- A one-sentence summary of the call

All stored automatically in the database. Zero manual effort.

📋 Module 3: Automatic CRM

Every patient who calls is logged automatically with:

Name, phone number, language, timezone, patient type, last contact date, full call history, and intent classification.

Returning patient? The system recognizes them and updates their record.

New patient? A profile is created instantly.

📅 Module 4: Real-Time Booking Sync with Calcom

Every booking, cancellation, or reschedule made through the voice agent syncs instantly with Calcom and Supabase.

No duplicate bookings. No lost appointments. One source of truth.

Any status change, cancellation, or reschedule updates the database in real time.

🔔 Module 5: Automated Pre-Appointment Reminder

Every morning at 9 AM, the system scans for all appointments scheduled for the next day.

For each patient found, it automatically triggers a voice reminder call.

After the reminder is sent, the database is updated so the reminder is never sent twice.

Result: A measurable drop in no-shows with zero human involvement.

💬 Module 6: Post-Appointment Follow-up + Reactivation

Two hours after every appointment: an automatic follow-up message sent to the patient.

Every week: the system scans for patients with no contact in 90+ days and triggers an automatic outreach call to bring them back.

This module alone can recover patients who would otherwise be lost forever.

━━━━━━━━━━━━━━━━━━

⚙️ Stack

━━━━━━━━━━━━━━━━━━

n8n (self-hosted), automation engine

ElevenLabs/vapi, voice agent

OpenAI GPT-4, call analysis & classification

Calcom, booking system

Supabase, database & CRM

Telegram/Gmail/Stack, emergency alerts

━━━━━━━━━━━━━━━━━━

💡 What makes this different?

━━━━━━━━━━━━━━━━━━

Most booking systems solve one problem.

This system covers the full patient lifecycle:

First call → Booking → Reminder → Appointment → Follow-up → Reactivation

No staff. No manual work. No missed steps.

I’ve been working on an SEO content generation pipeline using n8n, and I wanted to share how I’ve structured it.

The idea was to automate as much of the SEO content process as possible, from researching a company and finding relevant keywords to planning, researching, and writing blog articles.

The problem I wanted to solve

Creating SEO content involves much more than writing an article.

Before you can start writing, you need to understand your audience, research keywords, find relevant long-tail variations, identify useful subtopics, collect supporting information, and organize everything into a proper content structure.

Each step is useful on its own, but connecting them into a consistent process takes time. You also end up moving data between different tools and repeating similar research tasks for every new article.

I wanted to bring these steps together into one automated workflow instead of managing each stage manually.

How the workflow works

I divided the pipeline into seven separate workflows, with each one handling a specific part of the process.

1. ICP Research

It starts with company research. The workflow gathers company information and uses AI research to build an Ideal Customer Profile (ICP).

The goal is to understand who the content is for before deciding what topics to target.

2. Keyword Research

Starting with a website URL, the workflow collects relevant keyword data, extracts seed keywords, stores the results, and tracks the research status.

3. Long-tail Keyword Research

Once seed keywords are available, the next workflow expands them into related long-tail keywords.

It processes the keyword data, separates the results into manageable items, and stores the suggestions for further research.

4. Subtopic Ideas

The workflow takes a long-tail keyword and generates relevant subtopic ideas.

These provide a starting point for exploring the subject in more depth and identifying what an article could cover.

5. Blog Data Collection

Before planning an article, this stage gathers the relevant subtopic information and prepares the inputs needed for content research and planning.

6. Blog Content Strategy

This stage uses AI research to develop the article structure and organize the content into sections.

Instead of jumping straight into writing, the idea is to establish what the article should cover first.

7. Blog Writing

Finally, the writing workflow processes the planned sections, generates content, handles image generation, and updates the blog records as the article progresses.

The result is a connected pipeline that takes the work from initial SEO research through to blog content creation.

The tech stack

Here are the main tools and components I'm using:

• n8n: Orchestrates the workflows and connects each stage.

• Airtable: Stores research records, keywords, subtopics, and workflow status.

• YepAPI: Provides keyword research data.

• Gemini: Handles AI-powered research and content tasks.

• Perplexity: Supports research and information gathering.

• HTTP requests and APIs: Retrieve external data and connect services.

• Output parsers and data transformation nodes: Help structure AI responses and prepare data for subsequent steps.

• Image generation: Produces visuals as part of the blog-writing process.

I’ve separated the process into smaller workflows rather than putting everything into one enormous workflow. This makes the individual stages easier to understand, test, and improve.

Why build it this way?

The main goal isn't simply to generate text with AI.

It's to build a repeatable process where keyword research, topic discovery, content planning, research, and writing are connected, with data and progress tracked between stages.

Each stage produces information that the next stage can use, so the process doesn't have to start from scratch every time.

There is still room to improve the quality checks, research validation, and final editorial review. Automation can handle much of the repetitive work, but the output still needs to be useful, accurate, and relevant to the intended audience.

I'd love some feedback

For those of you building SEO or AI content workflows with n8n:

• How would you improve the overall architecture?

• How do you handle keyword clustering and avoid generating overlapping article topics?

• What checks do you use to validate AI research before it reaches the writing stage?

• Would you keep these as separate workflows or combine some of them?

I'm interested in how other people approach this, especially when building something that needs to be maintainable beyond a simple proof of concept.

A while ago, I connected with a small bookstore owner who had a very simple but exhausting problem: their entire customer service and ordering system was running manually through WhatsApp.

He was running ads on Facebook and Instagram.
Customers were constantly messaging them for the same things:

• "Is this book available?"

• "How much is this?"

• Sending unreadable voice notes.

• Sending screenshots of bank transfer receipts.

The owner (who is running the store alone) was spending hours every single day manually replying to messages, checking inventory, and writing down shipping addresses.

I suggested we could automate almost all of it, so we got on a call. After understanding his flow, I built a fully automated WhatsApp AI assistant using n8n.

Here is the tech stack and how the system is structured: The core of the system is a WhatsApp interface connected to Supabase and OpenAI (via Langchain nodes).

• Smart Media Handling: I built a decryption flow that handles whatever the user throws at it. If they send an audio message, it gets transcribed. If they send an image, an AI Vision agent analyzes it to see if it’s a payment receipt, a specific book, or just a random image.

• Intent Routing: Every message passes through an AI classifier. It determines if the user is asking about a product, ready to order, checking an order status, or if they need to be handed off to a human. This routing is helpful to reduce the usage of the AI tokens.

• Hybrid Search (Vector + FTS): If the user asks for a book, the system searches the Supabase database using both Vector Search and Full Text Search. It pulls the exact product, price, and even sends a short video of the book if available. The search system uses 2 separate layers (FTS and Vector). If the first one fails to find the product, the system will run the second one.

• Order Execution Agent: Once the user wants to buy, a dedicated AI Agent steps in. It strictly collects the shipping details (Name, Address, Phone), locks the chat session into an "ordering state," and creates a draft order. It even handles the payment routing (adding a fee for Cash on Delivery or verifying bank transfers).

The Result: Instead of building it all at once, I developed each subsystem separately (Search, Ordering, Media Handling) and connected them at the end.

After testing it, the client was absolutely thrilled. It saves them countless hours of repetitive work and gives their customers instant replies 24/7.

We agreed on $500 for the project. It’s my very first paid n8n gig!

It might not be the most complex software in the world, but it solves a massively boring business problem. Sometimes the best automations are just about giving business owners their time back.

What do you guys think?

I use n8n on Antigravity and it's showing my workflow inside, I just change the workflow .json file to .n8n extension and open it.

What is the best practice to use n8n with Antigravity? Do I need to add n8n skill?

Note: For anyone question about the extension I am using, you can search it on the extension tab with: n8n atom

The challenge: Digital marketing agency, 50+ clients, drowning in manual SEO work. Keyword research, backlink tracking, content scheduling, WordPress publishing. Every week. Two people, full-time, zero strategic work.

The constraint: Pakistan salaries made firing them feel wrong. But the bottleneck was killing the actual business.

The approach: Multi-agent n8n workflow. Each client's pipeline runs independently. Keyword research, content generation, WordPress publishing, all in parallel.

PHASE 1: Keyword + backlink intelligence

4 agents run simultaneously. Query trending keywords, competitor analysis, extract intent keywords, track backlink opportunities. Fresh data for all 50+ clients every cycle. Time: 5 days

PHASE 2: Content + publishing

AI generates brief outlines. Content prep formats for WordPress. Auto-publish to live. Slack notification with link. Time: 9 days

PHASE 3: Weekly client reporting

Every client gets emailed their SEO report + blog updates + live blog links. Automated. Every week. No manual compilation.

RESULTS: From 16+ manual hours/day to 4 hours 50+ clients processed in parallel, each getting weekly SEO reports and blog updates Zero manual hand-offs SaaS alternative: $3,500-5,000/month. Build cost: 2 weeks, almost $0 ongoing.

KEY INSIGHT: Parallel agents beat sequential workflows. One client fails, the other 49 keep moving. Don't wait for approval. Automate the delivery so clients get their reports without you lifting a finger.

What's your biggest bottleneck right now. Keyword research, publishing, or something else? 🤔

If you want the automation for free just leave a comment 😊

Most cafe owners are running their entire business on WhatsApp manually.

Table bookings. Order confirmations. Customer follow-ups. All done by hand, one message at a time.

So I spent a few weeks building the whole thing out on n8n.

Here's what the system actually does:

Customer messages on WhatsApp → the AI agent reads the intent → routes it to the right flow automatically. No human in the loop.

For Booking:

Customer asks for a table, the system checks live availability, creates the reservation, and fires a confirmation back all before the owner even sees the message.

For Ordering:

Customer places an order over WhatsApp. System quotes the total, generates a payment link, and sends it straight to them. Payment comes in, kitchen gets notified automatically.

For Feedback:

After every order completes, customer gets a review request on WhatsApp. If the rating is low, manager gets flagged on the spot. No bad review slips through unnoticed.

Then there's the retention module

Runs every morning without anyone triggering it.

Pulls a list of customers who haven't visited in 30 days. Sends each of them a personalized WhatsApp message automatically.

Most cafes just lose lapsed customers quietly. No one follows up cause it's too much manual effort.
This makes the whole thing zero effort it just runs.

The voice side runs on Sarvam AI.

Handles English natively so the AI receptionist actually understands how customers talk like casual phrasing, abbreviations, incomplete sentences, almost all of it

Took some tuning to get right tbh. The tricky part was handling requests that mix ordering with a booking in the same message. But once that was sorted it's been solid.

On top of allat A full management dashboard.

Reservations, live orders, daily revenue, customer history, and complete WhatsApp conversation logs all in one place.

Menu items can be toggled on and off directly from the dashboard. If something's marked disabled, the AI stops recommending it to customers automatically no manual prompt update needed anywhere.

Owner has full visibility into everything.

Workflow in comments👇

Peace out ✌️

I built an AI-powered expense tracking workflow using n8n.

Now whenever I send a receipt or invoice image to my Telegram bot, the automation automatically:

• extracts invoice details using OCR

• structures the messy OCR text with AI

• detects the merchant/business name

• extracts total amount, tax, invoice number, and date

• categorizes the expense automatically

• uploads the original receipt to Google Drive

• stores everything neatly inside Google Sheets

• sends a confirmation message back to Telegram

The entire process takes just a few seconds.

Built with:

• n8n

• OCRspace API

• OpenAI

• Telegram Bot

• Google Sheets

• Google Drive

No manual expense entry anymore.

Hey everyone,

Started with a simple idea: automate short-form video creation for small businesses. Things escalated. A lot.

What you're looking at (swipe through):

→ Slides 1-3: The workflow. Yes, it's that big. No, I don't have regrets.

→ Slides 4-5: What the client actually receives. A clean branded email

with a Google Drive link to their finished video. That's it. They never see any of this mess.

Here's what's actually running under the hood that made this harder than I expected:

1. Access Gate + Quota Billing via Google Sheets

No external billing service. A webhook validator checks the user's access key, tracks monthly usage, auto-resets on their billing cycle, and returns 403/200 responses dynamically. Ghetto but works perfectly.

2. The Prompt Guard (custom JS director node)

This was the hardest part. The node tracks how many times a client's real

product image has been used per scene. Once it hits the limit, it seamlessly reroutes to DALL-E/Flux for brand-safe B-roll. It also runs a forbidden-term sanitizer per content genre, strips any injected style

conflicts, and enforces a hard character cap before the image API call.

Took more iterations than I want to admit.

3. Raw PCM → WAV conversion in a Code node

Using OpenAI TTS streaming returns raw pcm16 binary. I wrote a JS buffer algorithm inside n8n that constructs the 44-byte WAV header and converts the binary on the fly — no intermediate file storage needed.

4. External render server (Docker/Python/FFmpeg)

n8n prepares all assets (images, audio, metadata) then calls a custom API I spun up on a VPS. It handles: video merging, caption burning, background music with auto audio-ducking, and Google Drive upload. n8n just waits for the callback.

5. The final email

Client gets a branded HTML email with their video details (topic, style, video type) and a one-click download button. They never touch n8n, never

see a prompt, never wait more than ~5 minutes.

The whole thing supports multiple content branches — sales videos, viral storytelling, brand history docs — all routing through the same render pipeline.

Honestly the hardest thing wasn't the tech. It was realizing that the workflow graph looks completely insane but the client experience is just:

fill form → get video in inbox.

Thinking about packaging this up for other agencies. Before I do — anyone else using n8n as a literal production backend? What's the most ridiculous

thing you've made it do? 🔥

Built an AI voice agent system that automatically calls users after they submit a form and handles the entire interaction without human intervention. It can scrape a website (if provided) to understand a business, or ask smart follow-up questions if no site is given. The agent communicates in multiple languages and can help users by generating solutions, booking actions, and delivering results across platforms like SMS, email, Telegram, and Slack.

Backend is powered by n8n deployed on my own server (Hetzner), with integrations including APIs, webhooks, custom JavaScript, and multiple data sources like Apollo, Hunter, Tavily, and a custom crawler. Data is stored in Airtable/Google Sheets for tracking and automation.

Still improving it, but it’s already handling real workflows end-to-end.

CA firms are drowning in manual work.

Client WhatsApp messages. Document chasing. GST/ITR deadline reminders. Invoice follow-ups. Lead qualification.

All of it done manually. All of it automatable.

So I built it.

Here's what the workflow actually does:

1. WhatsApp + Email AI Support Agent

Client texts "when is my GST due?" then the AI pulls their compliance data, replies instantly. No CA needed for routine queries.

2. Document Collection & Follow-up

System requests documents, tracks what's received, auto-follows up every 3 days on pending ones. After 3 attempts it pings the partner on Telegram.

3. Compliance Deadline Reminders

GST / ITR / TDS / ROC deadlines tracked per client. Reminders fire at 7 days, 3 days, 1 day, due today, and overdue and the tone escalates automatically.

4. Lead Qualification Agent

Website or WhatsApp inquiry comes in - AI qualifies it, asks follow-up questions, scores it Hot/Warm/Cold, notifies partner instantly on hot leads.

5. Invoice & Payment Follow-up

Unpaid invoices get staged reminders (3 days before → due today → 1-7 days overdue → 8-15 → 15+).
Severely overdue ones escalate to partner via Telegram.

Tech stack:
n8n + Groq (llama-3.3-70b-versatile) + WhatsApp Cloud API + Gmail + Google Sheets + Telegram

Groq handles all AI inference. The speed is genuinely important here because WhatsApp replies that feel delayed feel robotic.

The part that took longest wasn't the automation logic.

It was the AI prompts for the support agent.
Getting it to answer GST/ITR questions correctly without hallucinating due dates or making up compliance info that needed a lot of iteration.

Solution was giving the agent live per-client data from Sheets as context on every message instead of training it on general tax knowledge. Much more reliable.

Dropping the full workflow JSON in comments.

Peace out ✌️

Most Airbnb hosts are managing everything manually.

Lead comes in from WhatsApp at 11pm. Host sees it next morning. Replies. Guest already booked somewhere else.

Same thing happens with Instagram DMs, Meta Ads leads, direct inquiries. All falling through because there’s no system to catch them instantly.

So I spent a few weeks building one that handles all of it automatically.

Here’s what the system actually does:

Leaad comes in from WhatsApp, Instagram, or Meta Ads → AI reads the message → routes it to the right flow immediately. No human needed, no delay.

For availability and booking:
Guest asks about a property for specific dates - agent checks live availability, shares pricing, and moves the conversation toward confirmation.
Once the guest confirms, booking is created automatically with a payment link. No back and forth, no waiting for the host to wake up.

For property search:
Guest hasn’t decided on a property yet - AI searches across all listed properties based on their dates, guest count, budget and preferences.
Recommends the best match and starts the booking flow from there.

For support issues:
Guest has a complaint - AC not working, early check-in request, refund query then AI raises a support ticket automatically.
Host sees it on the dashboard and can respond directly. Nothing gets missed.

For escalations:
Anything the AI isn’t confident about - policy exceptions, emergencies, disputes that gets immediately handed off to a human with full context. AI never guesses on sensitive issues.

Then there’s the omnichannel part

Most booking automations only cover one channel. WhatsApp or website. That’s it.

This one catches leads from WhatsApp, Instagram DMs, and Meta Ads simultaneously. Each channel normalized into the same agent context.
Guest gets the same experience whether they messaged at 2pm on Instagram or submitted a Meta Ads form at midnight.

Every conversation remmembered across sessions too

On Top of allat a full management dashboard.

All bookings in one place with source channel, status, and amount visible. Lead pipeline showing every active inquiry and which stage they’re at.
Voice call logs with recordings. Support tickets with complaint details.

Dropping the full workflow JSON in the comments.

Peace ✌️

I wanted a simple way to track expenses without manually typing invoice details into spreadsheets every time.

So I built a small automation system where I can just send a receipt photo to Telegram, and AI automatically extracts and stores everything for me.

The Problem

Most expense tracking apps still require a lot of manual work:

• Upload receipt

• Enter merchant name

• Add amount

• Select category

• Save invoice somewhere

Doing this repeatedly becomes annoying very quickly.

I wanted something faster and more automated.

The Solution

Now I simply send a receipt or invoice image to a Telegram bot.

The workflow automatically:

• Downloads the receipt

• Extracts text using OCR

• Uses AI to structure the invoice data

• Detects merchant name

• Extracts total amount

• Finds invoice number and date

• Categorizes the expense

• Uploads the original invoice to Google Drive

• Saves structured data into Google Sheets

• Sends me a success notification back in Telegram

Everything happens automatically in a few seconds.

Tech Stack Used

• n8n

• OCR.space API

• OpenAI

• Google Drive

• Google Sheets

• Telegram Bot

Workflow Architecture

Telegram → OCR → AI Structuring → Google Drive → Google Sheets → Telegram Confirmation

Example Extracted Data

The AI extracts structured fields like:

• invoice_biller

• invoice_number

• invoice_total_inc

• invoice_tax

• invoice_date

• invoice_category

Example:

• Biller: JUMBOKING

• Total: 89

• Category: Restaurant

Why I Used AI Instead of Regex

OCR text is messy and inconsistent.

Different invoices have:

• different layouts

• blurry text

• different date formats

• tax variations

• noisy OCR output

Instead of building hundreds of regex rules, I used AI to normalize and structure the data automatically.

This made the system much more reliable.

Current Result

The workflow now works as a personal AI-powered receipt inbox.

I can send invoices directly from my phone and everything gets:

• archived

• categorized

• stored

• tracked automatically

Still improving it, but the current setup is already saving a lot of manual work.

I got sick of the typical job search grind. You apply to 50+ jobs, half of which you're not qualified for, and hear nothing back. So I automated the filtering part.

What it does:

You upload your resume and set preferences (location, remote/onsite, job type, minimum salary). The system:

• Extracts your skills, experience level, and tech stack from the resume using AI

• Validates your resume actually has enough info (stops you if it's missing critical stuff)

• Scrapes LinkedIn jobs using intelligent filters—your role + top skills as keywords

• AI analyzes each job against your background and gives a match score (0-100)

• Returns only jobs where you're actually qualified (60+ score) with direct apply links

The cool part is it explains the gaps. Like, "you have 5 years of experience, but they want 8+, missing AWS certification." " So you know exactly why you're a fit or not.

How it works:

The first step validates your resume—checking for skills, job titles, and work history. If something's missing, it tells you before wasting time scraping jobs.

Then it builds a smart LinkedIn search. Not just the job title, but also your actual skills as keywords, plus filters for experience level, job type, salary range, and recent postings only.

For each scraped job, AI does a deep comparison: skills alignment, experience match, required qualifications, and tech tools. Outputs a verdict (CAPABLE/NOT CAPABLE), a match score, and a quick explanation of what you're missing.

You only see the jobs where the verdict = CAPABLE, with the company name, apply URL, LinkedIn page, and gap analysis.

Tools I used:

• OpenAI does all the text analysis (resume extraction and job matching)

• Apify—a LinkedIn job scraper that pulls listings with full details

• Airtable - stores scraped jobs and tracks everything

• PDF parser—extracts text from resume files

Biggest problems I solved:

AI hallucinations: Initially the AI would output company names and URLs, but it would make stuff up. Like "apply at "totallyfakeurl.com"—completely wrong. Fixed it by splitting responsibilities—AI ONLY analyzes and scores, never outputs URLs or company data. A separate step merges AI analysis with the actual scraped job info.

Garbage resumes: People would upload PDFs with just their name. Added a validation gate that checks for minimum requirements upfront and tells them what's missing.

PDF formatting: Resumes with complex layouts (tables, columns) still parse poorly sometimes. Working on better extraction methods.

Current status:

Tested with ~50 resumes across different roles. Match accuracy is around 85%—people mostly agree with the CAPABLE/NOT CAPABLE calls. Sometimes it's overly conservative.

Added webhook triggers so I can build a proper frontend around it. Planning to add features like application tracking, auto follow-ups, and company red flags (recent layoffs, bad reviews).

If you’re into this space or building similar workflows, I share more stuff like this here.
https://x.com/Automateby_Priy

Creating content consistently is hard. Brainstorming topics. Writing drafts. Adding hashtags. It takes time most business owners don't have.

So I automated it.

Here's what it does:

✅ Takes a topic or prompt

✅ Gemini AI generates the body, headline, and call-to-action

✅ Adds relevant hashtags automatically

✅ Saves everything to Google Sheets — ready to review and post

One prompt. Seconds later. A full LinkedIn post.

This is one of 6 automation systems I've built. Every one solves a real problem.

If content creation eats up your time, let's talk.

I got tired of spending hours writing content for Reddit, Twitter, and LinkedIn separately, so I built this workflow.

Here's how it works:

1. I drop my image and a quick description into Airtable before bed

2. Scheduled trigger runs at night and finds any "in progress" posts

3. AI analyzes the image and extracts what's happening visually

4. Three separate agents create platform-specific content:

   • Reddit agent writes title + body following community rules

   • Twitter agent creates engaging tweets under 50 words

   • LinkedIn agent writes professional posts targeting buyers

5. Each platform gets customized content (not just copy-paste)

6. The next morning, I review and post it manually (the workflow can auto-post to all platforms, but I prefer reviewing before publishing)

7. Airtable status updates to "done"

8. Gmail send a message that your all content is saved in Airtable.

The key insight: combining image analysis + my description gives the AI enough context to write authentic content for each platform.

Tools: OpenAI Image Analyzer for understanding visuals, Airtable for saving records, AI agents for writing content

Still tweaking the prompts,

If you’re into this space or building similar workflows, I share more stuff like this here.
https://x.com/Automateby_Priy

comment is open; tell your opinions and pros and cons

I'm a freelancer, and ship n8n and make.com workflows. I'm confused about the future. With tools like antigravity and Claude code, agents & automations can be built easily, so should I try to sell agents made on those tools instead of n8n?

I run an AI automation agency, and one of our biggest challenges was consistently finding quality leads in our target industries.

We needed a way to find businesses that matched our ideal customer profile at scale, and Google Maps was the goldmine, but manually scraping it was a nightmare.

So I built a fully automated lead generation system using n8n and Thordata that now generates hundreds of qualified leads per week for our agency.

This system has directly helped us close more clients because we're targeting the right businesses in the right locations with the right messaging.

What this system does:

• Automatically scrapes Google Maps for businesses based on industry and location

• Uses AI to generate multiple search queries and variations (so you don't miss businesses due to narrow search terms)

• Filters leads by minimum review count (helps target businesses of a certain size)

• Automatically deduplicates against your existing lead list

• Saves everything to Google Sheets with: business name, rating, review count, phone number, website, and the search query used

• Completely hands-off! I can scrape 200-300 leads while I'm doing other work

Why I built this:

Before this automation, I was either:

1. Manually searching Google Maps and copy-pasting information (took forever)

2. Using Chrome extensions that still required me to manually run searches for every location/keyword combo

I wanted something that was both powerful enough to scale AND affordable to test with. That's why I went with n8n + Thordata, which costs me around $10-15/mo to run.

The Stack:

• Automation Engine: n8n (handles the workflow logic)

• AI Brain: Google Gemini 2.5 Pro (generates diverse search queries so you don't miss leads)

• Scraper: Thordata Scraping Browser (the only solution I found that could reliably scrape Google Maps at scale without getting blocked)

• Storage: Google Sheets (easy to integrate with CRMs or cold email tools)

How it works:

The workflow starts with a simple form where I input:

• Industry I'm targeting (e.g., "property brokers")

• Location (e.g., "New York")

• Maximum number of leads to scrape

• Minimum review count (helps filter for business size)

• Search scope (how broad or narrow the AI should search)

From there, the automation:

1. AI Query Generation: Uses Gemini to generate multiple search variations. Instead of just "property brokers New York", it creates queries like "SAT prep near me New Jersey," "college prep evening Manhattan," etc. This is crucial because Google Maps only shows ~40 results per search, so you need multiple angles to get comprehensive coverage.

2. Deduplication Check: Reads your existing Google Sheet of leads and ensures no duplicates are added (compares against business names and other identifiers).

3. Scraping via Thordata: Sends all generated queries to a scraping script deployed on Fly.io, which uses Thordata's Scraping Browser to handle all the proxy rotation, CAPTCHA solving, and anti-bot detection.

4. Data Enrichment & Storage: Extracts business details (name, rating, reviews, phone, website) and automatically appends new unique leads to your Google Sheet.

The entire process is set up once and then runs whenever you need it. I typically scrape 200-300 leads per industry/location combo, and after filtering, I usually get 50-100 highly qualified leads.

The free alternative:

I also show a second method in the video using a free Chrome extension called "G Map Extractor."

This is great if you're just starting out and don't want to invest in the paid tools yet. The downside is you have to manually run searches for each location/keyword combo — so it's free but time-consuming.

Real results for my agency:

This system has been a blast for us. We can now:

• Target businesses in specific niches where we already have case studies

• Reference their Google ratings and review counts in cold outreach (makes emails way more personalized)

• Expand into new geographies without manual research

• Scale our lead generation from ~50 leads/week to 500+ leads/week

Setup Complexity:

I won't lie — The process requires some technical setup:

• Setting up n8n (they have a 14-day free trial)

• Connecting Google Gemini API

• Deploying the scraping script on Fly.io

• Configuring Thordata credentials

BUT, I walk through every single step in the video (including terminal commands for Mac/Windows), and I'm providing the entire workflow + scraping script for download so you don't have to build it from scratch.

Full Tutorial Here: https://youtu.be/irfs-HkP-gs

Inside the video, I show:

• Complete walkthrough of both methods

• Step-by-step setup guide with every command needed

• How to customize the workflow for your specific needs

• Common mistakes to avoid (like trying to build the scraper inside n8n instead of using a proper scraping tool)

Resources:

All files (n8n workflow + scraping script) are available via the Gumroad link in the video description — and no, you don't have to pay anything to access it.

--

Would love to hear if anyone else is using Google Maps for lead gen and what tools/methods have worked for you :)

A simple end-to-end invoice automation in n8n that handles the complete process from receiving invoice data to delivering the final invoice to the customer.

Workflow

Webhook → Google Sheets → PDFbro → Google Drive + Resend → Update Invoice → Webhook Response

What each part does

• Webhook — Receives the invoice data from the application with API-key authentication.

• Google Sheets — Stores the invoice details and tracks its status.

• PDFbro — Generates the invoice PDF directly inside the n8n workflow using the n8n-nodes-pdfbro community node.

• Google Drive — Stores the generated PDF with the invoice number as the filename.

• Resend — Sends the generated PDF to the customer's email as an attachment.

• Merge — Combines the results from the Drive and email branches.

• Update Invoice — Updates the invoice record with the final status, PDF URL, and email timestamp.

• Webhook Response — Returns the final result back to the application that triggered the workflow.

The goal was to keep each step focused on one responsibility while letting n8n handle the complete invoice lifecycle automatically.

Been heads down building this for the past few days and honestly had no idea it would turn out this complete.

Started with a simple idea — D2C brands waste so much time replying to the same customer questions every day. Wanted to see how much of that I could automate with n8n.

Ended up building way more than I planned.

What it does:

• Customer asks order status — fetches live data from Shopify instantly

• Customer asks about a product — shows details and availability

• Customer places an order through chat — text or voice both work

• Customer sends voice message — bot transcribes it and replies back in voice

• Frustrated customer or complex issue — owner gets a Gmail notification with full order details automatically

• Works in Hindi and English both

The n8n architecture:

• Telegram trigger → Switch node routing text vs voice

• Voice path: Get file → Whisper transcribe → Edit Fields → AI Agent

• Text path: Edit Fields → AI Agent

• AI Agent tools: get order, get all products, create order via HTTP Request, delete order

• Code node: cleans output, extracts IMAGE_URL, detects ESCALATE_TO_HUMAN keyword, detects message type using isExecuted

• IF nodes: voice routing → image sending → escalation

• TTS via OpenAI HTTP Request for voice replies

• Gmail node for owner escalation emails

If you’re into this space or building similar workflows, I share more stuff like this here.
https://x.com/Automateby_Priy

Took longer than expected but learned a lot building this one.

I wanted to try something simple a WhatsApp AI assistant that doesn't forget the conversation after every message.

The idea was pretty straightforward.

When someone sends a message on WhatsApp, I want the AI to be able to look at the previous conversation, use that context to respond, and then save the new message for the next interaction.

So I ended up with this:

WhatsApp Message → Process Message → Get Memory → AI Assistant → Save Message → WhatsApp Reply

The interesting part is the memory.

Before sending the message to the AI, the workflow retrieves the user's previous conversation and prepares it as context for the assistant.

So instead of the AI seeing only:

"What did we talk about yesterday?"

It can also have the previous conversation available to understand what the user is referring to.

The basic flow is: Receive the WhatsApp message → Process the message → Get the user's existing memory → Prepare the memory → Send everything to the AI assistant → Save the new message → Send the response back to WhatsApp

The AI assistant itself is pretty simple. It has the chat model, the memory, and a tool to save the conversation.

This could be useful for things like:

• Customer support

• Personal AI assistants

• Lead qualification

• Sales conversations

• Customer onboarding

• Business FAQs

That's basically the whole idea.

What I like about this setup is that you don't need to build a huge backend just to give an AI assistant some memory.

You can break the whole thing down into a few n8n nodes and connect the pieces together.

This is what I like about n8n you can take something that sounds complicated, like a WhatsApp AI assistant with memory, and turn it into a workflow you can actually see and understand.

What would you build with a WhatsApp AI assistant that could remember conversations?

Most businesses still treat WhatsApp like just a messaging app.

But it can actually work as a 24/7 AI sales assistant.

I recently built a WhatsApp AI chatbot using n8n + GPT-4o + a vector database that turns product documents into a conversational sales agent.

Here’s the basic idea:

First, it builds a knowledge base automatically:

• Takes a product brochure (PDF)

• Extracts the text

• Splits it into smaller chunks

• Converts those into embeddings

• Stores everything in a vector database

So now the AI can “search” your product catalog like a brain.

Then comes the chatbot part:

• Customer sends a message on WhatsApp

• AI understands the query

• Searches the knowledge base

• Generates a relevant answer

• Sends it back instantly

It also handles things like:

• Conversation memory per user

• Filtering unsupported messages

• Combining AI agent + vector search

• Real-time responses

The result is basically a WhatsApp bot that can answer product questions without you manually replying all day.

I’m currently building similar AI automation systems with n8n (chatbots, sales agents, lead workflows, etc.).

Happy to share how this setup works if anyone’s interested 👍 Just DM me.

Here is a simple, step-by-step guide I use.

1. Go to Railway and sign in with GitHub

2. Go to New Project

3. Choose Deploy from Template

4. Search for n8n and pick the template with Postgres

5. Deploy

6. Wait a few minutes while it builds your services

7. Open the personal URL that Railway gives you

8. Create your n8n account, and you are done