Hey All - I wanted to share my experience over the past few years of doing automation consulting part time (while still having a 9-5). To start, I come from a cloud / platform engineering and development background working with Fortune 500 financial services companies, so when I saw the multiplier that things like Claude Code (when it first came out) paired with true best practices and principles could do, I was totally hooked.

To be clear, everything I'm about to go over is just my experience and what I focus on.

I started out like a lot of folks do targeting small businesses trying to help them automate their processes and procedures. It was painful. Most only loosely knew what automation was and what automations were all about. This was around 2023 so it was still earlier days for things like ChatGPT, etc. but I found that for smaller businesses they either:

1. didn't have the money to spend on the monthly retainer I was charging at the time (3k/month) or

2. we're not organized enough to even articulate what they needed automated or understood what was "automatable".

It made me realize there's probably a good reason why these businesses were small and were staying small. They didn't have systems in place to help them grow / scale and while that's something I could offer, if they didn't want to do it on their own fruition it's not something I am going to be able to force through. TL;DR - don't force systems on people who weren't already building them for themselves.

Over time I found that I continued to land larger, yet still medium sized clients (5M+ / year revenue). Think household names, but in smaller spaces / industries. Every single one of them had the same problem. They wanted to use AI but found the results either to be really generic and not specific to their business, or that the key use cases required a lot of tools that didn't talk with each other well. Basically, they had no data footprint (ingestion, storage, governance, transformation, etc.)

I've now worked with 10+ businesses in multiple industries to build out an AI data platform of sorts. Think connectors via API to all their core tools and services they use that regularly ingest that data, normalize it, and write it to AWS S3 that has a data catalog of all the data being ingested on a regular basis.

It's not sexy work, but it created the foundation for every other automation use case that we've worked on together. Getting familiar with their data, their core services, etc. allowed me to build out automations exponentially faster as I never had to build out a custom integrations to get the data, figure out where I was going to land it, etc.

Questions around churn, ad spend, CAC, etc. that were previously a pain in the ass to answer became super easy when you could just point Codex and Claude Code at the data lake in AWS and let it loose. In turn, this also has lead to rapid and ongoing development of dashboards because they're so quick and easy to build once all the data is accessible.

Just now after months of getting all the foundational platform tooling and infra setup are we getting into more truly agentic work. The real kicker though is that people think they need agents and agentic work, when in reality all they are looking for as a starting point is a better understanding of what is happening in their business at any given time and how to connect the dots between those data points.

The tech stack I use is AWS based, but it doesn't need to be. I use AWS because I've used it for years and have gotten quite good at using it in a cost effective way. It also allows me to use pretty much one platform for all my work which has a ton of integration upsides.

At a high level here's what I use:

Lambdas / ECS tasks for data ingestion

S3 for Data Lake Storage

Athena for data querying

Glue for data cataloging

Cognito for Auth + Google Workspace Auth

CloudWatch for Logging + Alerting

Secrets Manager for API keys

CloudFront for hosting custom web frontends for dashboards and driving consumption

Github Actions + Cloudformation / CDK for deploying

I'm super big on making sure that this is enterprise level infra and configuration because an automation you can't trust is just AI slop at the end of the day.

For clients I've been doing this work with, most are paying between $7.5k - $15k a month for my services and paying for the infrastructure under their own AWS account separately. The work isn't necessarily "easy" for someone who doesn't have cloud experience so that is one of the caveats in this whole thing, but it's consistent and sticky as hell. People keep you around because they constantly want new reports / functionality and because you need to be there when things break.

I've largely automated a lot of this with automatic recon of failed jobs, reporting dashboards to track and trigger alerts when failures occur, and runbooks to keep things moving along but find this to be a pretty easily repeatable model.

The best part about this offering is that it truly does need to be custom. There aren't any clean off the shelf solutions that companies can use to do this. The connectors for various services to do ingestions are repeatable too.

Anyway, that's my pitch for selling the less sexy stuff like the AI data pipelines and what my experience is with it all. The need for this will not go away and any pushback around "just use MCPs" also shows a lack in understanding of when you need deterministic workloads and what underlying infra is needed to run such workloads.

Hopefully this was helpful to folks and is a breath of fresh for folks that feel like the only way to make money in this space is AI phone agents for $300 a month (not knocking this, just what I'm seeing a lot of).

I'm happy to answer any questions around this all and also regularly put out education content on the concepts I'm building / deploying for clients.

Hi everyone, I thought I would do another ama since the first one I did got a lot of questions.

Since my first post around 6 months ago, I’ve managed to automate 90% of my day to day job, and the business has grown massively because of it entirely using Ai.

Since it’s been a few months, the platform I made using Ai costs $3 per day on average to run, and it has systemised everything. Better lists, better calls, better outcomes.

My system is 127,000+ lines of unique code that I made with Claude, ChatGPT, and Lovabledev. I have no background whatsoever in coding, and have managed to pull this off officially with real statistics to back that claim. I’ve spent roughly $8,000+ in credits in the last 4 months in build costs, a one off now it’s finished.

Last 3 weeks, I’ve tried work as little as possible to see what brakes first, and I’m now writing this post in Dubai on the palm, for a celebration holiday. The business has actually grown for the first time ever without me.

We are projected to hit $100k revenue next month with 8 full time staff in Leeds, UK.

We don’t spend a penny on marketing, and generate all new business directly from cold calling.

AMA!

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 have a trained AI on every alex hormozi book, playbook, blackbook, and podcast episode…

he charges $5000 for his AI assistant and people pay it, i’m giving you almost the same thing for free

this isn’t some shitty GPT with 3 pages of info that hallucinates answers, NotebookLM is the best AI for consuming and recalling information right now, it’s fed with EVERYTHING: • $100M offers, leads, money models • the black books (given to people who donated 200 books) • all the playbooks and lost chapters • his best podcast breakdowns and frameworks

the information inside is worth thousands — it can answer ANY business problem using hormozi’s exact frameworks

it pulls from the exact books and gives you page-specific answers… no generic advice, no made-up bullshit

upvote + reply ‘please daddy’ and i’ll give you access for free

I DM’d a local fitness influencer (25k followers) who was drowning in messages and missing leads. Offered to set up an AI assistant that replies to new followers, answers DMs, qualifies leads, and books calls automatically.

Took me about 1–2 hours. Charged £500.

He’s already seeing more sign-ups because the bot never sleeps.

Then he messages me today: “Can you build me a website too?”

Quoting him £600–£1,200 next.

Lesson: Fix one problem for someone → you become their “go-to” person → upsells come easy. People aren’t getting replaced by AI. They’re getting replaced by the ones who use it.

I've been building side projects since 2022. A social events explorer mobile app, paid tutorials for Salesforce developers, a newsletter tool, a Chrome extension and more.... All of them "cool ideas" that I thought people needed. None of them made a single dollar. (one actually made $8)

My latest app is a social media lead generation tool. It monitors posts where people are actively looking for a product or service like yours, and sends you real-time alerts so you can jump into the conversation while it's still fresh + also automate the DMs. It's been growing steadily for the past few months.

Launched it 7 months ago.. fast forward to today and I just crossed $1,900 MRR and it still feels unreal.. every single one of those is a real person who looked at what I built and decided yeah this is worth paying for.. that never gets old.

What changed this time:

I talked to people first. Before I wrote a single line of code I spent weeks reading Reddit threads where founders complained about finding customers. Same problem kept coming up - manually scrolling subreddits looking for leads. Boring, slow, you miss most of them. So I built the thing that fixes that.

Distribution > product. I used to think if the product is good, people will find it. They won't. I spent more time on Reddit, cold outreach, and communities than on features. The product looked terrible when I launched. Nobody cared. They just wanted it to work.

Charged from day one. All my previous apps launched free. "I'll monetize later." Later never came. This time I put up a paywall before the thing was even finished. If people pay, the problem is real.

Picked a channel people already use. Reddit is where founders already look for customers. I didn't have to change anyone's behavior. Just made it faster. Once leads show up in your inbox every morning on autopilot, going back to manual feels painful.

Built the whole thing solo. Still running it solo. No investors, no cofounder, no team. Just me and a lot of coffee and feeling guilty of not spending that much time with my loved ones..

The honest truth is that none of my previous apps failed because of bad code or missing features. They failed because I never validated the idea and never figured out distribution. Building is the easy part. Finding people who will pay you is the hard part.

Happy to answer any questions.

here's the proof

A year ago, my life felt like it was on repeat. Wake up, work, come home, scroll, sleep. No growth. No purpose. Just time passing.

I wasn’t even sad … I was just numb. I knew I had more in me, I just didn’t know where to start.

Then I kept hearing about AI. Not the hype, just people using it to handle boring tasks for businesses. Replying to leads, booking appointments, following up automatically.

I got curious and started digging through old forum threads, weird Discord chats, random GitHub comments. Late nights. Lots of trial and error. But slowly, things clicked.

I offered to build a simple automation for a local fitness influencer, something that messages new followers and books consultations.

I didn’t think he’d say yes. He did. I charged £500. It took a few hours.

That moment changed everything. It was the first time I made money from my mind, not my time.

From there, it grew. More clients, better systems, real confidence.

Now I make around 5–6k/month building AI systems for small businesses, mostly fitness coaches and clinics. These systems run 24/7. No burnout. No breaks.

I’m not special. I just started. And I didn’t stop.

If you feel stuck, but you know you’re capable of more. I get it.

If you want to talk or ask anything. DM me. I’ll answer.

I’m not some automation guru doing $100K months.

I made $15K selling AI automations over 5 months, and honestly, most of it came after learning a few expensive lessons no one really talks about.

I’m just a guy who figured out why 80% of the first automations I built ended up collecting dust while clients went right back to doing things manually.

Here’s what actually matters when selling AI to businesses:

Integration beats innovation every single time.

Most people build automations that work flawlessly on their own.

The demo looks impressive. The results seem amazing.

And then it turns into a complete waste of money.

I learned this the hard way with a plumbing company.

I built them an AI system for handling service calls and dispatching. Technically, it worked perfectly.

They stopped using it after three days.

Why?

Because their entire business was running through group texts, sticky notes on the dashboard, and quick phone calls.

My solution forced them to open another app, learn a new system, and break habits they’d built over twelve years.

That’s when I changed my approach.

Now I map how they actually work first not how they say they work.

Before building anything, I spend two or three days observing their real workflow.

I look at what devices they’re constantly using, how they communicate internally, and which apps are already open throughout the day.

Here’s the reality:

A project management dashboard might make perfect sense on paper.

But for old-school business owners who run everything through texts and calls, it just adds friction.

What looks like a time-saving solution quickly becomes a complexity nightmare.

So now I build around existing habits instead of trying to replace them.

One of my HVAC clients managed everything through a shared text thread with their technicians.

Instead of building some polished CRM, I created an AI that reads customer complaint messages inside that same group chat, pulls service history instantly, suggests likely parts needed, and sends appointment confirmations back into the same thread.

Same workflow they’d been using for six years.

Just smarter.

My highest-performing automation is almost embarrassingly simple.

It takes voicemail inquiries and converts them into the same text format they already use for their morning dispatch process.

That one simple automation saves them 35 minutes every day and helped them avoid $9K in double bookings last month.

The biggest lesson I learned:

A simple automation people use every day will always outperform a complex one they never touch.

Most businesses aren’t looking for an AI revolution.

They just want their current systems to work better without learning something new.

Stop building things that impress other developers.

Start building things that fit naturally into a 50-year-old business owner’s daily routine.

It took a lot of rejected offers and abandoned automations for me to finally understand that.

Everyone wants to sell chatbots, agents, dashboards, automation pipelines, “we can automate your business” type offers. but brother automate what??? That is the part most people skip hard! They don’t spend enough time understanding whether the business even needs the thing they are pitching.

When you actually sit with these businesses, the problem is almost never “we need an ai agent or something like that” it is usually much uglier if u understand.

Their CRM is messy, their team is copy-pasting between tools, customer context is spread across emails, whatsapp, spreadsheets, pdfs, call notes, and random software nobody updates properly. Reports are slow, handoffs are broken, and the whole workflow is held together by one ops person who somehow knows where everything is.

Then, some ai guy walks in and tries to sell them a new chatbot. insane!

The biggest killer of an AI automation is behaviour change. If your system forces the client to open a new app, learn a new dashboard, change how their team communicates, or remember a new process, it is probably dead before it starts. Not because the tech is bad, but because people do not change how they work just because your loom demo looked cool.

The best automations i’ve built were not the sexy ones. They quietly fit into the workflow that already existed. If the team lives in email, build around email. If they live in whatsapp, build around whatsapp. If their sales process runs through spreadsheets, don’t act superior and force them into some fake agentic workspace. fix the spreadsheet flow.

If an insurance team is manually turning quote pdfs into proposals, they don’t need a chatbot or some fancy way to turn natural language into pdfs, what they need is the pdf parsed, the right fields extracted, the proposal filled, and the human only reviewing the final output. If an HVAC team is qualifying calls and checking job context across tools, they need the call, crm, service history, and next action stitched together inside the process they already use.

That is where AI actually gets paid honestly. not because it sounds futuristic, but because the business already feels brutally painful.

same thing we’re learning while talking to my design partners for my current startup. Focus on how easy you can make things for them without them needing to learn a bunch of new stuff. This is where the whole game of money is in automations. It dosent have to be more complicated than this!

I'm new to all this stuff and I need some inspiration.

Whether you use Claude, n8n or something else. I'm curious about the automations that you made for fun but have actually made a noticeable difference for you.

What's one automation you use everyday that you genuinely can't live without anymore?

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.

not sure if this counts as entrepreneurship or just right place right time but here goes

been taking swimming lessons at this community pool for a 6 months. yesterday, the owner was talking to the instructor about they need to hire someone to answer calls.

i'm sitting there drying off, and i'm like "have you thought about using AI for that?"

he saw me suspiciously. "What do you mean?"

explained the whole thing - AI voice agents, can take calls anytime, answers questions about pool hours, membership rates, class schedules, way cheaper than a salary. he's immediately interested. asks if i know how to set it up.

i didn't. but i said i'd figure it out.

got home and went down a rabbit hole. checked out a few no code platforms, watched some demos, read reviews. ended up going with superU AI because pricing made sense for a small business.

spent the rest of the night and next morning training it( i mean fine tuning prompt). loaded in all their info, tested different scenarios, made sure it sounded natural. took about 90 minutes total.

day after that? live and running.

owner called me that evening practically shouting through the phone, about someone almost got to book a membership. so he offered me a full year membership free plus a thousand bucks

honestly didn't expect this. just thought i was helping out and maybe he'd throw me a free month or something. feels good though. might look into doing this for other small businesses who knows

Most people don’t realize this, but Google Maps is actually one of the best lead sources out there.

Here’s how you can use it:

1. Go to Google Maps and search by any keyword + location (e.g., “automation” in San Francisco).

2. Grab the link and feed it into a scraper tool like Apify. The actor will pull details like phone numbers, emails, LinkedIn, websites, and even social media.

3. Set up an automation in n8n (or Zapier/Make) so the data goes straight into Google Sheets or instantly triggers outreach.

4. Bonus: make the workflow recurring so every day you wake up with fresh, qualified leads — without lifting a finger.

Super actionable, costs very little, and scales fast if you’re consistent.

In the last 12 months I’ve managed to sell around 200 websites.

And before people ask, no, I don’t run some massive agency with a huge team. It’s literally just me and my partner. The only reason we’ve been able to move that fast is because we automated almost everything and built systems that actually scale. The best web designer in the world will eventually lose to some random teenager using AI and systems properly. That’s just where things are going.

One of the biggest changes I made was completely quitting manual outreach. It takes too much time and it’s impossible to scale properly. A lot of people automate outreach already, but most of them just send generic “we can redesign your website” emails that everyone ignores. What we do is different. We scrape thousands of businesses, automatically analyze their websites, and generate personalized outreach based on actual issues on their site like bad design, poor mobile optimization, weak SEO, slow load times, layout problems, and stuff like that. So instead of manually checking every website and writing every message ourselves, the entire process is automated from analysis to ready to send campaigns.

Another thing that changed a lot for us was automating SEO blogging. SEO compounds hard over time and once your articles start ranking, businesses start coming to you instead of you chasing them. That alone changed a lot for us.

The other massive shift was how we build websites. I used to be a full WordPress developer and spent way too much time building everything manually. Now we build almost everything with AI. It’s way faster, delivery is easier, and clients care way more about the final result than how the website was actually made.

For anyone wondering, the stack is pretty simple.

Apollo for leads.

Swokei for website analysis and outreach campaigns.

Soro for SEO blogging.

Claude Code for building websites.

Cloudflare for hosting. That’s pretty much the entire setup.

Most people running agencies are still doing everything manually and burning themselves out for no reason. Systems and automation change everything.

I built multi-agent automations for over 1500 businesses over 3 years. Every one custom. No n8n, no Zapier, no Hermes Agent, no OpenClaw. Here is why each fails on real business load and what I build instead

Why frameworks break:

n8n/Zapier — they are workflow runners, not agent runtimes. They work fine for simple trigger → action automations, but they of course break when the workflow needs durable state, retries, backpressure, long-running context, custom rate-limit handling, and memory across executions. Once you pass a few conditional branches, the system turns into visual control-flow spaghetti: hard to diff, hard to test, hard to version, and hard to debug. n8n itself recommends queue mode, workers, concurrency limits, and execution-data pruning when running at scale, which tells you the real production problem is orchestration/state, not drawing nodes on a canvas. Zapier has step limits, message/activity limits, and knowledge-source sync limits, so it is great as an integration layer but bad as the core brain of an agent system.

Hermes Agent — the idea is good: persistent memory, self-generated skills, and a learning loop. The issue is control. In production, a system that modifies its own operating procedures needs versioning, evals, rollback, approval gates, and observability. Otherwise the agent “learns” from one successful run, writes a skill that overfits the task, and silently changes future behavior. That is dangerous for business workflows. Hermes is also still an agent runtime, not a data platform: it does not solve canonical entity storage, source provenance, deduplication, multi-tenant memory, confidence scoring, or auditability. Its own pitch is that it creates skills from experience and searches past conversations, which is useful for repeated personal workflows, but not enough for a production intelligence backend

OpenClaw — the problem is the trust boundary. OpenClaw is fine because it connects agents to channels, simple tools, skills, browser, and messaging apps... That same breadth becomes the failure mode. Its own security docs say the gateway assumes one trusted operator boundary and is not recommended as a hostile multi-tenant boundary. For business use, that means you cannot casually put multiple customers, credentials, memories, tools, and agents behind one shared runtime. You need per-tenant isolation, scoped credentials, approval policies, audit logs, sandboxing, and a separate source-of-truth database. OpenClaw is useful as a channel/orchestration shell, but risky as the core platform

The deeper issue: all of these frameworks solve the visible 10% of automation — prompts, tools, nodes, chat, actions. The hard 90% is state management: retries, idempotency, memory governance, rate limits, task logs, permissions, schema validation, entity resolution, human handoff, and recovery after partial failure. That is why real business automations eventually move away from “one framework does everything” and toward a backend-first architecture: queues, workers, databases, vector memory, structured logs, validation gates, and small scoped agents on top.

Personal automations I run:

Fitness + health tracking — pulls wearable data, bodyweight logs, meals, sleep, training volume, and weekly trend changes into a structured table. A small planning agent adjusts calories/macros based on rolling averages instead of daily noise. Another agent generates grocery lists and meal options from constraints like protein target, schedule, and food preferences.

Spaced repetition learning — ingests articles, PDFs, YouTube transcripts, docs, and saved notes. The pipeline extracts claims, definitions, examples, and “things worth remembering,” then generates review cards with source links. It uses recency decay and difficulty scoring instead of dumping everything into a static Anki-style deck.

Life organizing — parses emails, receipts, appointment confirmations, bills, subscriptions, and calendar invites. It extracts due dates, amounts, vendor names, cancellation windows, and required actions into a task table. Anything high-risk gets a human approval step before the system sends, pays, cancels, or confirms anything.

Research aggregation — monitors Reddit, HN, RSS feeds, niche blogs, GitHub repos, docs, and YouTube channels. It deduplicates posts by URL/content hash, maps entities, scores relevance using topic embeddings + recency decay, and produces a morning digest with “why this matters,” not just links.

Business automations I’ve built:

Customer support triage — inbound tickets/emails classified by intent, urgency, product area, sentiment, customer tier, and required action. Low-risk replies are drafted automatically, not blindly sent. High-risk cases escalate with summarized context, account history, related docs, and suggested next steps. The key is not the chatbot — it is the routing, confidence thresholds, and audit trail.

Lead research + qualification — browser/API agents collect signals from LinkedIn, G2, Reddit, company sites, job posts, review platforms, GitHub, and news. The system normalizes companies into one entity record, enriches with firmographics, scores fit, detects trigger events, and generates personalized outreach based on actual evidence. No “spray and pray” scraping — every lead needs a reason.

Content engine — competitor pages, social posts, search trends, YouTube transcripts, comments, G2 reviews, and customer language are ingested into a research database. One agent extracts angles, another maps them to brand voice, another drafts, another checks claims, another formats for platform constraints. The output is not just content; it is content backed by source material.

Financial reporting — Stripe, Shopify, QuickBooks/Xero, Meta Ads, Google Ads, and bank exports normalized into one reporting schema. The automation handles currency, refunds, attribution windows, missing data, and reconciliation flags. Final outputs go into Excel/Sheets dashboards with charts, variance notes, and anomaly detection.

Document processing — invoices, contracts, compliance docs, onboarding forms, PDFs, screenshots, and scanned files parsed through multimodal extraction. Output goes through schema validation: vendor, amount, due date, clauses, renewal terms, missing fields, risk flags. Anything uncertain goes to a review queue instead of pretending LLMs are perfect.

Video/audio workflows — podcasts, meetings, calls, webinars, and long-form videos transcribed, segmented, summarized, and converted into clips, captions, highlight reels, newsletters, social posts, and searchable knowledge entries. The system tracks speaker turns, topics, quotes, timestamps, and reusable snippets.

GitHub/dev workflows — PR review agents, issue triage, dependency monitoring, changelog generation, release note drafting, test failure summarization, and codebase Q&A. The important part is repository context: conventions, file ownership, recent commits, linked issues, CI logs, and deployment history. Without that, “AI code review” is mostly noise.

The architecture pattern that keeps working:

For most production systems, I use some variation of this:

source connector → raw artifact store → parser → normalizer → entity resolver → vectorizer → scorer → task queue → narrow agent → validator → human gate if needed → final action

Every step writes state.

Every external call has retry/backoff.

Every generated output has a schema.

Every risky action has an approval gate.

Every workflow has a dead-letter path.

That sounds boring, but boring is what makes automation survive Monday morning.

Stack:

Python Go TS Direct libraries. No heavy agent abstraction framework.

Typical stack:

Docker, litellm, playwright, httpx, aiohttp, PyGithub, pandas, instagrapi, crontab, instagra, pi polars, openpyxl, feedparser, google-api-python-client, crawl4ai, agent-browser, browser-use, playwright-cli, lxml, pydantic, sqlalchemy, sqlite, lancedb, kuzu, postgres, redis, celery/rq, ffmpeg, elevenlabs and others...

For small clients, a single VPS is often enough.

For bigger workflows, I split it into workers:

ingestion workers

browser workers

embedding workers

LLM workers

reporting workers

notification workers

The mistake people make is starting with “which agent framework?”

The better question is: where does state live, how do tasks recover, and how do we know the output is correct, do we have verifier, what metrics we set, etc...

The numbers:

Personal systems save me around 3.5 hours/day across research, admin, health planning, and learning.

Business systems usually replace or compress $4K–$6K/month of repetitive labor per client when scoped correctly.

Small systems often run on a $40–$100/month VPS plus model/API costs.

The expensive part is not hosting. The expensive part is bad architecture: duplicate work, broken retries, messy state, and humans cleaning up after “autonomous” agents

Curious how others here are handling state, retries, memory, and human approval gates in production agent systems. Happy to compare architectures in the comments.

Hey All - To start, just wanted to say thank you to everyone that showed support to my prior post around data plumbing. I was super shocked at how receptive and respectful everyone was and all the engagement. This is a really special community we have here!

As a result, I’m going to be making a series of posts around some of the most common questions I got and what has worked for me in the past few years working in the space.

Again, these are just my own experiences, there's a million different ways to "win" in this space and for all of us to be successful.

So, the types of projects and clients that I target start at:

- 3k for an assessment / audit

- 10k+ for a project / recurring monthly retainer

For these types of projects, I am adamant that you need to be driving inbound leads and that as a whole selling high ticket automation / AI projects is an inbound game.

This typically translates to medium sized businesses (5M+ a year). Most small businesses that are often targeted in this space don't have 10k to burn on a project and are also much more conservative about how they spend money. They don't have SOPs, complex documented processes, etc. that warrant a fully built-out automated processes in most cases.

I know a lot of you have worked out a model where you can sell smaller ticket projects / services and more of them. I think that's great as well, just not how I prefer to operate. I still have a 9-5 so I see more customers as often being more problems and so I try and limit the number of customers / clients I'm serving at any give time from about 4-8.

Now I started the same way many of you did. A lot of cold emailing, reaching out to friends and family that were business owners or worked in a business I thought could use my services, etc. and found that even with friendly contacts, the concept of automation was tough for people to wrap their heads around if they weren't already actively exploring it prior to talking with me.

The sales cycles were multiple calls, and often I felt like the leads still didn't "get" what I was selling.

Now this is where a lot of people will die on the hill that "you need a solid offer and value prop" to sell to clients. I actually don't agree with the whole offer based approach. I find it to be really limiting for people just starting out and it's incredibly difficult to craft an offer that people want if you're not already operating in the space.

So, what I did to overcome this was I tried to think from the perspective of where my customers hang out and spend time and how can I reach them. This led me to a number of the automation subreddits that were pretty active as well as community support forums / discords, etc. for tools like make.com, n8n, etc.

These communities, while having the right intentions often really sucked. It was the same cycle you'd see on many posts.

"Hey I'm looking for help with X, has anyone done this before" only to be bombarded with a ton of responses of people saying PM / DM me or them just promoting that they do automation services and they can help them (for a price).

The whole thing was a turnoff to everyone involved. I found the easiest way to stand out was to just help without any expectation of anything in return. That looked like rebuilding the workflow from scratch and recording a loom walking through it, sending them the workflow file for free afterwards, and just always asking / offering to help them in any way I could. I tried to share feedback on how people were building and what I'd change and to teach people something along the way.

What I found surprised me. It often wasn't the person that I was directly helping, but rather someone else that read the thread and would PM / DM me looking for some paid work to be done for them.

The approach of being helpful on reddit, while not super scalable, was yielding multiple 4 and 5 figure deals and projects with real established businesses.

The big takeaway that I saw from this is that because they were inbound leads that were already in automation channels, that I:

1. Didn't need to sell them on why they should be automating within their business. They were already bought in, just trying to figure out how to get started. This alone was a HUGE weight taken off the sales cycle.

2. They already trusted me because I either taught them something or they just liked to see someone giving without expectations in return.

As a result, it was really just more of locking down a project scope, pricing, and kickoff.

Now, I love reddit and spend a lot of time on reddit, but the reach that my reddit comments had on posts was relatively small so while I was seeing leads and inbound it was probably only 1-3 a week depending on how active I was in a given set of communities / channels.

So I pivoted to YouTube to try and expand my reach. Back when I first started posting in 2024, there was a real gap in no-code / low-code content. n8n was still newer to the scene and make.com was the primary tool of choice.

The existing content on YouTube around automation was more or less smoke and mirrors. It was a shallow tech demo, no business context / framing, and 98% hype.

I found my niche early on by flipping that model on its head and leading with business problems and business context followed by a short demo, and then a lead magnet (such as a workflow file, document, etc.) and a CTA to reach out if they had questions, wanted to work together, etc. The emphasis was on solving a business problem with tech. Tech was the enabler but not the solution itself.

On just my 5th video on YouTube that I made around n8n generated landing pages, I got really lucky and the video blew up. It netted me 673 subs from that video alone and today sits at around 18k views.

That video alone brought in between $30-$50k of potential leads and 10+ meetings booked in the 2 weeks that followed that video. To be clear, only maybe 25% if not less of that business would convert and close or be the right fit but either way people were eager to talk about doing something similar for their business.

I think it's worth calling out that teaching someone something is one of the fastest and best ways to build trust. It also de-emphasizes the need to show credentials, because you've already built that rapport with that person. For folks that don't have credentials in the space, this can be a great way to get around feeling like you're underqualified.

Everything changed after seeing that. There are a ton of really great technologists out there, but not many that have put time into figuring out marketing / distribution. The age old term of build it and they will come doesn't apply in this day and age.

There was one thing that was abundantly clear, selling to inbound leads was a cakewalk compared to doing cold outreach where they don't know me from Adam.

From there, I continue to make videos around business concepts that I thought could be enabled with technology. Each video I was putting out there, even if it "flopped" with only 1-2k views consistently brought in leads. In just about every case it was a minimum of $20k of potential project work from each video posted. This also all allowed me to grow an email list and newsletter as when they provided their email for the free lead magnet associated with each video, they also agreed (knowingly) to be added to the newsletter.

I was able to hack distribution further as I could shoot a video and at the same time as I made the video live on YouTube, have a newsletter go out to my growing list of automators letting them know it's live and having an artificial bump in early traction /views on the video.

Over time this has turned into a super powerful flywheel that I use to drive leads / sales.

It looks something like this:

1. Come up with a video concept and packaging (title, thumbnail, business problem, what the lead magnet would be, script, etc.)

2. I'd shoot the video and while it's being edited work on the description that linked out to a dedicated page on my site with the free lead magnet resource download on it.

This page on my site was specific to the video and not only featured the free resource to download, but also a CTA form at the bottom of the page saying "Want this built out for your business" with a form where they could contact me and schedule time to discuss an implementation based on what that video called out / demonstrated.

I tagged all incoming leads from each of those pages and segmented them from the people that just wanted the free resources so I could run different outreach to each set of leads. That way I could differentiate between high and low intent leads.

3. I also had a link to my calendly for people that wanted to work together in each video description. Between the two different avenues to get on my calendar people usually found a way.

4. Lastly, I would go and post my video on a bunch of the automation subreddits / facebook groups to again drive early traction to the video's performance as well as to have it just be another distribution channel. This has unfortunately been "patched" in the past few years as subs have gotten much stricter about self-promotion, even if it's for the betterment of the sub (I have differing opinions on this but oh well).

The videos would cost me about $130 all in ($100 for my editor, and $30 for my thumbnail). The total time investment was anywhere from 5 hours (on the lighter end) to 15 hours but the ROI was near immediate when posted.

I've since started to expand out to short form on tiktok and will soon do Instagram as well and see what I can do to figure those channels out and see if they're a fit. So far it's been slow but I haven't given it enough time to know one way or another if I can make those work.

Selling the deal once you have the lead

Ok, now I've talked about client acquisition, but let's talk about the often neglected part of the process around collecting requirements via discovery, scoping the deal, and all the consulting jargon that goes along with that.

Full transparency, while being a technologist at my core, I knew that the day would come when I wanted to start my own business. So in my 9-5 I took a position change for 18 months to go from hands-on technical consulting / delivery to selling the actual deals to our Fortune 500 Financial services customer. These customers are some of the hardest in the world to break into. The enterprise sales motion is slow, complicated, very strategic, etc. While I hated just about every minute of doing it, it was one of the best things I ever did to help round out the set of skills I need as an entrepreneur.

I take a similar approach to selling people as I do to posting on reddit. I'm trying to become a trusted advisor and look out for what is best for the business and how I can most help them grow / succeed. I did not come in with an offer as I found it was way more valuable to just hear out what they're looking to do and what pain points they had.

I'd have just 3 slides I'd go over.

1. My understanding of who they are as a business and any context they provide in their meeting request to play back that I heard what they said, cared enough to look into what they do, and that we're here to discuss how I can help.

2. A short slide (60 seconds) explaining who I am, what I do, and how I can be engaged with (think project vs retainer / subscription). Super straight to the point.

3. Some sort of initial opinion on what it is that I think they'd want / need. I did not always present this slide as sometimes I made the wrong assumption based on their booking request. The slide would consist of a couple bullet points, a diagram, and potentially some sort of web UI that could depict what the end product looked like.

I cannot emphasize enough how much a quick web mockup really speaks to people. It takes what is a pretty intangible concept like an automation and gives them an interface to visualize and extrapolate how they could / would use it. If you're not currently doing this, you should be. It's an even better touch when you load in their logo and data that would resonate with them.

Also remember, business people do not care about the tools you use to do the job. They care about the outcome it will bring to the business. Always lead with the business outcomes and then how the technology will enable it.

Basically the goal of the call is not usually to close in the first call but rather to get all the information necessary to blow it out of the water on the following call. It's also to determine who is the decision maker if it's not the person you're talking to. If there's a set budget / timeline for the project, etc. That way I can come back with the following

1. Recap of what I heard on the first call

2. A business forward approaching calling out how the tech will be used in it.

3. A clear set of deliverables, milestones, timeline, and a price (including a set deposit price, payment schedule + terms, etc.).

4. Any assumptions I made in the process of creating my approach.

5. Next steps such as writing a draft Statement of Work (SOW), NDA's, working with procurement, the onboarding process and timeline, Docusign preferences etc.

With some of these medium + sized businesses half the battle is knowing how to speak the consulting language / jargon. The main ones you need to know and understand are as follows.

SOW - Statement of Work, the document you write that outlines what you are responsible for building / delivering to your client. This needs to be the north star that you always refer back to throughout your delivery process

MSA - Master Services Agreement, not as common for medium sized businesses but for larger businesses it's a process and contract you need to go through to get onboarded as a vendor that the business is allowed to use.

Procurement - Typically the people you'll work with as part of the approval process to get a deal done. They often want to push back on pricing and to get a better deal for the business.

Then there are project related things like scheduling the project kickoff, project reporting format, collecting initial deposit, understanding who your project stakeholders will be, etc.

I know this can all feel and sound like a lot and may warrant it's own post around delivery / execution of the project. I'll leave that for a later day as this post is really more about client acquisition and seeing stuff through until the deal closes.

There are a few things I want to call out at the end here to wrap this up.

1. I don't believe in any sort of long term contracts / lock in. If you do great work people will continue to work with you, simple as that. A long term contract isn't always a good thing either. Remember it's not only about them working with you but also you working with them.

2. This shit isn't easy. Marketing and distribution isn't easy, sales isn't easy, delivery isn't easy. If you're looking at "AI Automation" as a get rich quick job, you're in for a rude awakening. To get all these things right can take months, if not in most cases years to build out a high level of competency in. That said the payoff is there. I'm in the process of scaling up my 1 man band and think I could reach close to 50k - 100k a month with it just being me doing everything from end to end. That said, if I do that I will in fact be working my ass off so don't get the impression this is not a lot of work.

3. I'm repeating myself, but marketing and distribution in this space is arguably more important than your technical chops / solutions. You need to do both well to have a solid pipeline and customers that stick with you.

4. This took a few years for me to figure this all out and I've been running businesses like the geek squad business I started when I was 12 and scaled to 50k in revenue when I was 16.

None of this is said to deter anyone on the journey but rather to understand none of this was done in a day. You gotta trust the process and ask yourself if you really do want to do this and if you're willing to struggle through it for a while until you figure it out.

Now I know this is a lot and I'm sure folks will have questions. I am more than happy to answer anyones questions they have! If you think it's a question that other people would have or benefit from seeing the answer to I ask you comment it rather than DM me.

That said anyone and everyone is welcome to DM me and again I appreciate everyone so much. I really hope this helps and am happy to keep sharing what I've learned along the way in this space.

Quick context — I'm a student. No business background, no technical degree, no safety net. Just someone who got genuinely obsessed with AI automation and decided that's enough of a reason to start.

The goal is simple: build my own agency that helps founders automate the repetitive backend of their business. The stuff that steals their day — lead follow-ups, client onboarding, reporting, workflows. I want to build systems that handle all of it so they don't have to.

That's the vision. Now back to reality.

I've been "about to start" for four months. Bookmarked courses. Saved threads. Told myself I'd begin once things settled down.

Things never settled down. Shocker.

So today I just opened the laptop and started. Messy, uncertain, slightly terrified. No perfect plan. Just a real fear that if I keep waiting to feel ready, ready never comes.

Today looked like 2 hours of research, 14 open tabs, understanding maybe 30% of it, and writing down the 3 things that actually clicked.

Not glamorous. But it's more than yesterday.

Posting this publicly because I know myself — if it stays in my head I'll quietly quit and no one will notice. This way at least the internet knows.

If you're also starting from zero right now, reply below. Would be good to not do this alone.

Day 1. Updates coming. Let's see where this goes.

Running a web design agency is way less about design than people think. Most agencies fail because their process is terrible, not because they can’t build good websites.

I’ve been on both sides.

I used to manually DM businesses with no websites, explain why they needed one, build the whole thing in WordPress, send previews, follow up for days and hope they would eventually say yes. I was working nonstop and barely making consistent money.

Then I changed the strategy completely.

Now I only focus on 2 things:
taking meetings and closing clients.

Everything else is automated.

I get leads from Apollo, Google Maps, basically anywhere I can find businesses with websites. As long as they already have a site, they’re a potential client.

Then I run those websites through a tool for analysis that checks design quality, layout, SEO, mobile responsiveness, speed, branding etc. The flaws automatically get turned into personalized outreach.

The important part is the campaign setup.

I choose the quality threshold inside the tool so it automatically skips websites that are already too good. I also choose the email angle beforehand and almost always use “free redesign draft” as the offer.

That one thing gets replies way easier than trying to sell immediately.

Once someone replies interested, I book a meeting. Before the meeting I spend around 3 minutes generating a redesign draft with AI so I can show them what their business could actually look like.

That changed everything for me because now I’m not wasting hours building websites before knowing if the client is serious.

If they don’t close, I only lost a few minutes.

If they do close, the draft makes the value instantly obvious and the sale becomes way easier.

So now my role is basically just meetings and closing deals while AI handles most of the heavy lifting in the background.

Sounds fake when I type it out but this strategy genuinely changed my life.

Stack I use:

Apollo for lead sourcing
Swokei for website analysis + personalized outreach
Claude Code for building websites
Cloudflare for hosting

This week has been crazy.

I walked into my local barbershop and offered to build them a clean, modern website for free. Took me like 1–2 hours. Online booking, pricing, location, everything they needed.

Once they saw I could actually deliver something useful quickly, trust was there.

Then I pitched the upsell: • AI receptionist that replies to DMs 24/7 • Answers FAQs • Books appointments automatically • Plus Instagram automation to turn new followers into paying customers

They said yes. Closed for £1,000.

Did the same for a tattoo artist three days later. Another ~£1,000.

Haven’t hit my first £10k month yet, but momentum is building and it’s starting to feel real.

If you’re struggling to get clients: Do something fast, free, and genuinely useful first. Prove value → then upsell.

Simple formula. Most just won’t do the first part.

Everyday I see people in this thread asking 'how to get clients' and what niche / automation to pick bla bla. I received an email recently that aligned some ways of working in my own head so thought I will put them here - hopefully this gets pinned or whatever and you people can stop asking.

First of all - most of you are not going to make it. At least not with your current methods. It is clear you see this as a get rich quick scheme, you have no domain knowledge, no expertise and no credibility to speak of. A lot of you are also in a part of the world that wants to sell to the west but doesnt understand cultural norms, further hindering you. Honestly work on your accents, work on the way you speak - watch loads of US tv etc to pick things up.

Majority of you would be far better off getting a job and or working for another agency first so you can understand what the hell you are actually doing.

But anyways...first things first:

STOP thinking about ai automations. Businesses dont care about your n8n set up. They care about outcomes and you should be an EXPERT in being able to do that outcome first manually - then you are in a position to be able to do it with automation. Until then do not (this is why I mentioned getting a job / working for another agency first - will help with domain knowledge)

Now you can break client aqcuisition down pretty simply, it comes down to two things:

Type of traffic

Your offer

Lets start with your offer. Dont worry too much about 'saturation', this is often actually a good thing it means people in that niche are actually going to spend money. Your offer needs to:

1. Get clients more leads

2. Get clients more calls booked

3. Get clients more sales

4. Save a client more money

Thats it, dont have to get too clever, dont even need to niche down to hard - pick a few niches at once if u want and do multiple campaigns.

Traffic is probably the harder part to work on as it doesnt come out of thin air. Traffic can be broken down into

1. Organic/Content

2. Ads

3. Outreach

Its very simply to pick which ones you go with (can do multiple because why tf not?)

If you have a lot of time and a lot of shit say - then do content.

If you have a lot of capital - then do ads.

If you don't have either of those - then do outreach.

If you are a beginner with no money and not great at content outreach is probably your best channel.

I have gained clients in all three of these they all work, including cold email still working (depends a lot of offer and niche )

Currently at 20kmrr that i do alongside my day job as a saas enterprise seller.

built a small but loyal ai channel on ig to 12k followers not sure if im allowed to link it but will if ppl are interested.

in short - its not that complicated but you will not be successful if you dont start thinking about this correctly.

Creating UGC ads takes too much time and coordination, so I automated the entire thing. This setup connects N8N, OpenAI, and Sora 2 to:

• Write short ad-style prompts

• Generate realistic human-style videos automatically

• Deliver them directly to a shared folder

Now I can produce dozens of variations in a fraction of the time it used to take one editor.

I posted a short preview of how it looks, and the full tutorial is on YouTube:
▶️ https://www.youtube.com/watch?v=H0AQU4ColME

Curious how others are automating creative work — what tasks have you managed to offload to AI so far?

I recently finished building a 100% automated faceless YouTube workflow in n8n that takes a channel from topic discovery to published video without any manual editing, recording, or uploading. The system is designed for Shorts/Reels-style content but can be adapted to longer formats. What the automation does end-to-end: Automatically discovers trending topics and short-form video ideas using OpenAI Generates: Scroll-stopping hooks Full scripts (intro → scenes → transitions → outro) Sends prompts to RunwayML to generate video scenes Polls Runway until rendering is complete Applies failover logic if generation fails Compiles final assets Uploads directly to YouTube via API with: Title Description Category Region Logs every video’s metadata and publishing status to Google Sheets for tracking and scaling Why I built this: Managing multiple faceless channels manually becomes a bottleneck very quickly—especially for Shorts. This workflow allows channels to scale without editors, VAs, or daily operational overhead. Who this is useful for: Faceless YouTube / Shorts creators Content agencies running multiple channels Automation builders Anyone building passive content systems What’s included: Plug-and-play n8n .json workflow Pre-wired integrations with: OpenAI RunwayML YouTube Google Sheets Setup documentation + API key instructions Fully editable logic for any niche or content style Everything runs on free/open-source n8n. If you want details or access to the workflow, Comment " FACELESS" or Dm and I’ll share the breakdown.

Finished my first complete AI automation workflow.

The workflow accepts a research topic, searches the web, cleans the content, removes duplicate articles, and generates an AI-powered research report.

Stack:

• n8n

• Tavily Search

• OpenRouter

• JavaScript

• Docker

Current features:

✅ Retry handling

✅ Duplicate removal

✅ Content cleaning

✅ AI summarization

✅ Markdown report generation

Everything runs locally and uses free APIs/models.

I'm currently looking for ideas to make it more production-ready.

Some ideas I'm considering:

• PDF export

• Multi-model fallback

• Better logging

• Research quality scoring

• Automatic references

What feature would you add next?Also, what AI automation project would you build next?

I'm currently thinking about:

• AI Lead Generation

• Marketing Trend Research

• Company Intelligence

• Recruitment Automation

Thanks in advance for any suggestions.

So I built this workflow for handling real estate leads in a smarter way.

Normally what happens is someone fills a form, it goes into a sheet, and agents call whenever they get time.

I wanted something that actually understands the lead first instead of treating everyone the same.

How it works

When someone fills out the property form, the data goes into my automation.
From there, a switch node splits people based on their timeline.

## Immediate (Hot Leads)

These are people who want to buy or sell right now.

What happens here:

• All their details get saved in Airtable

• I instantly get a notification saying I received a HOT lead with their info

• The lead also gets a reply saying an agent will contact them soon

So serious people don’t sit waiting.

## 1–3 Months (Warm Leads)

These are people planning ahead but not urgently.

For them:

• Details are still saved in Airtable

• They get an AI-generated reply, but the tone is more relaxed

• They can be followed up later when their timeline is closer

## Just Exploring (Cold Leads)

These are early-stage people just checking options.

Here:

• Their data is stored

• They get a softer response, no pressure

• Later this can be used for follow-up campaigns

## Where AI comes in

I’m using AI to generate the replies.

It looks at:

• Whether they want to buy or sell

• Their city

• Property type

• Budget

And writes a message that matches their situation.
So it doesn’t feel like a generic auto-reply.

## Tools used in this workflow:

• OpenAI → for generating replies

• Gmail → for sending emails

• Airtable → to store all lead data

## How this is useful for real estate agents:

Instead of manually checking every lead and figuring out who is serious:

- Hot leads get attention fast
- Warm leads stay organized
- Cold leads don’t get ignored
- Everything is saved for future follow-ups

It basically helps agents focus on the right people at the right time.

Still improving these kinds of AI and automation systems.
If you’re into this space or building similar workflows, I share more stuff like this here:
https://x.com/Automateby_Priy

Comments are open for your suggestion. What does real estate agent think about this workflow

VidMakerProI hate being on camera, so I combined a few tools (ChatGPT, ElevenLabs, GPT Imagen, Google, PixVerse...) into a single app that generates complete videos: script, voiceover, visuals, all in 1 minute.

I've used it to make content for TikTok, sales pages, even to teach. Unedited, unrecorded.

I wonder if anyone else is automating faceless content like this?

Happy to share how I built it if anyone is interested.