r/AiAutomations

In this hobby project I recently launched, I am trying to understand the actual implications of AI automation by asking people to asess their actual job. Feedback deeply appreciated 🙏 Hope I’m not breaking any rules by posting. No company is involved in the project.

👋 Hey everyone,

Over the past few weeks I've been sharing workflows that use document extraction for things like currency conversion, invoice classification, duplicate detection, and Slack-based approvals. One question that keeps coming up – from myself and from people trying these workflows – is: how far can you push the extraction before it breaks?

Clean PDFs are easy. Every solution handles those. But what about a scanned invoice with coffee stains? A photo taken at an angle? A completely different layout than what the pipeline was trained on? A document that looks like someone used it as a coaster, scribbled notes all over it, and then left it in the rain?

I wanted to answer that properly, so I'm building a stress test workflow.

The idea:

Upload a document through a web form, extract the data, compare every single field against the known correct values, and get a results page with a per-field pass/fail breakdown and an overall accuracy percentage. Since the test always uses the same invoice data, the ground truth is fixed – you're purely measuring how well the extraction handles degraded quality and layout changes.

The test documents I'm preparing:

I'm going to run four versions of the same invoice through the workflow:

1. Original – clean PDF, the baseline. Should be 100%.
2. Layout Variant A – same data, completely different visual layout
3. Layout Variant B – another layout, different structure again
4. Version 7 ("The Survivor") – this one has coffee stains, pen annotations ("WRONG ADDRESS? check billing!"), scribbled-out sections, burn marks, and a circled-over amount due field. If anything can extract data from this, I'll be impressed.

I spent some time thinking about what makes a good stress test. Different layouts test whether the extraction actually reads the document or just memorises positions. The destroyed version tests OCR resilience when half the text is obstructed. Together they should give a pretty honest picture of where a solution actually stands.

What's coming next week:

I'm going to build out the full workflow, run all four documents through it, and share the results here – accuracy percentages across every version, including the destroyed one. I'll also share the workflow JSON, so anyone can import it and run their own benchmarks.

The workflow will be solution-agnostic too – you'll be able to swap out the extraction node for an HTTP Request node pointing at any other API, and the entire validation chain works identically. Good way to benchmark different tools side by side.

Curious to see where it breaks. Would love to hear if anyone else has been stress testing their extraction setups, or if you have ideas for even nastier test documents.

Best,
Felix

He was proud of his process. That's what made it hard to tell him it was killing his business.

I met this guy through a referral... runs a B2B SaaS consulting firm, six person team, genuinely smart operator. He had this whole GTM research routine he'd built over two years. Every week, his team would manually pull LinkedIn profiles, cross-reference company funding news, check hiring signals on job boards, dig through Crunchbase, and dump everything into a Google Sheet before deciding who to even reach out to. Sixteen hours a week. Just to figure out who was worth calling.

He called it "quality prospecting." I called it a very expensive spreadsheet habit.

The problem wasn't that the research was bad. It was actually solid. The problem was that by the time they finished researching, half those companies had already moved through their buying window. A Series B company that just hired a Head of Revenue is a perfect prospect... for about three weeks. After that, the team is hired, the tools are bought, and your outreach lands in a pile of ignored emails. They were doing great research on cold leads and didn't even know it.

So I stopped asking him what he wanted to automate and asked him one question instead. "What happens between when a lead looks perfect on paper and when your team actually closes them?" He paused for a long time. Then he said... "honestly, timing. We always seem to be one month late."

That one answer told me everything.

I built him a lead nurturing and GTM intelligence workflow that runs every morning at 6AM. It monitors funding announcements, new executive hires, job postings with specific keywords, and product launch signals across their entire target account list. When a company crosses three or more of those signals in a rolling fourteen day window, it automatically enriches the contact data, writes a one paragraph personalized context summary in plain English, scores the account by urgency, and drops it into their CRM with a follow-up task already assigned to the right rep. No spreadsheet. No manual digging. The team wakes up to a prioritized list of who to call that day and exactly why.

First month, they went from sixteen hours of weekly research to under two. Second month, they closed four accounts they would have missed entirely because the timing window was flagged before it closed. Forty thousand dollars in new revenue in sixty days. Not because I built something flashy. Because I built something that solved the actual problem... which was never research quality. It was research speed.

Here's what I keep seeing people get wrong with GTM automations. They build lead generation tools when the real gap is lead timing. Everyone's chasing more contacts. The smarter play is knowing exactly when your existing targets are ready to buy. A workflow that tells you the right moment is worth ten times more than one that gives you ten times more names.

The automation itself wasn't complicated. What took time was mapping the signals that actually mattered for their specific ICP. That's the work most people skip because it doesn't feel like building. But that two hour conversation about their best closed deals from the last year... that's where the whole thing came from. The n8n workflow was almost secondary.

If your client is spending hours on research every week, don't ask them what they want to automate. Ask them what they're always too late for. That's where the money is.

Just finished building my first real automation for someone's actual business (my friends)using n8n.

The workflow takes a supplier PDF from Google Drive, extracts all the text, sends it to Claude AI with a structured prompt, parses the JSON response, and routes each product category to the correct sheet tab in Google Sheets automatically.

A few things I learned building this:

n8n's Extract from File node only reads one page of a PDF so I had to work around that.

Getting Claude to return consistent JSON structure across different product types took several prompt iterations.

Using a Switch node to route different product categories to different sheet tabs was the cleanest solution for organizing the output.

Still learning n8n but building real projects for real use cases is teaching me more than any tutorial.

Anyone with more n8n experience what would you have done differently? And what's the best resource for going deeper on more complex workflows?

https://preview.redd.it/iqtnqtnc67tg1.png?width=1383&format=png&auto=webp&s=9d123913387091225ae92d96fa12defb5cc60ce9

Most people think you need a powerful laptop to run AI models.

That’s not really true anymore.

I tested Google’s Gemma 4 model on Google Colab and was able to run everything for free without any heavy setup.

What surprised me is that you can do multiple things in one flow:

• Transcribe audio
• Summarize content
• Extract key insights from videos

For example, you can take a YouTube video and turn it into a clean summary with important points in a few steps.

One thing to keep in mind:

• Whisper is still faster and more accurate for pure transcription
• Gemma is more flexible because it can handle multiple tasks

So it depends on your use case.

If you are into content creation, research, or automation, this can save a lot of time.

I recorded the full setup and demo here if you want to try it yourself.

Curious if anyone else here is testing smaller AI models instead of relying only on APIs.

Hi,

I have used playwright to automate applying to jobs within my category, it utterly failed despite I recorded two to three applications submittions on LinkedIn.

I want help from anyone what is the best seemless one which can automate and apply for 20 jobs daily without any manual tasks by completely filling all the application details from given resume.

My task is scrape the web with jobs related to niche, then click apply and then fill all the details and submit. It is ok to use paid captcha and any vpn as well but I want task to be completed 100% or else at least 90 to 95% and I manually cross check and had to click submit also ok.

Please help me any AI automation experts

hello all i hope you are fine,

i have a good background about ai automation and n8n workflows

i want to build a workflow or automation solutions to solve real business trendy problems to be easily selled for business owners and have passive income every month

what are the best automation workflows ideas ?

Hello, I have a problem. I have a client who wants to work with me, but he doesn't know anything about automation. He offers courses and is a well-known figure. I don't know what kind of help he needs, or how I can suggest ideas to him because I know it's very difficult to ask him directly. I want to offer him a solution directly or suggest three automation options, and he can choose what he wants. He employs many people to perform repetitive tasks like replying to emails, etc. What should I do in this situation? I have experience building automation systems, such as email, lead generation, and content creation. What do you suggest? Should I hold an initial meeting just to ask him questions and identify weaknesses, or should I prepare automation options?

Hi

I’m looking for something where I can automate job applications ( auto apply ) by tailoring cv and also automate emails / msgs to be sent to hiring manager or recruiter for the particular role…

And also search for jobs tailored based on my skills

If you know anything pls dm

I’m planning to learn AI Automation and sell it to businesses as a service, but I'm wondering if it’s actually profitable or just hype right now. For those doing this, is there real money in it and realistically how long does it take to learn the tools like Make/Zapier/APIs well enough to start charging clients?

I'm not going to pretend I was some careless developer. I tested everything. Ran it through every scenario I could think of. Showed the client a clean demo, walked them through the logic, got the sign-off. Felt genuinely proud of what I built. Then eleven days into production, their operations manager calls me calm as anything... "Hey, something feels off with the numbers." Two hours later I'm staring at a workflow that had been duplicating records since day three because their upstream data source added a new field I never accounted for. Nobody crashed. Nothing threw an error. It just kept running and quietly wrecking everything.

That's when I understood what production actually means. It's not your demo surviving one perfect run. It's your system surviving reality... and reality is messy, inconsistent, and constantly changing without telling you.

The biggest mistake I see people make, and I made it myself for almost a year, is building for the happy path. You test what should happen and call it done. Production doesn't care about what should happen. It cares about what does happen when someone inputs a name with an apostrophe, when the API returns a 200 status but sends back empty data anyway, when a perfectly normal Monday morning suddenly has three times the usual volume because a holiday pushed everything. I started calling these edge cases but honestly that word undersells them. They're not edge cases. They're Tuesday.

What changed everything for me was building for failure first instead of success. Before I write a single node now, I spend thirty minutes listing every way this workflow could silently do the wrong thing without throwing an error. Not crash... silently do the wrong thing. That's the dangerous category. A crash is obvious. Silent corruption runs for eleven days while you're answering other emails. Now every workflow I build has three things baked in before I even think about the actual logic. A heartbeat log that writes a success entry on every single run so I can see volume patterns. Plain English status updates to the client that show what processed, what got skipped, and why. And a dead man's switch... if this workflow doesn't run in the expected window, someone gets a message immediately.

My current client is a mid-sized logistics company. Their workflow processes inbound freight confirmations and updates three separate systems. Runs about four hundred times a day. The first version I built worked perfectly in testing and I was ready to ship it. Then I did something I'd started forcing myself to do... I sat with it for a week and just tried to break it. Sent malformed data. Killed the downstream API mid-run. Submitted the same confirmation twice. Every single one of those scenarios became a handled case with a proper fallback before it ever touched production. That workflow has been running for four months. Not four months without issues... four months where every issue got caught quietly instead of becoming a phone call.

Here's the thing nobody tells you about production automation. The goal isn't zero failures. That's not realistic and chasing it will make you build worse systems. The real goal is zero surprises. Every failure should be expected, logged, and handled with a fallback that keeps things moving. A workflow that gracefully handles a bad API response and queues the record for retry is ten times more valuable than a workflow that never fails in your test environment but has never actually met real data. Your clients don't care about your architecture. They care that things keep moving even when something breaks, and that they hear about problems from your monitoring before they find out themselves.

Production readiness cost me more upfront time on every single project since that incident. And it's made me more money than any technical skill I've ever learned. Because the clients who've seen it working for six months without a crisis? They don't shop around. They just keep paying.

What's the failure mode that's cost you the most? Curious whether people are building this in from the start now or still getting burned first.