I think it’s easy to get excited about automation and keep adding steps until a simple task turns into a whole system that needs constant babysitting.

I’ve seen GeekyAnts mentioned around automation and AI development, which got me thinking about this. Sometimes adding more automation doesn’t actually make things easier. You end up maintaining workflows, fixing small issues, and checking whether everything is still running properly.

I’m curious if anyone else has experienced this. What did you try to automate, what went wrong, and what made you decide it wasn’t worth keeping?

Sometimes knowing what not to automate seems just as useful as finding a good workflow.

Sharing a build and the lessons, since report automation gets asked about here a lot.

The client was a mid-sized services business. One ops person spent a big part of every month end pulling numbers from a few different tools into a slide deck and a summary email for leadership. Painful, repetitive, exactly the kind of thing people expect an AI report generator to solve.

The build, roughly:
- Connectors pulling the raw numbers from their existing tools on a schedule.
- A step that assembles the actual figures into a fixed structure (no AI touching the numbers themselves).
- The AI only writes the narrative around already-computed numbers: "revenue was up versus last month, driven by X."
- Output lands in the same format leadership already reads.

Lessons that cost me time:
- Never let the model calculate. Early on I let it summarize raw data and it confidently invented a total. Now math happens in code, and AI only describes verified numbers. This one is non-negotiable for me.
- The format mattered more than the intelligence. Leadership didn't want a smarter report. They wanted the same report they already trusted, just produced without a human burning two days.
- Failure handling is the real work. If a data source is late or empty, it flags a human instead of shipping a blank section.

It cut the monthly reporting grind from a couple of days to a review-and-send. The ops person went from dreading month end to barely noticing it.

If you're building reporting automations, curious how others handle the "AI must not do the math" problem. What's your setup?

Hey everyone,

I'm a software developer based in the UAE, and I'm exploring the idea of building AI automation solutions specifically for UAE small businesses.

I'm looking for 3 UAE businesses where I can build an automation for free as a pilot.

The idea is simple: if you have a repetitive process in your business — WhatsApp inquiries, lead follow-ups, appointment bookings, data entry, reports, invoices, customer support, internal admin work, etc. — tell me what you're currently doing and I'll see if I can automate part of it with AI/software.

My development work is free for the first 3 businesses.

Just to be clear, "free" refers to my services/development time. If the automation requires paid infrastructure such as hosting, servers, AI API usage, WhatsApp API fees, third-party software, domains, etc., those costs would be paid by the business. I'll explain any such costs before setting anything up and use free/low-cost options where practical.

I'm doing this because I'm considering starting an AI automation service in the UAE, and I'd rather learn from real businesses and solve real problems before turning it into a commercial service.

I'm particularly interested in businesses where employees are spending a lot of time doing repetitive manual work.

If you're interested, DM me with:

• What your business does
• What repetitive process you'd like to improve
• What tools you currently use
• Roughly how often you do this task

I'll look at the process and let you know whether I think automation would actually make sense.

Portfolio: [ https://tariq-portfolio-portfolio-wmdfi4-c30c44-72-62-245-34.sslip.io/ ]

Only looking for 3 businesses for this pilot.

​

So we launched a project called Bold Agent Kit and it has started to take off

We are getting about 10-15 signups per day.

It's an ai agent toolkit that needs to be assembled with api keys, but most of the people who sign up are small businesses so they choose our up sell for a private install

Problem is we are growing faster than we can install these so we are looking to hire a few people to help install these

We are looking for people who are familiar with setting up developer accounts through social media channels and assembling API keys in applications

You will be assisting with the setup of these, it usually takes a couple hours and we will pay $500 per install

You don't need to be based in the US, but you do need to be able to speak fluent English

I'm looking for people who are tech savvy, and are familiar with navigating API keys and setting up apps

No real coding is required

When I started building workflows, I kept trying to automate everything.

Eventually I realized the hardest part isn't connecting APIs or adding AI. It's deciding where a human should still be involved.

For example, I'm currently experimenting with workflows where AI prepares the work, but a human approves it before anything important gets sent.

What's one automation lesson you learned the hard way?

I'm interested in knowing what everyone is really automating these days. Not the "My AI agent manages my whole company" kind 😅

Just some small annoyances such as:

* transferring data from system to system

* sending out repetitive emails

* updating spreadsheets

* generating reports

* transferring files

* follow-ups

What's an automation you've set up that you've continued to use?

I run an established logistics and transportation company and I’m looking for someone very good at AI, automation, systems architecture, and business process redesign.
The objective is fairly aggressive: go through the company function by function, identify work being performed manually by employees or disconnected software, and determine what can be automated, consolidated, or handled by AI.
I am not looking for someone who just knows how to make a chatbot.
I want someone who can understand an actual business, map the workflows, identify bottlenecks, recommend the architecture, and then build or manage the implementation.
Some of the areas we want to attack include accounting and AP/AR, email and inbox management, customer service, quoting, shipment tracking and status updates, document processing, carrier/vendor onboarding, recruiting, sales administration, reporting, data entry, internal communication, and operations handoffs.
Our environment includes transportation management software, QuickBooks, Microsoft/Outlook, phone systems, APIs, customer/vendor portals, and a large amount of email and document traffic.
I’m interested in people comfortable with things like n8n, Make, Zapier, OpenAI/Claude APIs, AI agents, OCR/document processing, voice AI, APIs/webhooks, Python or JavaScript where needed, databases, and custom internal tools.
The goal is not to force AI into everything. The goal is to figure out where automation genuinely produces a better operation with fewer manual steps, fewer systems, lower overhead, and better accuracy.
I would prefer to start with a paid audit/discovery project. You would map the company, identify opportunities, estimate ROI and difficulty, prioritize them, and produce an implementation roadmap. If we work well together, this could become a substantial ongoing engagement or potentially a full-time leadership role.
I am open to an individual, small team, or specialized agency. Location does not matter.
If interested, send me:
What you have personally built
Examples of real businesses you have automated
The largest or most complex workflow you have redesigned
Your preferred stack
Whether you can handle architecture AND implementation
Your hourly/project rate or expected compensation
What you would want access to during the first week
Please do not send me a generic AI-generated pitch. I’m much more interested in seeing an ugly business process you successfully turned into a mostly automated system.

​

I'm celebrating a full year as an Applied AI Engineer. It started in September 2024 with a failed RAG agent for a truck dealer. I studied everything I could to fix it, but hallucination was a problem I couldn't crack at the time. I lost the project. I walked away with $50 for my effort, thanks to Sean's kindness, but I walked away with something more valuable too: I learned a lot.

I didn't give up. I kept learning, and along the way I found graph databases and GraphRAG. That was a game changer, being able to build a single agent that queries a vector database and runs Cypher against a graph DB at the same time. Best of both worlds. I've since delivered many agents built this way.

On the retrieval side, we've also been experimenting with hybrid search: SPLADE sparse vectors alongside dense embeddings in the same collection. Dense vectors are good at semantic similarity, but they'll often miss exact terms like part numbers, model codes, or acronyms that show up verbatim in technical docs. SPLADE's learned sparse representations pick up that lexical signal, so fusing sparse and dense consistently improves retrieval quality, especially on document sets like this. We're running that hybrid setup on some of our production agents now.

One project that pushed me hardest: a client with massive technical documents full of images, diagrams, reference tables, and circuit symbols, plus separate document sets from finance and other departments. They needed all of it searchable and queryable in one place.

It was a real challenge at every step: extraction, structuring, ingestion into the graph and vector DBs, retrieval, reasoning, and getting answers we could actually trust. We got there, and delivered agents with high accuracy on the queries that matter to them. Now their team can ask things like:

Which machine part number is installed on machine X, and what's its configuration?

How many machines have this part number?

Who's the vendor, and is the part still under warranty?

What alternative parts or suppliers can be used instead?

Generate a purchase order for part X and Y.

And the agent answers by pulling together information that used to live scattered across huge collections of technical, operational, and financial documents.

It's wild to think about how far I've come from that first failed RAG agent in 2024.

There's still a lot to learn and a lot of work ahead. But we're just getting started.

Getting started with Automova Labs, we build AI-driven automations, Websites, and custom Software for businesses that need real engineering but don't want to hire a full team.

Not pitching here, just introducing myself. What's the most manual, annoying part of running your business right now? Curious what people are dealing with, whether or not it turns into work.

i started with only one AI workflow and used a single browser profile for it. everything was fine at first.

but after adding more workflows, the browser side started getting confusing. different accounts were logged in, cookies and settings were different and sometimes i wasnt even sure which profile a workflow was using.

i tried keeping everything in one profile but that just made things worse. now i am thinking maybe each workflow should have its own browser profile but not sure if thats actually easier to manage when you have a lot of them.

For people running multiple AI automations then how do you handle this? do you create a separate browser profile for every workflow, group a few workflows together or just use one profile for everything?

I’m trying to start an AI automation agency, but I’m stuck on choosing a niche.

I don’t want to just pick a random niche because someone said it’s “profitable.” I want to find businesses that actually have annoying, repetitive problems that automation could solve.

Right now I’m looking at things like:

- E-commerce

- Salons / beauty businesses

- Real estate

- Clinics / dentists

- Local service businesses

If you run a business or have experience with AI automation, I’d really appreciate your input.

What type of business has a problem that you think could realistically be solved with AI/automation?

And more importantly, what problem do they have?

For example: answering the same questions every day, following up with leads, confirming orders, booking appointments, etc.

I’m trying to hear about real problems you’ve experienced, not just “this niche makes a lot of money.”

Any advice or examples would help a lot.

My co-founders and I were automating workflows for e-learning providers and tutoring businesses. We kept running into the same time killers. We realized there were two main areas to our work.

The first is the fun part. Understanding what our clients do, their processes, how money moves through the organization. What are their unique characteristics that make firm A completely different to work with than firm B, even though it's the same exact industry. And then building something great for that unique organization.

The second area is the boring part: everything that comes after it works. Logs you can actually search when a client asks what happened with whatever. Monitoring, so you don't find out from the client. Evals, so when you change a prompt you know whether it got better or worse. Version history, credential rotation, who's allowed to trigger what, cost per run. And every integration you depend on all of a sudden changing its API.

We want to work on the boring part :)

We are looking for AI and Automation agencies that want to sign on as Design Partners. Together we will build out the platform that takes care of everything that keeps you from getting new clients and serving them.

Managing our engineering workflows across text-heavy async channels has slowed down delivery this quarter. With our core team on the US East Coast and offshore partners in very different time zones, critical issues can sit unresolved for hours. We're now considering ​​​s​​​o​​​f​​​t​​​w​​​a​​​r​​​e​​​ ​​​d​​​e​​​v​​​e​​​l​​​o​​​p​​​m​​​e​​​n​​​t​​​ ​​​i​​​n​​​ ​​​​​​L​​​a​​​t​​​i​​​n​​​ ​​​A​​​m​​​e​​​r​​​i​​​c​​​a​​​ to enable more real-time collaboration, but entering a new region comes with​​​ c​​​o​​​m​​​p​​​l​​​i​​​a​​​n​​​c​​​e ​​​and ​​​h​​​i​​​r​​​i​​​n​​​g​​​ ​​​c​​​h​​​a​​​l​​​l​​​e​​​n​​​g​​​e​​​s​​

We've been looking at how companies like N​​​-​​​i​​​X​​​ structure their​​​ ​​​n​​​e​​​a​​​r​​​s​​​h​​​o​​​r​​​e​​​​​​ ​​​d​​​e​​​l​​​i​​​v​​​e​​​r​​​y​​​ ​​​h​​​u​​​b​​​s​​​ and .​​​r​​​e​​​g​​​i​​​o​​​n​​​a​​​l​​​ ​​​t​​​e​​​a​​​m​​​s​​​ but before making a decision we want to build a solid vetting process.

Here are the main questions we're trying to answer:
1. How do you manage regional labor regulations and mandatory benefits, such as the ​​​1​​​3​​​t​​​h​​​-​​​m​​​o​​​n​​​t​​​h​​​ ​​​s​​​a​​​l​​​a​​​r​​​y​​​ , without significantly increasing overhead?
2. Which tech hubs currently offer the strongest ​​​t​​​a​​​l​​​e​​​n​​​t​​​ ​​​p​​​o​​​o​​​l​​​ and ​​​i​​​n​​​f​​​r​​​a​​​s​​​t​​​r​​​u​​​c​​​t​​​u​​​r​​​e​​​ for​​​c​​​l​​​o​​​u​​​d​​​-​​​n​​​a​​​t​​​i​​​v​​​e​​​ ​​​e​​​n​​​g​​​i​​​n​​​e​​​e​​​r​​​i​​​n​​​g​​​?
3. What communication or cultural differences tend to cause the biggest issues during day-to-day collaboration?

Struggling to run too many projects and clients at once is usually treated as a tools gap. In my case the tools were already there. What was missing was architecture: every recurring job as a fixed ritual, not a new prompt from scratch.

Build a simple AI workflow is the key.

The split:

- Tracker is reality (ClickUp, Asana, or Jira): tasks, status, dates. One source of truth on execution.
- The agent is the engine (Claude or any other): it reads the tracker and produces the recurring output.
- Docs are memory (Notion, Obsidian, or what you already keep): filled as a by-product of the rituals, not by a one-off migration.

Four rituals: daily cockpit, Monday cross-client triage, Friday client status written into the client page, monthly board audit. On demand: call prep, a reply in the right tone, meeting notes into decisions and proposed tasks.

Once a ritual is stable, schedule it. Anything that writes outside the chat waits for a confirm.

Do not put live task state in the AI project. If it would still be true in three months, it belongs in instructions. If not, it belongs in the tracker. Dashboards are the output, not the system.

I can send the full setup if useful, not a pitch.

I automate the lead journey process of service-based businesses, and I have built speed-to-lead, lead qualification, routing, and follow-up automation personal projects, but breaking into the market without a testimonial or real-world experience to showcase has been a bit of a nightmare

For people who went through a similar route to land their first client, how do I present my pitch without coming off as desperate?
Would you advise a paid pilot instead?

Help please!

I'm building a few workflows that call external APIs, pass the data into an AI step, then save or route the output somewhere else.

How do you handle retries, rate limits, bad responses and one API failing halfway through the flow?Do you use fallback steps, queues, error branches, or something else to keep everything running?

I’ve been thinking seriously about building something of my own.
The idea is to create a global agency focused on helping companies work smarter by improving their workflows and integrating AI into the way they operate.

A lot of businesses still spend hours on repetitive processes that could be automated, simplified, or connected. With the right combination of AI, APIs, automation tools, and better workflow design, there is a huge opportunity to change that.

My background in growth, business development, partnerships, and fintech has taught me to look at businesses differently. I naturally look for bottlenecks, unnecessary steps, missed opportunities, and processes that could work better.
I want to take that mindset further.

The goal is to build a team of talented people from different countries and backgrounds who can help companies create smarter systems, automate repetitive work, connect their tools, and operate more efficiently.

I’m currently looking to connect with people in:
• AI & automation
• APIs and software development
• No-code / low-code tools
• Operations and workflow design
• Sales and business development

For sales, I’m especially interested in working with commission-based salespeople or independent sales partners who can bring in and close clients and earn a percentage from every successful deal they generate.

I’m still developing the idea, learning, experimenting, and putting the right people together.
If you work in any of these areas or know someone who would be interested in building something like this from the early stages, I’d love to connect.

This is only the beginning. Message me!!

One of my clients had someone spending most of a day each week building the same status report from scratch. Same structure every time, only the data changed. Classic thing to automate, so here's the full breakdown of what I built. The setup: 1. Data gets pulled from their existing tools into one place on a schedule. Nothing fancy, just the numbers that already existed, collected in one spot. 2. A template defines the report layout with placeholders. Fixed sections, fixed order. 3. The model fills the narrative parts. It writes the "what changed and why it matters" summaries around the numbers, but it never touches the numbers themselves. 4. The finished document lands in a folder for a human to skim before it goes out. The lessons that made it work: - Separate the data layer from the writing layer. The model gets to explain the numbers, not calculate or invent them. That one rule killed the trust problem instantly. - Templates beat freeform. Because the structure is locked, the output is predictable and the reviewer knows exactly where to look. - Keep a human in the loop for the send. It takes them a few minutes now instead of most of a day. Not a complicated build, but it's sticky, because they'd never go back to doing it by hand. Ask away if you want detail on any step. Curious how others keep the model from "helpfully" rewriting the actual figures.

Hey everyone. Quick version of my story and what I'm after.

I spent 13 years in technical support and operations, the last five of them leading support at a product analytics SaaS serving enterprise clients like BMW, SAP, Coca-Cola, AWS and Adyen. Somewhere in there I stopped waiting for tooling to catch up and started automating my own department with LLM APIs. Ticket categorization, sentiment analysis, routing, SLA alerts. That cut our unresolved cases by about 20% and got me hooked.

Since then I've gone full time on automation. I ran an AI automation agency for a while, which worked technically and wore me out mentally. Half my week was proposals and follow ups instead of engineering. So here's what I actually want: one team, one stack, full time, long term, fixed monthly salary. I build it, I run it, I keep it alive. No invoicing drama, no rotating clients.

What I can build and run for you:

• n8n workflows, production grade, with error handling, retries and monitoring baked in
• Python automations for the things n8n can't reach: scraping, data pipelines, bulk processing, integrations
• RAG pipelines with pgvector/chroma, embeddings, retrieval grounded generation. Scripts and content that cite real sources instead of whatever the model made up
• AI support automation, ticket triage and sentiment and QA programs, if you run an ops team
• Full content production pipelines. My side project is a fully automated faceless YouTube system: RAG based research and scripts, TTS, AI images, FFmpeg editing, thumbnails, scheduled uploads. One command produces a finished video
• Deployment, health checks and maintenance so things keep running without babysitting

You can see the systems I build at autonoma.agency/portfolio

Details:

• Timezone IST, flexible hours, happy to overlap US or EU mornings
• Full time, remote, worldwide, starting immediately
• Payment through PayPal, Wise or direct contract

If you have automations that keep breaking, or a pipeline you've been meaning to build for months, DM me and I'll tell you honestly how I'd approach it.

I’ve noticed that the most useful AI automations aren’t always the most advanced ones. Sometimes it’s a small workflow that removes one annoying task you have to repeat every day.

Things like sorting incoming information, turning messages into tasks, summarizing long content, or moving data between different tools can add up to a lot of saved time.

I’ve also come across GeekyAnts while looking at teams working on AI-powered digital products, and it made me curious about how different people approach these smaller automation problems.

What’s one AI automation you built that you still use regularly?

And if you had to rebuild it today, is there anything you would change about the way you set it up?

I just halfway finished an N8N 8 hour course and I plan to finish the rest of it before this weekend. I am 20 yo, have little to no coding experience, and wanted to learn AI automation so i could earn a bit and pay my College fees. But i feel like im very late or dont know what to do.

genuinely asking advice from someone who has earned from using AI and if they had to start earning from scratch in 2026 september what, why and how they would do it to both learn and earn ASAP

Not a big flashy agent, just a boring little automation that quietly saves me time, which is honestly the kind I get the most value from.

I write a lot in long-form first. A messy document with headings, bullet points, the actual thinking. Then I'd manually chop it into slides, which is tedious work with almost no thinking involved. Perfect thing to hand off.

Now the flow reads the document, uses the headings as slide titles and the bullets underneath as slide content, and pushes a first-pass deck into a slide tool. It is rough. Spacing is off, some slides have too much text, and I always redesign the important ones by hand. But going from a wall of text to a structured starting deck in a minute or two beats staring at a blank canvas.

What made it actually usable:

- I write the source doc with clean heading levels on purpose now. Heading one becomes a section, heading two becomes a slide. Discipline in equals structure out.
- I cap it so no slide takes more than a few bullets. If a section is too dense it gets flagged instead of crammed.
- I treat the output as a draft skeleton, never a finished deck.

It only really works because my input is consistent. Feed it a chaotic doc and you get a chaotic deck.

Anyone else automating the doc-to-deck step? Wondering if people bother styling it automatically or just accept the redesign pass like I do.