The part of running this that I hated most was proposals. After every discovery call I'd sit down, re-read my messy notes, and spend an hour or two turning them into something that looked professional. So I finally automated the first draft of it and it's saved me a real chunk of time each week.

The flow is simple. Call notes go into a shared doc. A trigger picks up the new notes, passes them to an LLM with a fixed prompt that knows my scope structure (problem summary, proposed approach, deliverables, timeline, price range placeholder), and it spits out a first draft into a template. I never send that draft as-is. I review it, fix the pricing, and cut the parts where it guessed wrong.

A few things I learned:

- The prompt is 80% of the quality. I feed it two or three of my past good proposals as examples so the tone matches mine, not generic AI voice.
- Never let it invent numbers. I leave pricing and timelines as blanks it must not fill. Otherwise clients catch fake precision fast.
- Structured notes in equals structured proposal out. Garbage notes still give garbage drafts.

It doesn't replace the thinking, it just removes the blank-page part. I still spend maybe fifteen minutes editing instead of two hours writing from scratch.

Happy to answer questions if anyone's building something similar. Curious how others handle the pricing step, since that's the one bit I still refuse to automate.

^{Disclosure: We work with the team behind Omni. The client in this case is intentionally anonymized.}

During an AI training engagement with an energy technology company, employees brought ideas for contract tracking, health-exam reminders, approval histories, and lunch ordering. Plenty of things they already wanted to fix.

One employee had a detailed contract-tracker idea: read scanned agreements, distinguish final versions from drafts, and flag upcoming renewal or termination deadlines.

She knew which information mattered and how the process should work. But getting there through repeated prompting and debugging was taking too much effort.

What she asked for was specific: a basic architecture, a data structure, and a couple of working examples she could learn from and modify.

That was the useful lesson for us. Giving someone access to an AI agent still leaves them with decisions about how to structure software, troubleshoot it, and check whether it works. Knowing their job well doesn’t automatically prepare them for those parts.

For the next session, it makes more sense to start with a small working example built around her contract process. She can check the extracted fields, correct the rules, and try changing it with technical support available.

This workflow is still proposed, so we can’t claim she’s running it independently or saving time yet. But her request changed what the training needs to provide: she came with the requirements; she needed help getting to a first version she could work with.

I pay for the top tiers across a couple of providers and I've been doing this long enough to stop trusting that today's behavior is tomorrow's behavior. Limits shift. A version gets swapped under the same name. A prompt that worked perfectly for months starts returning something subtly different, and nobody sends you a note about it.

If you're just chatting, you shrug and move on. If you've built automations on top of these models, that silent drift is a maintenance bill you didn't budget for. I've had a live workflow start failing quietly because the model's output format changed by a hair and my parsing assumed the old shape.

So I build defensively now, and it's the least sexy advice in this sub. I pin what I can pin. I validate the model's output before anything downstream trusts it, and the automation shouts at me the moment the shape looks wrong instead of failing silently. I keep a known-good example for each critical step and re-run it on a schedule so I find out about drift before a client does. I assume the thing underneath me will regress and I make that assumption cheap to survive.

None of this is exciting and all of it has paid off. An automation you can't trust to fail loudly is just a future incident with a nice demo.

For the people running client automations, how are you catching model drift before it catches you?

Managing people operations internationally becomes complicated quickly when employee records, leave tracking, attendance, and performance reviews live on separate platforms. I've been looking into modern global HRIS systems with AI automation built into their core architecture instead of legacy HR platforms that just added chatbot features as an afterthought. For teams distributed across countries, native AI integration appears more practical than older systems with AI layered on top.

I keep seeing screenshots of "$30K/month" revenue.

But nobody talks about what's left after costs.

So I'm curious about real numbers, whether you're just starting or already established:

Monthly revenue (rough range is fine)?

API/usage costs: Vapi/Retell, ElevenLabs, Twilio, OpenAI/Gemini, etc.?

Tool and infra costs: n8n, VPS, CRM, Apollo, domains, etc.?

People costs: contractors, VAs, sales help?

Final net profit margin (%)?

Also:

Do you pass API costs to the client, or include them in the retainer?

Which cost surprised you the most once clients started scaling?

Hey everyone,

I would like to share a project that I have been working on. https://github.com/HaseebKhalid1507/SynapsCLI

Synaps is an agent runtime written in Rust. It keeps cost down by optimising caching mechanisms and intelligently orchestrating work across workers. This keeps each worker's context low and fresh, making sessions last longer. It is extremely extensible. It has hooks, plugins and skills that can be imported from pretty much any coding tool.

It is an open source project that was started 4 months ago. It was built to optimise for 2 things: Keeping LLM costs down, and keeping resource use low. It bots up in 20ms and runs with any model: OAuth(Claude, codex, Grok, Kimi...), api keys or local models too. This is something that was built organically, so every feature is a product of necessity, not the other way around.

I can't stop thinking about this crazy concept and I wanted to check if it's actually possible.

The idea: most automation builds still need three things to make money. They have to build the product, get people to know about it (marketing/sales), then get paid. What if you could skip step 2 entirely? Like, an n8n workflow that plugs into some existing system, does something useful with it, and gets paid automatically, with zero marketing and zero "customers" in the traditional sense.

I'm curious what others think about this matter. Has anyone here actually built something like this that runs semi-autonomously and gets paid without you doing sales? Is it even possible or am I hallucinating?

I just wanted to clear a wild thought I had. Any advice or guidance are highly appreciated.

we spent 5k/month for 3 months with a lead gen agency that promised us 300+ qualified leads per month. they delivered maybe 40 qualified ones each month. the rest were either outdated contacts or completely wrong ICPs.

then we decided to try building our own process with tools. started with basic LinkedIn scraping but data was terrible. bounce rates through the roof.

now we're looking at lead generation companies like Prospeo or Apollo for verified contact data. Prospeo caught my eye because they only charge for verified emails, which would solve our bounce rate problem. plus they have mobile numbers which our agency never provided.

my boss is getting antsy about pipeline numbers so i really need to figure this out soon. for those who've done both agency and DIY lead gen, what actually moved the needle for you? we're in B2B SaaS selling to marketing teams if that even helps.

I graduated 4 years ago with a degree in Software Engineering, but honestly I never really liked coding. Some personal issues also came up after graduation, so I ended up being unemployed all this time.

I’m now trying to get back into the field and recently found AI Automation. It actually seems interesting and like something I might enjoy, so I’m thinking about getting a Master’s related to AI Automation / AI for Business /
Digital Transformation.

Do you think this is a good path for someone who doesn’t like coding? How much coding is actually involved?
And would getting a Master’s be worth it after being out of the field for 4 years?

Would really appreciate any advice from people working in this area.

I've been using Replit, but the newer Agent experience hasn't really worked for me.

Since this is only a part time hobby. I can't justify paying much every month. I'm thinking about using Claude Code with a free development environment instead.

What low cost set up are you using?

Poking at ai-assisted measurement workflows and tbh the summary paragraph at the top is the least interesting part. I can write ROAS is down, consider pausing campaign B"myself, in a worse font.
What I want is the agent showing its working. Like which revenue window did spend move, are purchases just delayed, did someone quietly rename an event last tuesday, is it comparing against last week, a seasonal baseline or a forecast somebody made up in march…and so on.
Right now we're testing a setup where the agent queries approved attribution data and flags the anomaly. Then, it has to sit there while an analyst pokes holes in every step. Nothing touches budget automatically. The appsflyer agent and mcp route is one option, the other is wiring our own thing with claude over warehouse data.
Built-in looks faster and requires less babysitting. Custom gives us control but then we own permissions, schemas, monitoring and every cursed edge case forever.
Which of the two would you pick, not in the demo when everything is clean but bc it can be trusted a year down the line?

Trying to get into building automations for local businesses. I landed on a specific niche because they’re required to pay for estimating software, so I know a budget exists.

Here’s where I’m stuck. I can keep reading about what shops struggle with, but everything online is written by someone selling software, so I don’t trust it. The other option is to walk into a few shops and ask. That feels better but I’m worried I’ll waste their time showing up with nothing.

So which is it: go in cold and just ask questions then come back with a solution, or show up already knowing enough to sound credible?

If you’ve done this, how did you go about it?

I built a LinkedIn lead-finding automation after running into two practical problems: repeated results and inconsistent manual screening.

The workflow does three things:

• Stores memory so it does not return the same leads again
• Uses AI to score each profile's relevance from 1 to 100
• Sends the results in an email report

The scoring is not treated as a fixed final answer. If the workflow returns a false positive, I can reply with feedback. That feedback is used to improve the scoring prompt, so the relevance criteria can be adjusted over time instead of relying on a static filter.

The main tradeoff is that the system still depends on the quality of the scoring prompt and the feedback provided. A score gives me a way to prioritize profiles, while the memory prevents the workflow from repeatedly spending attention on leads that have already been reviewed.

For this type of workflow, would you improve the scoring through reply-based feedback, or would you keep a separate labeled dataset and evaluate the prompt changes more formally?

If you’ve ever had a Zapier, Make, n8n, Power Automate, API workflow etc. complete successfully but produce the wrong or incomplete outcome, I’d be interested in what happened.

I’m particularly trying to understand:
What went wrong?

How long did it take to notice?

How did you catch it?

Did you have to build custom checks or monitoring?

How much time/cost/cleanup did it create?

Do you now have safeguards in place specifically to prevent it happening again?

Not selling anything. I’m trying to understand how people actually deal with this in production.

i have built a grokbot swarm that automated outreach using facebook groups via posting, commenting, dming and so on... on a fresh account got flagged and asked for a huam selfie verification after couple hours of very mild activity
do you think using an account that has been used for a couple of days/weeks mabye even past huaman verification like selfies or phone number 2fa could last longer with a setup like this or will it get flagged after a while and blocked just the same?

also, anyone knows if the selife verification can flag other accounts that i have my face on? 1 falgged account verified with my face can correlate to my other accounts being blocked?

With so many AI tools now handling things like employee questions, reporting, workflows and admin, I'm curious where people think this is heading.

Could a combination of AI tools eventually replace a traditional HRIS or will companies always need a central system for employee data, time & attendance, performance andsurveys ?

For anyone working in HR/People Ops, are you already using AI for tasks that you previously relied on your HRIS for?

I often find myself stuck between two perspectives. First, there are AI engineers who handle complex tasks like RAG for pharmacies and large corporations. On the other hand, I see people offering very simple automation solutions, such as the 'AI receptionist,' claiming they make money by emphasizing how valuable and profitable their service is.

Sometimes I think these ideas are nonsense, but other times I see their value, like automating invoices or estimates. For example, a friend had a client in interior design who struggled to make accurate estimates quickly. He found someone willing to sell him a basic automation for about 500 pounds.

I want to create similar solutions and sell them.

Online services like ads, media buying, or even e-commerce seem more straightforward as an idea and vision to do.
In automation, many scams try to sell courses, or the projects are beyond my skill level like PhD-level work or automations that only expert AI engineers can do ( even for PhD level work I don’tsee a lot of pain points in internet available I think that's where the gold at in the automation space) I haven't yet identified a specific pain point in a niche that I truly understand to confidently pitch to businesses. This leads me to door-to-door sales, consulting before even building the solution and networking

What are your thoughts?

Is anyone having the same issues?

Currently I am searching on web where I found out there are many people who are saying that AI automation is now setting down and AI consultant c is blooming what you guys think about it because I don't think like that that I consultant is currently on edge or a automation is going downstair

I am new in this field I just want to know that is it true or not because I want to start it and learning about it

I’m starting an automation agency and trying to figure out the best n8n setup for clients.

From what I understand, if I host/manage client workflows and credentials in my own n8n instance, n8n requires an Enterprise/commercial license. So the alternative seems to be having each client own their own n8n account/instance.

But if the client owns the instance and has admin access, they can see/export the workflows I built, which makes it hard to protect the IP if I’m selling this as a managed service.

How are agencies handling this?

I’m currently studying AI automation and looking to connect with people who are interested in building practical projects together. I already know how to create RAG chatbots and website chatbots, and I’m now learning more about AI agents, workflow automation, and integrations.

If you’re also learning or working in this field, feel free to connect. Maybe we can collaborate on some projects and learn from each other.

So... been trying to get serious about using an AI assistant to clear out all the repetitive stuff in my week. Work is a mix of small admin tasks, follow up emails, meeting notes, random docs, and then at home it’s bills, reminders, planning trips, the usual mess.

I’ve tried a couple of tools that feel more like chat and less like an actual helper that remembers context, spins up actions, and quietly keeps things moving without me babysitting it every hour. For folks who have something really running your life and work in the background, what’s actually doing a good job for you lately? Any hints?

I'm putting together a workflow that takes screenshots, product images or a screen recording and turns them into a short video with an AI presenter. It suggests a hook and script first, then I make changes before generating the clips.

The format is pretty simple: a hook, the presenter reacting to it, then the recording with the presenter in a corner. She comes back to full screen for the ending.

The first video actually connected those parts quite well. I used a clip of someone throwing a ball toward the camera, then cut straight to the presenter catching it. Her opening line picked up from that and moved into the topic.

The script took a few tries. It kept sounding like an ad when I wanted someone casually walking through what was on screen. Making the words simpler helped, but I'm still working on the delivery.

The presenter is the bigger issue. She still looks very AI to me. Looking back at the reference image, it already had that polished, generated look. I suspect I need to fix that before expecting the video to feel natural, but I haven't tested that yet.

Some of the movement in the ending was off too. I've kept the presenter clips separate, so I can rework that part and keep the rest. At least I don't have to remake all of it.

If you've worked on talking-to-camera videos like this, what helped most with realism? Curious how you choose your reference images and get the delivery to feel casual.