I run an agency out of India which was limited till SaaS, Mobile apps, e-commerce, demand generation, etc. I’ve got clients mostly around USA, Australia and little bit in Europe as well. I’m not much of technical guy but I know how to market and sell very well.

I’m planning to expand into AI services, and looking for a trustable partner in India (No agencies please). We can initially work on some internal POCs (paid) and some project based contracts.

I’m open to partnering or co-running the AI vertical together in future if things workout well. I’m also open to discuss product/service ideas if you got.

i'm looking for a browser mainly for automation tasks like account management, testing, and running workflows. i've seen both 1browser and gologin mentioned, but i'm curious how they compare in terms of stability, profile isolation, and automation support.

if you've used either one, which would you recommend and why? are there any other browsers that work better for automation?

Dripify costs money n my n8n workflow automation chain for automated LinkedIn messages was free. LinkedIn updated a frontend selector last Tuesday n my headless browser flow started throwing errors. I spent three hours fixing it before my first call.

The DIY automation community makes linkedin message automation look easy. Scrape leads. Enrich them. Fire off connection requests. Then u deploy it n LinkedIn changes their DOM structure. Or ur proxy IP gets flagged. Has anyone here maintained a DIY automation stack for more than six months?

I've been trying to understand how AI outbound calling actually works because I'm seeing it mentioned more and more in automation project. From what I understand, there are a few pieces involved- the system making the calls, the AI voice handling the conversation, and the CRM tracking what happens afterward.

The part I'm still confused about is how these pieces usually connect. Is the dialer normally built into the voice platform, or do people usually connect separate tool together? Also curious how these setups handle scaling before things start getting messy.

If anyone ha worked with a real setup, I'd love to hear how it was structured.

I am trying to build a meeting pipeline that does most of the boring work for me. The dream is record, upload, get transcript, push action items to Todoist, publish a summary to Notion, email the attendee list. I am about halfway there.

Currently I use an n8n webhook to listen for new files in a folder, send them to a transcription service, and then format the result. The fragile part is the transcription step. Some APIs take forever, some have strict file limits, and some return summaries that are too generic to be useful.

I have been testing vomo ai as the transcription step for now. I upload through the web app, download the transcript and action items, and then format them into the next steps. There is no native API yet, so the upload and export steps are still manual glue.

What I can get running in one weekend is something like this. If you have glued transcription into an agent workflow I would love to hear whether you went local or cloud and how painful the handoff was.

the failures that scare me are not the ones where the workflow crashes. those are obvious.

it's the partial success ones: it wrote the draft, skipped the approval note, used an old source, scheduled the wrong version, or marked the job done because one step returned ok.

i've started treating every automated content or ops flow as needing three outputs:

• what changed
• what was skipped
• what needs a human decision

that sounds boring, but it catches a lot of fake progress. if an agent cannot explain the skipped part, i don't really trust the completed part either.

curious how others handle this. do you log partial success as a failure, a warning, or just a normal run with notes?

I've been experimenting with AI automation over the last few months, and one thing surprised me.

Building the automation is often the easiest part. Understanding the client's workflow, handling edge cases, fixing unexpected issues, and maintaining everything takes much more time than I expected.

I'm curious how others here are handling this.

• Do you charge a one-time setup fee, a monthly retainer, or both?
• Do you ask clients to pay for APIs and software directly, or do you include those costs in your pricing?
• What's one pricing mistake you made early on that you'd never repeat?

I'd love to learn from people who are already working with AI automation clients.

I got interested in workflow automation in my last job, and I’ve now set up n8n on a virtual private server and am using it to host workflows for the full-stack project I’m building, as well as my personal website.

The project is an AI and automation enhanced business simulation, with the idea being to experiment with where I can use AI and automation to, well, enhance the system. The project is still in its infancy, and so far all I’ve done is automate my notion dev log to my website dev log, and automate the contact form on my site.

Automations I have in mind:

·       notifications for customers and merchant (it’s both sides of an ecom store)

·       things around orders being placed such as a calendar note, inventory updated

·       coordinating marketing development for ‘new’ products added to the system

·       sales analysis > email summary

·       eventual CRM integration

These are just ideas, and I know that automation should come as problem solving/time reducing than backbone project structure, but I haven’t got that far yet so I’m still just letting my mind run with it.

Anyway, I thought I’d post this as an introduction as I’ll be posting more automations as I build them.

If anyone has built anything similar, or has any suggestions, I’d be interested to hear.

Disclosure up front: I work at Serval now, which is one of the tools below, so weight this accordingly. The reason I ended up there is basically the reason for this post. About 18 months ago I was running internal IT for a ~600-person org, inherited Freshservice, and "70% of our tickets are repeats" had become the whole problem, so I ran a real eval. I've tried to write this so it's still useful even if you land somewhere else, and I've left out a couple of tools I can't speak to honestly.

The thing I'd tell past-me: the real fork isn't "which vendor," it's whether you want chat to be an intake channel or the actual place work gets done. Most tools that say "Slack support" just open a ticket from Slack and then move you back into a web console. A smaller group tries to resolve the request inside chat before a ticket ever exists. Those are different products with different day-200 realities, and the demos make them look identical.

How I assessed them: what share of requests actually resolve inside Slack/Teams, how much automation happens before a ticket is created, admin/maintenance overhead, and whether it holds up as volume grows. Here's where each landed for us.

Freshservice, best for structured ITSM with asset and change control. If you want a clean service catalog, asset lifecycle, and change management with defined process, it's solid, and it's what we ran at the time. Slack/Teams are submission channels; most real work stays in the web console. Where it strained for us was exactly the repeat-ticket problem, it organizes the queue well but doesn't do much to stop the queue from filling.

Jira Service Management, best for engineering-aligned orgs already in Atlassian. Strong SLA and approval workflows, deep ecosystem fit, real governance. Chat is intake, configuration lives in Jira. Heavier to set up and maintain than the AI-native options, but if your IT and dev worlds are tightly coupled it's a defensible pick.

Atomicwork, best if you want AI-native but from an ex-Freshworks lineage. Genuinely in the modern-ITSM lane and shows up on most of these shortlists for a reason. Worth piloting alongside the others; where it lands depends on how much of your volume is knowledge-answering versus action-taking.

Moveworks, worth a note mainly for context. It was on my shortlist back then as the AI-assistant/deflection layer, but ServiceNow acquired it (closed end of 2025), so it's now part of ServiceNow rather than a standalone tool you'd buy on its own. If you're already deep in ServiceNow it's becoming the native front door. I'm leaving it here because it shaped how I thought about deflection at the time, not as a current standalone pick.

Serval, best when the pain is action-heavy repeat requests, not just questions. It resolves a request end to end inside Slack, Teams, email, or the portal instead of routing it faster, so an access or onboarding request actually runs (identity and group changes, provisioning, approvals, logged per step) rather than becoming a ticket worked later. The automations are deterministic, so nothing improvises against production. Honest tradeoff: if most of your volume is KB questions rather than cross-system actions, a good deflection tool may serve you just as well.

Where I'd start if I were doing it again: take your actual last 90 days of tickets, cluster them, and look at whether the top clusters are questions or actions. If they're mostly actions (access, onboarding, group membership, resets), weight the automation-first tools. If they're mostly questions, a strong deflection/KB tool gets you most of the way. And whatever you pilot, test it on your real ticket data for two weeks, not the demo. The demos all look the same. The two-week pilot on your own volume is the only thing that told me the truth.

For people running real business automations, which problems cause the most ongoing work?

I’m particularly interested in workflows involving AI agents or multiple external tools.

Do you struggle more with:

• Broken integrations
• Expired credentials
• Unexpected data formats
• Duplicate actions
• Approval steps
• Failed retries
• Poor visibility into what happened
• Fixing workflows that only partially completed

Are tools like Zapier, Make, and n8n sufficient once workflows become complex, or do you eventually need to build a custom reliability layer?

I’m researching this problem and would appreciate honest examples of what breaks in practice.

I dread this task and would appreciate any specific direction about how to do this.
If possible explain to me like a 5 year old please. I use Claude and CoWork but currently only at what I would describe as a D+ level.
Thanks very much.

Curious if anyone has found a workflow automation platform that actually reduces repetitive admin work instead of just moving the same problem somewhere else

We keep looking at these tools for basic stuff like intake forms, approvals, reminders, routing tasks, and copying data between systems, but every demo makes it look way better than it ends up being once real users touch it

Half the time the setup takes longer than just doing the task manually, and then you still end up babysitting exceptions anyway

Has anyone found something that is actually worth using for workflow automation, or is this mostly just a fancy way to create more admin on top of the admin.

Hey, hey everybody, how's it going?

So, a while ago I got laid off from where I was working. A couple of weeks later I was still in interview mode, and one of them basically told me I didnt have enough passion just because my path hasnt been linear. Different roles, different projects, startups here and there.

Personally, I dont agree with that.

Instead of waiting around for the next "yes," I started building something in the go-to-market space. A chat-first GTM Copilot. You basically ask it things like competitor analysis, ICP, funnels, LinkedIn/email campaigns, and it helps you figure out how to actually run GTM, not just research it. Nothing perfect, and honestly still not. But I launched it anyway.

Now, we are at $1,989MRR

Im still processing that a little. Not because $2k changes everything, but because a month ago this was mostly just me shipping and talking about what I was building.

One short example: we had a support call with a client that tried to run a competitor analysis workflow. They wanted everything on how their competitors distribute and execute GTM, but the output wasnt actionable. We spent a couple days figuring it out, built what they needed, tested it, and now their running it (with some limitations, but it works kinda). That type of feedback has been more usefull than any growth tip tbh.

What surprised me most is that the posts that actually brought people in werent the polished "here's how to do GTM" ones. They were the messy ones. The weird interview story. The "this broke and I spent two days figuring it out" posts. The stuff that feels to common to even share :/

People can smell a pitch pretty fast. They respond more when it just sounds like a real person went trough something specific.

I also launched earlier than I wanted to. I kept telling myself it needed one more feature, one more fix, one more week. If id listened to that, I'd probably still be polishing it in private.

And one thing I learned the hard way: you dont need to invent something completly new. I tried that first and it went nowhere. What worked better was looking at problems people already care about, noticing the part that still sucks, and making that part a bit more actionable.

Anyway, this journey just started. Still figuring out pricing, retention, and a lot of other things (even tho am working on the GTM space)

What do you guys think?

sharing an automation that's been quietly running my week.

What it does

• A WhatsApp message creates a card on a shared board and assigns it to an agent.
• The agent runs the work and saves a real deliverable.
• The moment it's done the task re-assigns to a person to review
• Per-agent model choice; the trigger surface (WhatsApp) and the app are the same agent on the same tables.

So: phone → board → agent does the work → human approves. No terminal, no dashboard-babysitting, whole team sees the pipeline live.

👷 How I built it Stack: Lemma (open source) + Claude — Time: a couple hours

1. Builder skill in Claude, described the flow.
2. One command scaffolded the system across six folders.
3. RBAC + approval workflow included; the WhatsApp surface is native, not a glued-on integration.

I have been thinking about this while tightening a few AI and workflow automations.

The part that makes me trust a workflow is not just that it runs once. It is whether it handles the boring edge cases clearly: missing inputs, duplicate records, stale context, failed checks, and actions that should stop for human review.

The pattern I keep coming back to is simple: define the input shape, define what safe to proceed means, return a useful stop reason when it should not run, log what changed and what did not, and make retries safe enough that they do not duplicate work.

That feels less exciting than adding another model or integration, but it is usually what makes the automation usable in real work.

For people building automations, what makes you comfortable letting one run unattended? Good logs, human approval steps, rollback paths, test runs, alerts, or something else?

I recently read on one of these subreddits about a Shopify founder who had an absolutely unhinged setup recently. They connected Midjourney to a print-on-demand pipeline so trending memes from Twitter/X automatically became t-shirt mockups within minutes. The system scraped viral posts, generated parody shirt concepts, created mockups, and pushed products directly to the store before most brands even noticed the meme existed. The crazy part is they said most of the sales came from being early, not from having amazing designs.

This was clearly an automation that humans just because of the speed and consistency. So curious, what is an automation that actually outperformed humans for you?

Hey,

I kept seeing the same thing with n8n. People build a workflow, test the happy path once, flip it on, and then something weird happens in production. Duplicate webhook. Missing field. Stripe retry. Suddenly you emailed half your list twice or charged someone wrong.

So I built Crash Labs.

You paste your n8n workflow JSON. It runs it against a fake company (mock email, Stripe, Slack, etc). Then it shows the blast radius. What would have been sent, charged, created. Pass / warn / fail.

It is early. Not every node is covered yet. Coverage and blind spots are in the report so it does not pretend it proved more than it did.

If you build client automations or anything that touches money / customers, try dumping a workflow in and tell me what it gets wrong.

the first one is already free. If you tell me your email you signed up with i can grand you more so you can test the product full!

Happy to take brutal feedback.

i described the outcome instead of drawing the workflow node by node.

a few minutes later, it had generated a flow that:

finds local businesses

→ researches their websites

→ finds and verifies public contact emails

→ removes duplicates

→ ranks the best matches

→ waits for my approval

→ saves the approved shortlist to google sheets

the image is the actual generated workflow graph, not a mockup.

still not magic: businesses with no website or public email will return nothing, and i wouldn’t let it contact anyone automatically.

where do you think this breaks first in the real world: website extraction, email verification, or lead scoring?

Small ops team (3 people) at an ecommerce company.

Built a support agent on top of GPT-4 with a long system prompt covering our return policy, shipping rules, etc. Works fine for 80% of tickets. The other 20% it either loops or confidently gives an answer that contradicts our actual policy.

Trying to figure out if the fix is a better prompt (feels like a losing battle at this point) or a genuinely different architecture.

Anyone moved off pure prompt engineering for something like this?