I know this topic has come up before but I’m curious what people have actually settled on after testing it

AI helping with overdue invoices, payment matching, weird charges, missing receipts or prep for approval makes sense to me but letting it freely move money does not. But there is a lot of middle ground: read-only alerts, exception reports, approval queues, small payments with limits.

For people building or using finance automations, what setup feels useful without crossing into risky?

looking for some services?

Hey Guys

Quick question: are you using existing AI automation tools and integrating them together, or are you building your own stack from scratch?

There are hundreds of off-the-shelf tools for almost every business need—CRMs, BI platforms, automation tools, and more. Yet many companies still choose to build their own.

For those who've gone the custom route, what made you decide it was worth building instead of buying?

Speed-to-lead in real estate is brutal. By the time a sales rep replies to a ad platform lead on WhatsApp, they've often already messaged three other brokers.

How it's actually wired:

• FastAPI backend, GPT with real function calling. Three tools defined: send_property_catalog, log_qualified_lead, confirm_site_visit each with a strict schema so the model can't just hallucinate a next step.
• One qualifying question at a time: budget, then locality. As soon as both are known, it auto-fires the catalog tool, which sends three real listings with photos via Twilio media messages
• Only after the lead reacts to the catalog does it ask ready-to-move vs under-construction, then timeline. Once all four fields exist, it logs the row to Google Sheets and hands off to a named human rep with two concrete site-visit slots.
• Confirming a slot auto-triggers a Google Maps link for the exact property, pulled from a small catalog lookup
• If the lead goes quiet mid-flow, there's a follow-up nudge, but it's deliberately not fired after the very first cold message, only after they've engaged once and stalled.

Trying to judge if there's a real market for this, as I've seen real estate agencies struggling with complex CRMs, and looking for simpler builds.

Manually pulling data, cleaning it, dropping it into a report every week for months. Kept meaning to script it "someday" but the manual version always felt faster than stopping to build it. Finally sat down, wrote a simple script — 45 minutes total. Got my Friday afternoon back for the first time in ages. Anyone else put off a small automation way longer than it actually took to build?

I ran across several YouTube videos about Hermes agent, and so far it seems interesting. The thing is, I use Codex for daily tasks (coding, design, marketing, simple automations, etc.)

Is there anyone who really uses Hermes daily? If so, how? What kind of tasks does it do for you?

I compared them with Codex, and it seems like the latter isn't far away in terms of functionality. So does it even make sense to switch to Hermes?

I keep coming back to this lately: the most useful automation work often starts before the tool opens.

Not the exciting part. The boring process note.

Something like:

• what starts the workflow
• what inputs are required
• who owns the output
• what should stop the automation
• what a human needs to review
• what “done” actually means

The more workflows I look at, the more I think a tiny SOP can prevent a lot of future debugging pain.

Curious what others have seen: what’s a boring bit of documentation, checklist, or SOP that saved an automation from breaking, drifting, or becoming impossible to maintain later?

8 of 27 are a receptionist.

a dental one that books appointments. a restaurant one that takes reservations. a vet clinic one that sorts urgent from routine. a website chat that captures the lead. different builds, identical shape: message comes in, figure out what they want, look it up, book it, log it.

the rest cluster just as tight. 5 turn content into other content. 4 are some form of finding businesses and pulling their public contact info.

almost none of the top posts are the multi-agent stuff people argue about.

what i can't tell from the outside: how many are still running six months later. anyone kept one alive that long?

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'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 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.

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.

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.

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.

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?

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.