Hey guys, I've been building a small experiment around AI agents. I’m just trying to see if developers or people that use ai for heavy workflow would find something like this useful?

The basic idea is:
You describe a task - the system figures out what specialist is needed - finds the best available agent - delegates the task - returns the best result.

So instead of you having to figure out which AI/tool/agent to use, the network handles the procurement for you and gives you the best match based on your task.
I've got a basic working prototype now and I'm looking for people to try it and tell me where the idea falls apart.

I'm particularly interested in tasks where you'd normally need to use multiple tools or hire someone.
I'm genuinely trying to build something useful, any feedback would be appreciated. I’ll drop a link in a few days if anyone would actually be interested in trying this out.

If you think this is genuinely crap and no one would use is, that’s great too.

Cheers everyone 😁

Hi everyone,

I’m looking to build an AI-powered quotation system for an electronic components trading business and would appreciate advice from anyone experienced with n8n, Gmail, OpenAI, or AI automation.

The main problem is that we already have thousands of previous quotation emails from suppliers/brokers, and many of those emails already contain our final quoted selling price.

I want to automate the process like this:

Customer Email → n8n → Extract MPN & Quantity → Search Existing Quotation Emails → Find Matching Previous Quote → Use Existing Quoted Price → Prepare Our Quotation → Human Approval → Send Email

For example:

Previous quotation:
STM32G031G8U6TR — 50,000 pcs — USD 2.40/pc

New customer enquiry:
STM32G031G8U6TR — 20,000 pcs

The system should find the previous quotation and use USD 2.40/pc.

It should NOT add another margin, because the existing price is already our selling price.

It should also check:

• Exact MPN
• Manufacturer
• Customer quantity vs quoted quantity
• Previous quotation date
• Price validity
• MOQ
• Lead time
• Stock
• Supplier/source
• Multiple previous quotations

If the previous quotation is expired or the quantity is different, it should flag the item for manual confirmation rather than making up a price.

What I want to know

Would n8n + Gmail + OpenAI + a database be the best way to build this?

And what would you recommend for storing/searching thousands of previous quotation emails?

Any practical workflow examples or advice would be greatly appreciated.

Thanks!

Hello,

I am working as an AI/ML Intern at a software company that builds ERP software based on companies' needs, such as schools, Construction sites, Hospitals, Fuel Pumps, and so on. I need help building an AI Chatbot which can be integrated with an ERP application. The company uses .NET as their main language for building applications.

For example: an ERP application for a school

There are user roles: Admin, Principal, Teacher, Accountant, Student/Parent, Driver

Student lifecycle: Admission → Academic → Attendance → Examination → Result

ERP Modules: Admission Management -> Admission, Registration, Entrance Admit Card, and so on, such as Billing Management, Transport Management, and Examination Management

The task is to integrate an AI system into the ERP web application in which:

1. A user, such as an admin, principal, or accountant, inputs a question in natural language like "Give me the names of students whose remaining dues are over 5000 since last month" or "How many students are present today" or "Show me a graph of student attendance for the last 3 weeks"

2. The input is then converted to a relevant database query and searches for all the required details from the user input. The ERP uses Microsoft SQL to store all its data.

3. The AI generates output in natural language for the user and shows the relevant answer.

Restriction: No user should be able to alter any of the data in the database. It should be read-only.

I am doing market research and I am looking for people who can spare 10 to 15 mins of their time to answer a few questions. This is about people who have been involved in the migration of workflows from one platform to the next. So, make to n8n or zapier to n8n.

Anyone out there who can help?

I have only basic coding knowledge (C, JavaScript, Python) and rely a lot on vibe coding/AI assistance. Can I realistically build medium-to-complex AI automations and AI agents using n8n?

Hey, I have been using n8n for a couple months now and have built a handful of workflows, currently I am attempting to build an outbound voice agent, so when clients fill out a form on my website they receive a personalized outbound call using the clients first name and key details gathered from the form. I got it to call me but it hangs up instantly and I am now feeling stuck. I am looking for ways to build a personalized voice outbound agent or I am looking for a way to fix my workflow. I DONT KNOW HOW TO PUT MY JSON ON HERE OR ELSE I WOULD IS HOW DESPERATE I AM

- Which automation or solution did you sell?

- How did you approach them?

- What was the process?

- How can anyone like me who has just started, can land their first client?

Something I keep seeing: someone builds a chat workflow, grabs the Chat Trigger's public webhook URL, and pastes it straight into a customer-facing site. It works, so it ships. But that bare URL is now your entire security model.

Things the raw webhook doesn't do for you:

No caller identity. You can't tell one customer's traffic from another's, or either from a bot.

No rate limiting. If the workflow calls an LLM, every request costs you money. Someone running curl in a loop against your webhook is spending your API budget, not theirs.

No origin control. The URL works from any site on earth, and URLs that ship in client-side code always leak eventually. Once it leaks you can't un-leak it without breaking every integration.

No conversation history you can show a user next week or debug against.

To be clear, none of this is n8n's fault. The webhook is a trigger primitive and it's great at that. The mistake is treating a primitive like a product surface.

What I ended up doing is one hop of indirection: browser widget -> small gateway API -> the n8n webhook, which stays server-side and secret. The widget only ever sees the gateway URL plus a public site key. The gateway looks up the tenant by site key, checks the Origin header against domains that tenant registered, rate limits per tenant and per IP, writes both sides of the conversation to a table, then proxies server-to-server.

The site key + origin allowlist combo is the same trust model Stripe uses for publishable keys. It's not secret-based auth, it's "public identifier, verified context". For a widget that has to run in untrusted browsers I think that's the honest option, since anything stronger requires the customer's site to run server code, which kills the copy-paste embed.

Trade-offs I ran into: the extra hop adds latency, you have to handle webhook timeouts yourself instead of letting n8n's hosted chat page deal with it, and streaming responses through the proxy is noticeably more annoying than terminating them directly.

Curious how others handle this. Is everyone just shipping the bare webhook, or do you put something in front of it?

I landed my first paid automation project last week. Client sells football jerseys wholesale and needed qualified retailer contacts across the UK, France, and Italy. He was doing it manually — scrolling Google Maps for hours, missing most opportunities, and had no way to score which shops were actually worth a cold call.

I built him a full qualification pipeline. Here's what shipped:

https://preview.redd.it/2sfatu3g31jh1.png?width=1528&format=png&auto=webp&s=1a96b7b50c3e094c5da1bdb651f9af2764368d40

The stack:

• Apify (Google Maps Scraper) for raw data pull
• n8n for orchestration — 3 parallel branches (GB/FR/IT), Loop Over Items for batching
• OpenRouter +DeepSeek for qualification — reads page_title, meta_description, meta_keywords
• Google Sheets as the destination

The filtering logic:
I call it "Smart Wide." It only blocks the obvious junk — chains, club megastores, pubs, schools, stadiums, malls. Everything else passes to DeepSeek. The AI returns DEFINITELY, LIKELY, MAYBE, or NO with a one-sentence reason.

The result: 23 qualified independent jersey shops in the first 2 days. Client went from manual scrolling to a filtered Sheet he can call down in an afternoon.

One painful lesson: I started with keyword-only filtering. Thought I could catch everything with "vintage football shirts" and "retro jerseys." It was a disaster. Caught printing shops, souvenir stands, and club megastores that happened to mention the right words. I had to rebuild the whole scoring layer with DeepSeek reading the actual page content — title, description, keywords — and returning a real qualification score with reasoning. Night and day difference. Keywords catch mentions. AI catches intent.

I just got a question for the community how do you handle the transition from keyword filtering to AI qualification without burning through API costs on obvious junk? I used a "Smart Wide" pre-filter to block the worst categories before sending anything to DeepSeek, but I'm curious if there's a cleaner way to tier filtering without losing nuance.

I’m building an AI receptionist, and after getting my first client through warm outreach, I’ve started running into challenges that you don’t really see in most YouTube tutorials.

For example:

1. CRM integration during development

The client uses a CRM like MyCRM.finance. They provide a public API, but there’s no trial or sandbox account for us to test with. If we connect our development system directly to their production CRM, our test calls could create fake contacts or interfere with real data.

How do people usually handle this? Mock APIs? A separate staging environment? A dedicated test account from the client?

2. Spam/scam callers

Should we save every caller as a CRM contact, or should the AI first determine whether the caller is a genuine potential customer before creating a contact?

3. Appointment booking

I see two approaches:

• After the call, send the customer a Calendly link by SMS so they can choose a time and enter their own information.
• Let the AI ask for a preferred time, check availability through Calendly, and book the appointment directly.

The second option is more seamless, but what happens if the AI mishears the customer's email address? Do you simply have the AI repeat it back for confirmation, or is sending a booking link generally safer?

4. Concurrent calls

If multiple customers call at the same time, can Retell/Twilio handle them independently? And what do we need to consider on the backend to prevent things like duplicate bookings or race conditions?

I think this is the biggest difference between building a demo and building something production-ready.

YouTube tutorials usually show:
Call → AI talks → collect info → book appointment → done.

But with a real client, you suddenly have to think about CRM safety, spam, data accuracy, API failures, concurrent calls, booking conflicts, etc.

I’d love to hear how experienced builders approach these problems when moving an AI receptionist from demo → production.

If you could automate just one task or process in a small business, what would you choose and why?

Could be anything from lead follow-ups and customer support to admin work or data entry.

Guys, what is the best way to make an n8n instance publicly available ??

What is the most developer friendly way to do it ?? So that i can trigger telegram easily to get and send messages....