Long-term n8n Collaboration Opportunity – Production Workflows

Jobs
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I run an AI automation agency based in India, and we are currently building and selling AI-powered appointment booking and workflow automation systems (primarily using n8n, Cal.com, and LLM integrations).

Right now, we already have working demo workflows, but we need an expert like you to help us convert these into production-ready systems.

Specifically, I’m looking for support with:

· Converting demo workflows into scalable, production-grade architectures

· Implementing proper error handling, retries, and logging

· Structuring multi-step workflows and sub-workflows

· Ensuring webhook reliability and clean data contracts

· Optimizing LLM usage for consistent structured outputs (not just chatbot flows)

· Making systems stable enough for real client deployment

My goal is not just one-off work. I am looking to build a long-term collaboration, where:

· You handle the technical architecture and production readiness

· I handle client acquisition, sales, and project flow

· We scale together as more clients come in

To start, I’d like to work with you on 1–2 workflows and then continue on a per-project basis as we grow.

If this sounds aligned, I’d love to:

· Share one of our current workflows

· Get your thoughts on improving it

· Discuss how we can work together long-term

Looking forward to hearing from you.

Best regards,
Kishor Nayak
SEO Labs India
https://seolabsindia.com/

1 Like
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Hey @SEOLabs ,

This is exactly the kind of work I specialize in.

I’ve built and deployed multiple production-grade n8n systems — not just demos — including AI appointment setters, voice agents, and multi-step automation pipelines used by real clients.

From what you described, the gaps are very clear:

  • Demo workflows breaking under real usage

  • Weak error handling / retries

  • Unstructured LLM outputs

  • Fragile webhook + data flow design

That’s where I come in.

I focus specifically on:

  • Converting demo workflows into scalable, modular architectures

  • Implementing robust error handling, retries, and logging

  • Designing clean webhook + data contracts

  • Structuring LLM outputs into reliable, production-safe formats

  • Building systems that don’t just work once — but run consistently for clients

Here are a few relevant builds:

  • AI Appointment Setter (n8n + WhatsApp + Calendar)

  • Voice AI Receptionist (real-time booking + API integrations)

  • End-to-end lead generation & onboarding automations

Portfolio (with demos):
https://muhammad-ai-automations.notion.site/Muhammad-Bin-Zohaib-AI-Automation-Projects-29da292a241380f889c2e337a134c010

If you share one of your current workflows, I’ll break down exactly what’s wrong and how to make it production-ready.

If that aligns, we can turn this into a long-term execution pipeline.

– Muhammad

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@SEOLabs sounds like there could be a natural fit here. we’re Noyra-X, an AI automation company from Germany — we build and operate production-grade n8n + Claude systems for SME clients, GDPR-compliant on european infrastructure.

your setup (you handle client side, partner handles technical architecture and production readiness) is exactly the model that makes sense for scaling this kind of work. the challenges you listed — webhook reliability, clean data contracts, structured LLM outputs — are things we work on daily.

rather than going back and forth in the thread, probably worth a short call to see if there’s actual alignment. happy to take a look at one of your demo workflows beforehand if you want to share it.

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Hi Kishor,

I’ve reviewed your request, and this is exactly what I do. Converting a “working demo” into a “production-ready system” is where most agencies fail, and I’m glad you’re prioritizing this before scaling.

I live in n8n, Docker, and Python, and I’ve built dozens of high-load systems where “it just works” is the main requirement.

Here is how I will help you stabilize your workflows:

  • Reliability: Implementing Global Error Triggers, per-node retries, and dead-letter queues. A failed API call should never kill the whole process.

  • Architecture: Moving from messy “all-in-one” flows to modular Sub-workflows. This makes them easier to debug, test, and reuse across different clients.

  • Data Integrity: Enforcing strict data contracts between nodes and using PostgreSQL as a state machine so we can recover any flow if it breaks.

  • LLM Consistency: Moving away from conversational “chat” flows to Structured Outputs (JSON mode/Function calling) to ensure the LLM drives the workflow reliably every time.

  • Webhooks: Setting up proper validation and response headers to ensure Cal.com and other triggers are acknowledged instantly.

I’m looking for a long-term partner who handles the business side while I ensure the tech is bulletproof.

Send over your current workflow (JSON or screenshot), and I’ll give you my initial thoughts on how to “harden” it for a real client deployment.

Portfolio: https://mikedevai.netlify.app/ Telegram: @hely_chatbots WhatsApp: +375293761570

Best regards, Mihail Rogal Automation Architect

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Hi Mihail I have sent a whatsapp message to you. My whatsapp number: 9845837171

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Hello @SEOLabs

I made this video specifically for you: Long-term n8n Collaboration Opportunity – Production Workflows - Kishor | Loom

I’ve been building n8n workflows for 6 and 7-figure businesses for 2 years and the video shows exactly a few of them.

Here you´ve my portfolio as well: Fran´s Portfolio - Google Präsentationen

Shoot me a message and let’s get started. [email protected]

Fran

P.S.: The video is 3 minutes. Worth it.

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Hello Kishor,
I have DM’ed you with relevant details. Looking forward.

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Production-hardening n8n workflows is our daily work — we maintain 86+ workflows with error handling, retry logic, sub-workflows, and Telegram/Sentry monitoring.

Our production patterns include exponential backoff, circuit breakers, split-in-batches for large datasets, dead letter queues for failed items, and health check crons that ping every 5 minutes with alerts.

Cal.com is familiar territory — we’ve built scheduling/booking integrations that feed into CRM pipelines. LLM integration is a core competency: Claude API with structured outputs (JSON schema enforcement), token optimization, and fallback chains.

We run everything on self-hosted infrastructure with proper logging, webhook signature verification, and clean data contracts between sub-workflows.

Happy to start with 1-2 workflows as a trial. Portfolio: flipfactory (dot) it (dot) com

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Thanks Everyone for your offers. We have got our N8N Expert. This post is closed. Thanks

1 Like
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This sounds like a great fit. I specialize in exactly this — taking n8n demo workflows and hardening them for production: error handling, retry logic, structured logging, webhook validation, and scalable sub-workflow patterns.

I work with Cal.com integrations and LLM-based appointment booking flows regularly. Happy to review what you have and give you a clear breakdown of what needs to change to make it production-grade.

Would you be open to a quick call to walk through the existing workflows? Email me at [email protected] or reply here.