Long-term n8n Collaboration Opportunity – Production Workflows

Jobs
1

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/

2 Me gusta
2

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

1 me gusta
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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. franmolina.cu@gmail.com

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 me gusta
12

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 salimoulhaj1@gmail.com or reply here.

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Hi. I can help turn demo n8n workflows into production-ready systems, but I would keep the first step bounded and paid.

For your appointment-booking/LLM workflows, I would start with one hardening slice: webhook/data contract, sub-workflow boundaries, retry/error logging, structured LLM output validation, and a handoff note that your team can reuse across client builds.

My floor for that first slice is $450-$700. After that, per-project pricing can be set by complexity.

One question: which demo flow is closest to being sold to a real client right now?

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Hi,

I’m very interested in the AI Automation Engineer / n8n Voice AI Specialist opportunity.

I specialize in building AI-powered automation systems using:

  • n8n

  • AI agents

  • OpenAI/Gemini integrations

  • APIs & webhooks

  • WhatsApp/SMS workflows

  • Voice automation systems

  • CRM & Google Sheets integrations

  • Multi-step business automations

Over the past few months, I’ve been actively building automation workflows focused on streamlining business operations, client communication, scheduling, and AI-powered workflow orchestration.

Some of the systems I’ve built include:

  • AI assistant workflows

  • Appointment reminder ecosystems

  • WhatsApp & SMS automation systems

  • AI voice and document processing workflows

  • Multi-step follow-up automations

  • Real-time notification systems

I’ve attached workflow snapshots from some of my recent projects, including:

  1. AI-powered Telegram Assistant Workflow

  2. Automated Follow-Up & Reminder Ecosystem

Portfolio:
https://hksautomations.github.io

My focus is on building workflows that are:

  • scalable

  • reliable

  • business-focused

  • easy to maintain

  • optimized for real-world usage

I’m comfortable working remotely, learning new tools quickly, troubleshooting workflows, and building structured automation systems with clean logic and integrations.

I’m currently available and would be happy to discuss how I can contribute to your automation projects.

Looking forward to connecting.

Best regards,
Harvish Shah

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Hi Kishor, this is aligned with the kind of production-readiness work I take on: turning demo n8n/LLM workflows into client-safe systems with clean webhook contracts, structured outputs, retries, error logs, alerting and handoff notes.

For a first collaboration I would keep it narrow and paid:

- review one existing workflow and data flow;

- identify breakpoints, missing retries/logs and unclear payload contracts;

- refactor one critical path into a production-ready version using test data;

- deliver the exported workflow plus a short runbook and next-step backlog.

A practical first milestone would be 300-500 USD depending on workflow size, funded before implementation. If that works, share one representative workflow brief, expected input/output and current failure points, and I can map the exact first milestone.

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i! I specialize in production-grade n8n workflows — error handling, monitoring, documentation, scaling. Looking for exactly this kind of long-term collaboration. Portfolio: rudolf.systems

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Hi Kishor,

Your setup makes sense for me. You handle clients, scoping and sales; I handle production engineering and delivery.

I build n8n and Make automations alongside Python and API services. My larger Make systems have gone past 100 modules because I account for validation, retries, duplicate protection, logging, replay paths and the operational edge cases that show up after launch.

For a first paid workflow, send me a sanitized export, intended inputs and outputs, known failure points and acceptance criteria. I can review it and return a fixed quote, timeline and stabilization plan before implementation.

My usual rate is USD 25-50/hour depending on scope and ownership, or fixed-price after review. Happy to share relevant work samples by DM

.

Terry

18

Hi Kishor, the gap you described is exactly where a workflow stops being a demo and starts needing operating discipline. For a first contained handoff, I would take one existing flow and make the trigger, data contract, duplicate handling, retry boundary, owner alert, and proof test explicit before it goes near a client environment. I work asynchronously and can return a documented fixed-scope plan before any production credentials are involved. My relevant work is here: Michael Gould's Work | Contra. If you are still reviewing builders, send the broad shape of the first workflow and I will tell you whether it is a sensible fit for a contained first engagement.

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Hi Kishor,

Thank you for reaching out. Your vision for building production-ready AI automation systems is exactly the type of work I’m looking for.

I specialize in n8n, AI workflow architecture, LLM integrations, API development, and business process automation. Over the past few years I’ve built automation systems for real businesses, including customer support, appointment scheduling, CRM integrations, WhatsApp automation, and AI assistants. My focus isn’t just making workflows work—it’s making them reliable, scalable, and easy to maintain in production.

From your description, I can help with:

  • Converting prototype workflows into production-grade architectures.
  • Designing modular workflows with reusable sub-workflows.
  • Implementing robust error handling, retries, logging, and monitoring.
  • Building reliable webhook and API integrations with clean data contracts.
  • Optimizing LLM pipelines for structured outputs, validation, and consistency.
  • Improving performance, maintainability, and scalability for client deployments.

I also enjoy working closely with agencies where responsibilities are clearly divided—you focus on sales and client relationships while I focus on the technical architecture and delivery. I believe this is an effective way to scale.

I’d be happy to review one of your existing workflows, identify opportunities for improvement, and discuss how we can build a long-term partnership. I’m confident I can add value not only by developing workflows but also by helping establish technical standards that support future growth.

Looking forward to hearing from you and seeing your first workflow.

Best regards,

Nathan Torres
AI Automation Engineer | n8n Specialist
LLM Integrations • API Development • Workflow Automation • Production Systems

20

“Production” is doing a lot of work in that title, so let me say what it has meant for me in practice.

I run 31 n8n workflows in production for real businesses, self-hosted, with Postgres for state. Three things I only learned by breaking them:

An HTTP node runs once per item. I once turned a single file download into 744 requests and got the API to answer 403 to everything downstream. executeOnce exists for a reason, and nothing in the editor warns you.

Every webhook needs idempotency. Providers re-send the same event when you answer slowly, so without a unique event ID you process the same thing twice and your reports quietly lie about volume.

Something has to watch the watchers. I have one workflow whose only job is to check every 15 minutes that the other thirty ran, and to alert me before the client notices. Nothing changed how clients perceive the work as much as that one did.

The fourth, which matters most for agency work: business rules belong in code, not in the prompt. An AI agent of mine once offered a discount that did not exist because the price list lived in the system message. Now a validation node builds every figure before the model sees it, and the model only writes the sentence around it.

The split you describe, you on sales and client relationships, me on architecture and delivery, is exactly how I prefer to work. I would rather own the technical standard than the client call.

Happy to take one of your existing workflows and come back with what I would change and why, before either of us commits to anything.

Stack: n8n self-hosted, OpenAI and Claude APIs, Postgres and Supabase, WhatsApp Cloud API and Evolution, REST integrations, JavaScript, PHP, Python. Based in Portugal, working in English, Portuguese and Spanish.

What I have not done, so it does not surprise you later: no custom n8n nodes, no React or Vue front ends, no native mobile apps.

21

On “consistent structured outputs, not just chatbot flows” – the failure mode that actually bites in production isn’t the model refusing to follow a schema, it’s the model following the schema while quietly making up a value that fits it. A JSON parse can succeed and still be wrong.

What holds up: validate structure and content separately. Structural validation (right types, required fields present) catches the obvious breaks. Content validation is a second, deterministic pass against your actual data – if the model returns a customer ID, confirm it exists in your system; if it returns a price or slot, confirm it against the real price list or calendar rather than trusting the model’s arithmetic. For booking/appointment flows specifically, business rules and prices should live in a lookup the workflow builds before the model ever sees the request – the model only writes the sentence around the number, it doesn’t calculate it.

Second point, since you mentioned production-ready: before pushing a workflow change live, replay it once against a small fixed set of saved past inputs (a handful of real webhook payloads) and diff the output against the last known-good run. Most regressions in n8n workflows aren’t caused by the AI step – they’re an upstream node change shifting a field name that only breaks something three steps later. A two-minute replay against saved payloads catches that before a client does.

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