n8n consulting and implementation: agent-built workflows, AI agents and a team that builds on its own.
I build and maintain automations on n8n: system-to-system integrations, document handling, AI agents where a judgement call is needed. The workflows are built by AI agents I equipped myself through n8n-mcp. I am accountable for them as for my own code, and I teach your team to do the same.
- 5.0 / 5certified n8n partner · reviews in the official n8n expert directory
- 143,000+users of n8n-mcp, the tool AI agents use to build n8n workflows
- weeklydata on how AI agents build n8n: the n8n AI Automation Index
Automation in most companies stops at the same place: the simple flows work, and the costly ones need a judgement call, an exception or a document no form can handle. My work starts at that line.
I am not an agency. I built n8n-mcp, the tool that lets AI agents build working n8n workflows, and more than 143,000 people use it today. I work the same way for clients: agents build, I design, review and own the result. One person delivers the scope an agency needs a team for, and the knowledge of how it works stays inside your company.
What I do
n8n implementation on your server
Self-hosted n8n on the client's infrastructure: installation, queue mode, backups, team access. Data stays inside the company and the cost does not grow with the number of executions.
System-to-system integrations
CRM, ERP, e-commerce, marketplaces, spreadsheets, supplier APIs and systems with no public API. I connect tools that were never designed to talk to each other and keep those connections working when the other side changes.
AI agents inside the process
Where the process needs judgement: lead qualification, ticket classification, routing, reading a document. The agent gets a clear decision scope and a point where it hands over to a person.
Documents and back office
Quotes, invoices, orders, tickets: many suppliers, many inboxes, many formats reduced to one register with duplicate and exception control.
Audit and maintenance of an existing instance
Security review of webhooks and credentials, error handling in every workflow, naming and folder hygiene, version upgrades. Office hours where I review the team's work.
Microservices where low-code runs out
Own APIs, scrapers, document converters, apps with a front end and n8n as the backend. Written when the ready-made block does not exist or would become the bottleneck.
First stage: n8n on your server and one working integration
Instead of a proposal covering ten processes, one fixed-price stage. Afterwards we both know whether we want to continue, and you have a running instance and a first process in production.
- Self-hosted n8n on your server, with backups and team access
- One trial integration chosen together and brought to production
- Error handling and alerts from the first workflow
- A recommendation of what to automate next, and in what order
Who it is for
- Companies where several people spend a measurable part of their week re-typing data between systems.
- Teams that already run n8n or another iPaaS and have hit its ceiling.
- Organisations whose automation has to handle documents, not only structured data.
- Companies that want the team to build on its own instead of depending on a vendor.
How I run it
- 01Process mapping
I sit with the people who run the process today and write it down step by step, including the exceptions nobody remembers until they happen.
- 02Agents build, I am accountable
Workflows are built with AI agents working through n8n-mcp, with every change validated. I design the architecture, review the result and take responsibility for what reaches production.
- 03First flow in production
I start with one well-chosen process and take it all the way. A working flow in production is a better argument than a plan covering ten.
- 04Extending and handing over
Further processes are added on the same infrastructure, with the same monitoring and error handling. The team gets documentation, access and weekly sessions where it learns to build on its own.
A team that builds on its own
Training is not a separate product. It is part of every longer engagement: a weekly one-hour session with the people who automate inside their own departments. At an e-commerce client I have run it since May 2026; the team comes from finance, warehouse and sales, not from engineering.
- One topic per session and a hands-on exercise on each participant's own instance: error handling, sub-workflow contracts, Data Tables, webhook and credential security, agents with tools.
- Blockers come first: whoever brings a real problem gets it solved in the session, and the plan moves a week.
- A one-page note after every session, so a new team member never needs a live repeat.
- Results after a dozen sessions: a warehouse team member built their own app for expiry-date stocktaking, sales has automated reporting on commercial decisions, every production workflow has an error workflow with alerts, and the team runs its own agents on Telegram.
Implementations
From single flows to platforms where the client now runs more than two hundred automations with its own team.
Over 200 workflows on one platform — marketplace automation across 6 countries, agentic product research, custom microservices and an internal automation team trained to work independently.
Read the case studyFor a CFO-as-a-Service firm: an agentic content engine publishing one article a day, and a Coda-based finance operating system — a counterparty registry, an income-and-expense ledger fed straight from invoices, and a hub that shows who needs paying and how much.
Read the case studyCost invoices arriving across seven team inboxes are found, read, de-duplicated and filed automatically — into a Google Drive archive and a Google Sheets register that's ready to hand to the accountant. Twelve connected n8n automations.
Read the case studyAI reads broker offerings from emails and flyers (vision), fuzzy-matches them against a property database, enriches them with neighbourhood data and generates Letters of Intent in one click.
Read the case studyFrom a file in a folder to a live ad — AI analysis of images and videos, French copy in three positioned variants, standardised naming and automated upload to Meta Ads.
Read the case studyGuides
How the n8n AI Agent works, where to host it, and how to give agents the context they cannot guess.
What is the n8n AI Agent and how does it work?
The n8n AI Agent is a node in which a language model receives instructions (a system message) and a set of tools, then decides on its own which tools to call to complete a task. Without tools it is just a chat. With tools, most often sub-workflows, the agent can read a document, check data in a spreadsheet and make a judgement call no plain rule can handle. Since August 2026 n8n also has a separate Agents category: agents with their own memory, channels and skills that live outside a single workflow.
Read the guiden8n self-hosted or cloud: what does it cost and which should you choose?
n8n Cloud starts at €20 a month for 2,500 workflow executions, and the price grows with executions. Self-hosted n8n in the free Community Edition has no execution limit, costs what the server and maintenance time cost, and keeps data inside the company. For companies handling documents, personal data or larger volumes I recommend your own server. Cloud makes sense at the start, when nobody in the company wants to run a server.
Read the guideHow do you install n8n-skills, and why does an AI agent need skills for n8n?
n8n-skills is a set of 14 skills plus a skill router and a hooks layer that teach an AI agent the right patterns for building n8n workflows through the n8n-mcp server. You install it with one command in Claude Code, as zip files in Claude.ai and ChatGPT, or as a plugin in Codex. An agent without skills guesses at syntax and puts instructions in the wrong place; an agent with skills sticks to proven patterns and conventions.
Read the guideWhat the data says about AI-built workflows
Every week I publish figures from n8n-mcp telemetry. Three of them explain why workflows built by agents need review, error handling and a verified setup before they reach production.
Frequently asked questions
- How do I choose an n8n expert or consultant?
- Check three things: whether they are listed in the official n8n expert directory with client reviews, whether they show implementations running in production rather than demos, and whether they can leave your team self-sufficient. I am a certified n8n partner rated 5.0 out of 5 in the directory, the creator of n8n-mcp, and my clients now grow their automations with their own teams.
- How much does an n8n implementation cost?
- The first stage costs €1,000 to €2,000 and covers self-hosted n8n on your server plus one integration brought to production. Further work is priced stage by stage, per process, based on what already runs. Self-hosted n8n itself has no per-execution fees; you pay for the server and for the actual AI model calls.
- n8n cloud or self-hosted?
- For companies handling personal data, documents or larger volumes I recommend self-hosting: data stays inside the company and the cost does not grow with executions. n8n cloud makes sense at the start when nobody wants to run a server. Setting up your own server is part of the first stage.
- Why n8n rather than Make or Zapier?
- n8n can be self-hosted, so data never leaves the company's infrastructure, and there is no cap on operations that wrecks the economics at higher volumes. It also lets you write your own code where the ready-made blocks end. If Make is enough for you, I will say so.
- Why not an agency?
- Because you get what an agency delivers without its overhead and without diluted accountability. The workflows are built by AI agents I equipped myself through n8n-mcp, and every change is checked by validation and by me. One person knows the whole system from the server to the agent prompt, and by the end of the engagement so does your team.
- Does automation have to use AI?
- No, and often it should not. A model is needed where something has to be understood or judged. Where a rule is enough, a rule is cheaper, faster and predictable.
- What happens when an automation breaks?
- Every flow handles errors on three levels: retries for transient failures of external services, a separate path for expected errors, and an error workflow with a chat alert for the unexpected ones. Failure is loud, not silent. Critical processes also keep a manual fallback.
- Will the team cope without you?
- That is the goal. The weekly team sessions are part of the engagement precisely so the company does not end up depending on me. At some clients the team now builds most automations itself, including its own apps and agents.
