Most people offering “AI automation” picked up n8n a few months ago. I spent six years as a software engineer at Amazon and AWS before I built my first workflow, shipping systems that carried real production traffic and had a pager attached to them. I still build the automation in n8n. I just also know what happens underneath it when something goes wrong.
What I actually build:
🛠️ All your tools connected in your workflows, the agents don’t guess, they use what they need
🤖 Multi-agent workflows in n8n, with RAG over your own documents so answers come from your data, not a guess
📥 Document automation: inbound email triggers, classification, extraction, summarization, built for a law firm managing cases across multiple jurisdictions
🎭 Prompting that adapts to the person using it instead of one rigid script for everyone
🧱 The infrastructure underneath the automation: queues that preserve order under load, isolated processing for sensitive data, systems that scale without someone babysitting them
Why it holds up in production:
🏗️ Six years at Amazon and AWS: Lambda, DynamoDB, Cloud Run, CI/CD, canary deployments, I can build infrastructure in AWS or GCP
🔒 Experience handling legally sensitive documents under strict data-processing boundaries
📈 Built a RAG assistant for regional energy community administrators that expanded from one region to three provinces without rewriting the core system
⚡ Nobody asked me to build my most-used AWS project. I noticed a gap, built it anyway, and it became one of the most-used parts of a platform 3,000 Solution Architects relied on daily
Based in Brussels, comfortable working into US hours. If you want automation that still works in six months, let’s talk.
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A law firm handling cases across national and international jurisdictions was drowning in manual admin. Someone had to read every incoming email, work out which case it belonged to, and file the attachments by hand before anyone could act on them. I built the system that does that automatically, end to end, with the level of care legal documents actually require.
How it works:
📧 Inbound email is triggered and automatically classified by case, no manual sorting
📎 Attachments are matched to the correct case file automatically, even across parallel active cases
🧾 Documents are extracted and summarized so the team gets the gist without opening every file
🔐 Highly sensitive material is processed under restricted boundaries: strict limits on what reaches third-party services, agreed with the firm before a single document moved
Built to actually hold up:
🧠 n8n orchestrates the entire pipeline, from the moment an email lands to the moment a summarized case file is ready
🧩 Custom logic beyond what n8n’s built-in nodes cover (classification rules, case-matching) written in TypeScript
🗂️ Supabase (PostgreSQL) for structured case data
☁️ Cloud Run functions, written in Python, handle extraction from encrypted files, format conversion, and information extraction
🔁 A FIFO queue guarantees per-client message ordering, so a burst of emails from one client doesn’t get processed out of sequence
📈 The same queue absorbs peak load without over-provisioning infrastructure the rest of the time
Orchestrated end to end in n8n. Built in close coordination with the firm to get the confidentiality boundaries right before the automation touched a real case.
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