Omschrijving

Most "automate this for us" conversations start with the wrong question. The real one is: which process is actually worth automating, and which isn't?

I run a fixed-price, 2-3 day AI Process Diagnostic: map the process AS-IS, find the one costing the most time or money, and hand back a TO-BE flow with a clear yes/no on whether AI is the right tool for it. No discovery-phase invoice creep.

Agentic RAG tool (LangGraph + Claude + ChromaDB, multilingual) that runs full EU AI Act compliance audits end-to-end: risk classification, GPAI detection, gap assessment, client-ready PDF report, fully local for data privacy. Also shipped UrbanScore (Le Wagon): image-based property search + price prediction with LLMs and HuggingFace embeddings.

When a Process Diagnostic surfaces a regulated AI system, I can run that same compliance gap-check as an add-on — not a separate practice, just the natural next question once you've mapped how a process actually works.

Why this combination holds up:

→ European Commission (ATOS/DIGIT), 2.5 years: designed the RBAC and approval-workflow architecture for an EU-scale ServiceNow migration — who can request, what triggers automatically, what escalates. The governance question agentic AI raises today, solved at EU scale before the term existed.

→ BNP Paribas: GDPR data governance on high-security banking platforms — data mapping, risk classification, stakeholder alignment.

→ 8+ years as Technical Business Analyst / Scrum Master / Product Owner: stakeholder workshops, AS-IS mapping, documentation, turning findings into backlog items a team can actually execute.

Taking on AI Process Diagnostic engagements now. What's the one process quietly costing your team the most time? Let's find out if it's worth automating.

Python · LangGraph · Claude/Anthropic API · RAG · ChromaDB · sentence-transformers · SQL · Scikit-learn · TensorFlow/Keras · PyTorch · HuggingFace · Prompt Engineering · MLflow · Docker · GCP · Streamlit · ServiceNow

Talen

Engels
Tweetalig / moedertaal
Spaans
Tweetalig / moedertaal
Nederlands
Beperkte professionele capaciteit
Portugees
Vloeiend

Voorkeuren voor werkplek

Kan op locatie werken

Antwerp (tot 50km)

Le Wagon
Data Science, Machine Learning and AI
januari 2026 - maart 2026 (2 maanden)
Tokyo, Japan
Bootcamp completed March 2026 — fully dedicated to my AI consultancy practice from April 2026. Data Science and AI Bootcamp — Science, Machine Learning & AI ▸ Python and its data libraries (Pandas, Numpy, Matplotlib) ▸ SQL ▸ Data ETL ▸ Data visualization ▸ Machine learning modelling (Scikit-learn) ▸ Deep learning modeling (Tensorflow-Keras) ▸ CI/CD/CT (Git, GitHub, MLflow) ▸ MLOps (Docker, Google Cloud Platform) ▸ Generative AI Application and Ethics
artificial intelligence Machine learning Data science
KRS Solutions
Founder & AI Business Consultant
juni 2023 - Vandaag (3 jaren en 1 maand)
Antwerp, Belgium
risk classification, stakeholder alignment.→ 8+ years as Technical Business Analyst / Scrum Master / Product Owner: stakeholder workshops, AS-IS mapping, documentation, turning findings into backlog items a team can actually execute.Building this through KRS Solutions, my consultancy based in Antwerp, serving Belgium & the EU. Taking on AI Process Diagnostic engagements now.What's the one process quietly costing your team the most time? Let's find out if it's worth automating.Stack: Python
• · LangGraph
◦ · Claude/Anthropic API
• · RAG
• · ChromaDB
• · sentence-transformers
◦ · SQL
◦ · Scikit-learn
◦ ·

I build the AI myself rather than just advising on it — solo-built an agentic RAG tool (LangGraph, Claude, ChromaDB) that runs full EU AI Act compliance audits end-to-end (full writeup in Projects below). When a diagnostic surfaces a regulated AI system, that same tool powers an optional compliance gap check inside the engagement.
Servicenow
Business Process Analyst - HAM (Consultant)
mei 2023 - oktober 2025 (2 jaren en 5 maanden)
Brussels, Belgium
Led two AI-driven digital transformation projects for the European Commission logistics department (ATOS/DIGIT). PROJECT 1 — Asset Tracking App (built from scratch) ▸ Stakeholder discovery across warehouse teams, IT infrastructure and suppliers ▸ Designed app mockups (Balsamiq), features, user roadmap and data architecture across multiple disconnected databases ▸ Automated asset credentialing via QR scanning app — credentials assigned automatically upon each device scan ▸ Validated REST API integrations using Swagger, JSON, Postman ▸ UX/UI prototyping: Balsamiq → Adobe XD → Figma PROJECT 2 — (EU scale) ▸ Mapped AS-IS workflows and redesigned TO-BE processes across 5 business cases and 11 sub-cases
▸ Defined roles, access rights and governance rules across a highly complex institution ▸ Designed RBAC including a management tool giving team leads real-time visibility of hardware/software with approval workflows ▸ Automated full hardware lifecycle: reception → storage → configuration → assignment → delivery ▸ Managed warehouse layout: pallet organisation, supplier reception and delivery sequencing ▸ Designed pre-delivery software installation workflow with automated expiry tracking ▸ Automated software license expiry alerts (SAM) — preventing service interruption for thousands of EC users ▸ Managed simultaneous onboarding for 400+ new users Result: two systems replacing manual processes — structured, automated and AI-ready for predictive analytics at EU scale.