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AI engineering

AI that answers with sources, and knows when to stay silent

I build AI systems for production: assistants that answer with citations, say honestly what they don't know, and only act where a person has approved it — with evaluation guarding every change.

At Politie Noord-Holland I applied AI within criminal law, an environment where privacy and information security are not an afterthought. Most AI projects fail on the data, not the model; so I build the foundation first and the assistant second.

Approach

What a team can expect from me

01

Sources behind every claim

An answer without a retrieved passage is no answer. Every claim points to the document or ruling it came from.

02

Guardrails before the model

Prompt injection, PII and off-topic questions are caught in plain code before a single token is spent. Fail closed, never crash.

03

Human in the loop

Actions with consequences — sending a message, changing something — wait for approval. A hard daily cost limit keeps the bill predictable.

04

Evaluation on every change

Labelled datasets and thresholds in CI: retrieval recall, correct tool selection, task completion. A regression blocks the merge.

Experience

Where I did this

2024

Data Analyst

Politie Noord-Holland

Applied AI and automation within criminal law — where confidential and privacy-sensitive data is the norm and every outcome has to be traceable.

2023

Data Engineer

Netherlands Aerospace Centre (NLR)

Built the data foundation AI applications have to stand on: clean, modelled and tested data in a research environment.

Since 2020

Instructor

Udemy

Explaining complex technology to people without a technical background, 241 students with a 4.2 rating — including what AI can and cannot do.

Works with
LLMs
RAG
AI agents
LangGraph
LangChain
Retrieval evaluation
Guardrails
pgvector
LangFuse
Python
FastAPI
Document processing
Streaming (SSE)
Principles
  • No source, no answer.

  • Guards in code, not in the prompt.

  • Risky actions only with human approval.

Projects

Projects that show it

Two own AI systems running in production — with guardrails, evaluation and the code in the open.

Own project

Digital twin: an AI agent that answers for me — with sources

A LangGraph agent over my CV, projects and writing that answers visitors' questions in a streaming chat: every claim cited, refusal when there is no source, a hard daily cost cap, and human approval before it acts on my behalf.

GenAI
Agents
LLM
Python
Own project

Strafrecht-RAG: no source, no answer

RAG over 1,906 Dutch criminal-law rulings with one rule: no source, no answer. Every claim ends in an ECLI, section and paragraph — and that reference is checked in code, not requested from the model.

GenAI
RAG
Python
LLM

AI you'd dare to show a client?

I'm looking for a team where I can do this work properly. A conversation is easily arranged.