OO.

SOFTWARE · DATA · APPLIED AI

Projects across
software & AI.

A collection of work in software, data, and applied AI—focused on practical problems and systems people can inspect and use.

Browse projects Find me on GitHub
PROJECT FLOW / 001PROBLEM → BUILD → REVIEW
01 / DEFINEUnderstand itProblem & context
02 / BUILDTry an approachTools & methods
03 / REVIEWCheck the resultTests & evaluation
SOFTWARE✳DATA · AI
PORTFOLIO / 2026INDEPENDENT PROJECTS & EXPERIMENTS

01 — PROJECTS

A document assistant
with more than one source.

A hands-on project for learning how an assistant can use company documents and structured business data together.

FEATURED PROJECTGENAI / MLOPS

Company Knowledge
Assistant

Ask a question in natural language. The assistant can retrieve document passages, query a read-only analytics dataset, or use focused tools. I built it to explore these pieces and make the results inspectable.

Hybrid RAGTool callingEvaluationObservability
Open live demo More on GitHub

NOTE BrightBlocks Toys is fictional. Its documents and retail figures are synthetic demo data.

QQUESTION01

Which product led net sales in 2025?

AGROUNDED ANSWER02

Harbor Rescue Kit led with €79,066.80.

↳ Read-only SQL query · 3 rows returned
↳ Synthetic 2025 retail dataset
RETRIEVAL EXPERIMENTHybrid search
1.00 hit@5

On a small synthetic evaluation set. A useful baseline, not a claim of production-scale performance.

01

Two kinds of knowledge

Document retrieval handles policies, supplier records, product information, and quality reports. A separate read-only SQL tool answers questions over synthetic sales data.

02

Evidence is visible

Answers include citations or a record of the tool used, so a person can inspect how the assistant reached its response.

03

Quality is measured

Retrieval strategies can be compared against labeled questions. Application tests run in CI before deployment; MLflow records evaluation runs.

02 — FOCUS

Useful work,
clearly explained.

I’m interested in projects that connect a clear user need with thoughtful implementation, useful data, and results that can be checked.

01
Start with the problem

Understand the task and context before choosing a technical approach.

02
Make behavior measurable

Use tests and evaluation to see where a system works and where it needs improvement.

03
Explain the choices

Make the design, sources, and tradeoffs understandable to the people using the result.

TOOLS IN THIS PROJECT

Technologies used in the featured project.

PythonFastAPIReactPostgreSQLChromaMLflowDockerGitHub Actions