- Research support: tools, data pipelines, prototypes and document or corpus analysis
- Technology support: architecture, code review, turning a prototype into a reliable tool
- Course sessions, practical workshops and final-year projects on realistic cases
- Scope, data, confidentiality and ownership agreed with the institution before starting
Practical support for research teams
A lab or research team often has a clear question but little time to build the tool that would answer it. We handle the software side, while the researchers keep control of the question, the method and the interpretation.
The goal is processing the team can rerun, understand and describe in its work, not a one-off demonstration.
- Custom tools to collect, clean or structure data
- Reproducible data pipelines, documented and easy to rerun
- AI-assisted analysis of documents or corpora, with results checked on a sample
- Prototypes to test a hypothesis or method before investing further
- Small interfaces to browse, annotate or share results within the team
Technical backing for existing projects
Some projects already have code: a thesis script that became essential, a prototype others now need, a tool nobody dares to change. We help make it solid without rebuilding everything.
This support can cover architecture, code review, tests, documentation or turning a prototype into a tool that lasts. What is kept, secured or rewritten is decided with the team.
Students who are up to date when they graduate
AI tools are changing how companies work faster than curricula can follow. The aim is for students to know how teams really use these tools today, so they are not lost in their first job.
Possible formats are defined with the teaching team, based on the course objectives and the students’ level. There is no fixed duration or ready-made programme.
| Possible format | For whom | What remains after |
|---|---|---|
| Session within a course | Students of an existing course, with the lecturer | Examples and material the teaching team can reuse |
| Practical workshop | A group of students or PhD candidates | A working method and a verification checklist |
| Final-year project | A student or small group, with an academic supervisor | A documented project on a realistic case |
| Session for young graduates | Recent graduates who want to stay current | Reference points on current tools and practices |
What students practise
We show the tools and methods companies use today, on concrete cases. The priority stays the same: knowing how to check what an AI produces before relying on it.
- AI assistants for drafting, summarising, searching or analysing, and their limits
- AI agents: what they can do on their own, and where human control is needed
- Automating repetitive tasks between everyday tools
- AI-assisted development: reading, testing and understanding generated code
- Checking an answer against the source, spotting an invented claim or false precision
- Deciding which data may go into a tool, and which may not
How we work with an institution
Each collaboration starts with a short written framework that can be checked. The institution’s rules come first; we adapt to them rather than propose our own.
- A named contact person on the university or lab side
- Scope and deliverables written down before starting
- Rules on research data, personal data and confidentiality set at the outset
- Ethics committee or data protection requirements: decided by the institution
- Ownership of code and results defined per project