Public challenge · try it online
Who supervises the AI?
Six situations. You choose how to verify what the AI hands you.
≈ 4 minutes · no score · neither a validated test nor a Hulotech solution.
Try the challenge →
ALL IN trade show · Montréal · September 16–17, 2026
Hulotech at ALL IN 2026
ALL IN trade show, Montréal, September 16–17, 2026.
Presented by its organizers as Canada's largest AI and tech event. Hulotech takes part to show its work on AI-assisted training under human supervision, and to open conversations.
The public challenge and the interactive demo are already available online: try the challenge →
Illustrative image.
Why Hulotech will be at ALL IN
When AI takes part in the work, what do we know about what a person understands, can verify and can mobilize again?
Hulotech explores how to move from a one-off performance to evidence that accumulates. At the show, we come to present what we observe, what we cannot conclude, and to meet organizations interested in AI-assisted training under human supervision.
Our founder works every day with AI agents able to carry out certain technical tasks well beyond his direct expertise. His challenge is not to become better than them, but to know when to trust them, what evidence to ask for and when to escalate.
Established concepts that Hulotech did not invent: calibrated trust, meaningful human oversight, automation bias, the asymmetry between solving and verifying.
What we will present at the show
Three demonstrations.
Two experiences you can try online right now, and a demonstrator presented by the team.
Interactive demo · open it online
Output is not capability.
View the interactive demo →
New tab · large screen recommended
Demonstrator · presented by the team
Capability Evidence: an evidence record, not a score.
Diagnostic → gap → targeted practice → retest → evidence record. A real demonstrator, presented by the Hulotech team. No AI evaluates capability.
See the real state of each component ↓After the challenge
Are six good answers enough to say that you know how to supervise AI? No.
These six decisions show how you reacted in these situations. They do not prove that you are, or are not, a good supervisor of AI.
A good answer on a test is not necessarily a demonstrated capability. A capability observed in one situation is not automatically demonstrated in another.
- Good answer≠
- Good test performance≠
- Transfer observed≠
- Capability across contexts≠
- Real-world capability
Confidence ≠ competence · AI-assisted performance ≠ independent capability · Second AI opinion ≠ independent evidence
This is precisely the problem Hulotech explores.
What this says about our broader work
Four components, one logic.
The challenge and the demonstrator are the visible parts of broader research on learning, evidence state and follow-up over time.
- 0
Starting point
A real objective, a real person
- Initial behaviour · exploration
01 · Person-centred training
Student-Based Training
« What should this person practise next? »
- Working demonstrator
02 · Evidence of capability
Capability Evidence
« What have we actually observed? »
- Existing foundation · not yet connected
03 · Memory of evidence over time
Cognitive Passport
« What do we know over time? »
- Research direction
04 · Next learning experience
Next Best Learning Experience
« What should happen next? »
Real maturity level
Where each component really stands.
We know something. We do not claim to know more.
Capability Evidence
Working demonstratorA real demonstrator exists. It shows approximately: diagnostic → gap → targeted practice → retest → capability evidence record.
No AI evaluates capability. The system currently relies on heuristic formative signals, human adjudication and an evidence workflow.
The retest result was observed after practice, in this situation. Transfer remains undemonstrated.
Student-Based Training
Initial behaviour / explorationAn elementary logic already exists: diagnostic → gap adjudicated by a person → prescribed practice → retest.
It is not a complete adaptive engine.
Cognitive Passport
Existing foundation — not yet connectedTechnical foundations and models exist.
There is no complete longitudinal engine, no fully connected writing, and no complete operational product.
Next Best Learning Experience
Research directionNothing is implemented.
The question is asked: what should we observe next to reduce uncertainty?
We can show a public challenge that makes the problem tangible, then an interactive demo that explains how Hulotech explores an answer based on learning, evidence state and longitudinal follow-up, with a real demonstrator mostly on the Capability Evidence side.
Meet us at the show
Will you be at ALL IN?
We show what we observe and what we cannot conclude. Write to us to arrange a time on site.
Let's talk about
- AI-assisted training
- Human supervision
- Modernizing learning
- Capability Evidence
- AI projects applied to training