Learning intelligence
Models that can tell what someone already knows, what they're ready for next, and when they're about to forget it. We treat mastery and memory as things you can measure.
We research and build learning technology at the frontier of AI, so that anyone, anywhere, can learn from the best teacher there has ever been.
intelligence that teaches, and to put it in everyone's hands, for the benefit of society.A great teacher, for every person who wants one.
Most people never get a great teacher. The ones who do usually remember that teacher's name for the rest of their lives. The gap between the few who are taught well and everyone else is the problem Nucleus Labs was started to work on.
Models can now read, reason, speak, and make video well enough to teach. We take the best of them, test them against what learners actually remember a week later, and build them into tools a school or a company can start using on Monday morning.
We're a small lab based in Vancouver, Canada. We ship products, publish what we learn, and work with every serious model maker, because the learner deserves the best one available.

Our research sits close to the limit of what models can do. Tutors that reason across a whole course, voice and video instruction in real time, simulations generated on demand, and agents that coach someone through the actual task.

Before anything reaches a learner, we check it for accuracy, fairness, privacy, and its effect on the person using it. We write the results down and share them with the people who rely on it.
Everything we study comes back to one question. How do you help a person really learn something, and know for certain that they have?
Models that can tell what someone already knows, what they're ready for next, and when they're about to forget it. We treat mastery and memory as things you can measure.
Turning a policy document, a product manual, or a textbook chapter into lessons, practice, voice, and video, while keeping every fact traceable to its source.
No single model is good at everything. We send reasoning to reasoning models, speech to speech models, and video to video models, and we keep a record of why each choice was made.
We hold learning AI to the standard you'd hold a good teacher to. It should be truthful, patient, fair, and accountable, and we test for each of those before anything ships.
Voice, avatar, and agent tutors that listen, demonstrate, and coach someone through real work, so one to one instruction stops being a luxury.
Datasets, benchmarks, and tools for educational AI that we publish, so schools, nonprofits, and other researchers can build on them instead of starting over.

Nucleus OS
A case study from Nucleus Labs
Nucleus OS is the first product to come out of the lab. Give it your links, slides, videos, or documents and it makes finished training from them, then teaches it to your people and shows you what stuck.
The challenge
Someone writes a script, an agency films it, legal reviews it. A single video can take a month.
By the time it's finished, the product, the policy, or the pitch has usually moved on.
Building, assigning, teaching, and reporting happen in four different tools that don't share data.
And at the end of it, nobody could say whether people had actually learned anything.
The approach
Underneath, a set of agents built on the lab's orchestration research hands each step to the models best suited to it.
Drop in links, images, videos, documents, or a short prompt. Reasoning models read the material and write a story spine, scene by scene, that you can edit frame by frame.
Usually handled by
Nothing ships until review checks pass for accuracy, tone, and completeness.
2 minutes
Quick video in your brand for the things that change every month.
Several modules
Full programs with a soundtrack, scene edits, and your brand throughout.
The outcome
From source material to a finished training video. It used to take weeks.
One login and one dataset in place of an LMS, an LXP, an authoring tool, and a virtual classroom.
Integrations with the HR, identity, and sales systems companies already run.
From launch to our first design partners signing on.
What the lab learned
No model we tested was good at storyboarding, rendering, narrating, and tutoring all at once. Giving each step to a specialist made the biggest difference to quality.
Checking accuracy, tone, and completeness before anything ships is what made companies comfortable putting their name on generated video.
Keeping 480p drafts separate from the 1080p finish brought the cost of frontier video down far enough for small teams to use it.
Four rules that decide what we build, which models we use, and what we refuse to ship.
We judge our work by what people can do after they use it. Time on screen and benchmark rankings tell us very little about that.
We work with every major lab and the open weights community. If another model teaches a concept better than the one we used last month, we switch.
Every system is red teamed for misinformation, manipulation, bias, and harm to young or vulnerable people before it ships.
Good instruction shouldn't depend on where you live, what you earn, or which language you speak. We design for the hardest case first.
We work with the strongest models from every major lab, including Claude, GPT, Gemini, Grok, Llama, DeepSeek, Qwen, Kimi, GLM, Seedance, and Wan. Closed and open, from North America, Europe, and Asia.
Our orchestration layer scores each one on how well people learn from it, then hands every task to whichever model does it best.
Model names and trademarks belong to their owners. This list shows the models we evaluate, integrate, or route to. It does not imply endorsement or partnership.

If you research, teach, run an institution, or simply want to build this with us, we'd like to hear from you.