At Sapience Labs, we build an AI that keeps learning from your work and connects it to everyone else's. We call this networked continual learning.
Continual learning
A pre-trained model ships knowing the world up to a cutoff, and then it stops learning. Each new session starts from zero, so you explain your project and its constraints again, including what you settled last Tuesday. In machine learning, continual learning is the old research problem of a system that keeps learning after deployment without losing what it already knew.
We work on continual learning at the system level, around the model. Sapience learns from your work as you do it, with no retraining of the model underneath. What you establish stays structured and cited, and it is current in every session and every tool you use. You can change the model and keep what you have learned.
Networked
Every AI today learns once and serves one person at a time. Most of the useful knowledge in science is in no paper and no dataset. That knowledge sits in personal notes about methods that worked or runs that failed, including why a parameter was chosen. The person who needs it often works in another field with a different vocabulary and has never heard of you.
A networked system lets that knowledge move between people. Your findings can reach someone you have never met in time to help with a complementary part of the problem. That knowledge is shared only with your consent and with your name attached. People in different fields can build on findings that fit with their own work. People who already work together can keep a shared record current as their collaborative work continues across sessions. Each person's shared findings become part of the collective knowledge the group can draw on.
What that means in practice
- Continual learning. Your AI picks up where you left off, on any model, without you managing its context. The results are on the homepage.
- Connections. When you choose, your work connects with other people's work, including work by people you would never have searched for. You see why the two fit.
- Ownership. You own the record of what you know. It works on any model. Everything you share is credited to you, and nothing leaves unless you share it.
Why science first
We start with frontier science. Producing ideas in frontier science is expensive, and lost results cost people years of work. Credit matters to scientists, so a system that keeps the author's name on each result is one they can trust with their work. If the approach holds up for research, it will carry over to other knowledge work.
The longer argument for why ownership and sharing go together is in Own your intelligence (March 2026). To try Sapience, ask for a place on the homepage.