Both turn on cooperative binding. Your 2024 note that a protein stabilizes through cooperative binding1 answers Maya, a materials scientist you follow, whose alloy has to hold under stress2. Neither of you went looking; the shared mechanism connected you.3
Today, no two people can think together through their AIs. This is what it looks like when they can: the Sapience app, live, every answer from your own knowledge and the people connected to it, cited to the exact objects it used. We build for science first because it is the hardest kind of knowledge work; the same brain runs a lab, a codebase, or a family move. Wander through a live example ›
Your work organizes itself into a navigable map of knowledge, from a single thought out to the whole landscape of what you know.
It holds ten million tokens of your work, years of it, past where every frontier context window ends, and reasons over all of it at once. Nothing you established is forgotten.
Every answer is synthesized from your own knowledge and cited to its sources. It cannot hallucinate support that is not there: when it does not know, it says so.
From any idea, surface the adjacent work and people you would not have found by search. For scientists, that reach extends into 250 million research papers.
Tell one AI something and every other one knows it: Claude, GPT, Gemini, or open.
Connected over MCP, it works behind your Claude, GPT, or Codex sessions: reading what you know mid-conversation, writing what you learn back. And because it retrieves the few objects that matter instead of replaying your whole history, answers arrive at a measured 357x fewer tokens, and the same plan goes further.
A session can leave an addressed note for another thread, or for a future you. Pinned commitments surface when the next session starts, and results route back to the thread that asked, across Claude Code, claude.ai, GPT, and Codex. Nothing loses the thread between sessions and days.
Share a project or your whole brain. On import it lights up where a colleague's knowledge connects to yours, and every claim stays credited to whoever established it.
Someone you have never heard of, in a lab or a company you would never think to look in, is working on the other half of your breakthrough. And AI is making it worse: each scientist several times more productive, fields measurably less connected (Nature, 2026). Sapience is the bridge. Because knowledge stays owned and credited, it can be matched across people and organizations no single vendor could connect. This is measurable: in an internal benchmark on discoveries published after the reasoning model’s training cutoff, Sapience ranked the approach that later won first in 11 of 13 problems.
Memory recalls what one person stored. Sapience matches what everyone knows, by mechanism rather than keyword. And a connection is not the end point: it becomes a decision, a collaboration, a breakthrough no one could reach alone.
Science is the hardest version of the problem. The same brain serves anyone who builds knowledge with an AI beside them: research groups and engineering teams, and just as naturally a couple comparing childcare options, a family planning a move, two friends building anything together.
Everything here works today for one brain. The network it plugs into is what we are building it for.
Frontier models train on your work, lock it inside one tool, and isolate every knowledge worker behind a generation engine. The smarter each of us gets alone, the more fragmented the whole becomes.
You own your intelligence, captured once and portable across every AI. And because it stays yours, signed and credited, it can connect.
The AI that knows your field exists because a lab trained on work like yours. It learns from you, for everyone but you.
Opt out and your data stays yours, but the model stays frozen: no memory of your methods, your decisions, your last six months.
Sapience learns continually from your work, for you alone. Nothing trains a foreign model. And when you choose, it connects with others' knowledge, credited, never given away.
An enclosure of knowledge is underway: frontier labs bake expert work into private weights and rent it back. What matters next lives in experts' heads and private work, on the order of a million times larger than the web (Trask and Strahm, Institute for Progress, 2025). The company that makes private expert knowledge usable without exposing it defines the next era of AI.
And ownership has an endgame: because every claim is signed to its creator, expert knowledge can one day be licensed directly, from the people who make it to the labs that need it, priced, permissioned, and compensated, with no broker taking the difference. The attribution registry is the rails.
That is the gap Sapience Labs exists to close: for one mind, and between all of them.
“This is something incredibly useful. The cross-domain connections are things I would never have found on my own.”
“The first two papers it surfaced were spot on for my research. It is giving me ideas on what to do next.”
“I one billion percent see the benefit. The more collaboration across the world, the faster science progresses.”
The team. Physicists, engineers, and researchers from Berkeley, Cambridge, Harvard, CERN, SpaceX, Google, DeepMind, and OpenAI, quietly building; more than $100M raised for previous ventures.
We store the knowledge objects we extract, never your raw conversations, and we never train on your data. The collective network is built from shipped primitives and a roadmap; we describe what is live as live. Performance against frontier baselines is validated separately and reported in full to partners, not summarized here.