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Thirty seconds inside a real brain.
Recorded from the app you build in about ten minutes. Or put your own question to a live brain ›.
Cultivating collective human wisdom
sa·pi·ence /ˈseɪ·pi·əns/ n. 1. wisdom; the trait our species is named for. 2. knowledge grounded in experience, compounded when minds connect.
It was trained on the commons, everything humanity wrote, measured, and discovered together, then sold back to each of us one seat at a time. So each of us thinks alone beside a private oracle, more productive and more alone, while the fields we work in grow measurably less connected.
Somewhere out there, someone holds the other half of your breakthrough. Your AI will never find them. It was not built to.
Sapience brings AI back to people, by connecting them. What you learn stays yours: signed to you, working in whichever model you use tomorrow, seen by no one unless you decide.
We are building second brains that connect, by choice, into a shared memory for humanity.
Sapience turns what we each know into what we all know. Owned, never taken.
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. Here is what it looks like when they can: every answer drawn from your own knowledge and the people connected to it, cited to the exact objects it used. Ask a real research brain your own question ›
One brain across every session. Claude Code, Claude, and Codex share the same memory: what one session learns, the next one knows.
Watch it work
Recorded from the app you build in about ten minutes. Or put your own question to a live brain ›.
Your first week
You stop re-introducing yourself to your AI every morning. It already knows the project, the constraints, the thing you decided last Tuesday.
Your Claude and your GPT finally know the same you. Switch tools mid-thought; nothing resets.
Your plan does more before it runs out, because your AI reads three facts instead of replaying your history.
And the first time a colleague’s knowledge lights up against yours, you feel what the network is for.
Your work organizes itself into a navigable map, from a single thought to the whole of what you know.
Ten million tokens, years of your work, past where every frontier context window ends and reasoned over at once. Nothing you established is forgotten.
Every answer is synthesized from your own knowledge and cited to source. When it does not know, it says so.
From any idea, surface the work and people you would not have found by search. For scientists, that reach extends into 250 million papers.
Tell one AI something and every other one knows it: Claude, GPT, Gemini, or open.
It works behind your Claude, GPT, or Codex sessions over MCP, reading what you know and writing back what you learn. Retrieving the few objects that matter instead of your whole history, answers arrive at a measured 357x fewer tokens.
A session can leave an addressed note for another thread, or a future you. Pinned commitments surface when the next starts, and results route back to the thread that asked, across Claude Code, claude.ai, GPT, and Codex.
Share a project or your whole brain. On import it lights up where a colleague's knowledge meets yours, every claim 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. The way it is set up today widens the gap: each scientist several times more productive, the fields they work in measurably less connected (Nature, 2026). Because knowledge stays owned and credited, Sapience matches it across people and organizations no single vendor could connect. In an internal benchmark on discoveries published after the reasoning model’s training cutoff, it ranked the approach that later won first in 11 of 13 problems, on a small internal set whose protocol we share with partners. Watch three strangers solve one problem ›
Memory recalls what one person stored. Sapience matches what everyone knows, by mechanism rather than keyword, and a connection 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, from research groups to a family planning a move.
Everything here works today for one brain. The network it plugs into is what we are building it for.
Sapience turns what we each know into what we all know. Owned, never taken.
The way it works today: your work trains a frontier model, then stays locked inside one vendor's tool, with nothing flowing back to you. The smarter each of us gets alone, the more fragmented the whole becomes.
You own your intelligence, captured once and portable across every AI. 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. When you choose, it connects with others' knowledge, credited.
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). Whoever makes private expert knowledge usable without exposing it defines the next era of AI.
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, 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, SpaceX, Google, DeepMind, and OpenAI, quietly building; more than $100M raised for previous ventures. If this is the problem you want to spend your years on, come build it with us.
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. The headline results are further down this page; the full protocols and run files go to partners.
A brain is only as useful as the links it can make. Sapience is built for connection at two scales: between the pieces of what you know, and between minds. The labs ship a frozen generation engine; Sapience is a system that keeps learning. Its unit is the Knowledge Object: structured, typed, credited, and linked to others by edges that make discovery possible.
A prediction-error gate weighs every claim against what the system already knows, so it accumulates signal, not noise.
Like a brain asleep, it replays and consolidates episodic memory into durable knowledge, surfacing non-obvious connections. Discovery, not storage.
When new knowledge contradicts old, it resolves and supersedes rather than holding both. Contradictions are flagged, not buried.
Near-duplicate memories suppress each other, so retrieval stays sharp as the store grows past a single context window.
The reasoning model is interchangeable: Claude, GPT, Gemini, or open. The intelligence lives in the architecture, not in any one vendor's weights.
Every claim is traceable to its source and cryptographically signable. Provenance is a property of the substrate, not an afterthought.
Why knowledge objects, not logs.A knowledge object is the condensed chain of thought behind a result: reasoned over once, at capture, available for recall forever. Flat text stores and session logs save what was said and force every future query to re-derive the thinking. Sapience stores the thinking itself, which is why the substrate stays small, fast, and reasonable-over at field scale.
A complementary-learning memory architecture, with forty provisional patent filings across it. Performance validation against frontier baselines is ongoing and reported separately.
One mind, whole problem.
Most agent architectures exist to dodge a context limit: split the problem across a swarm, hand each agent a shard, reconcile the lossy handoffs. Every split loses constraints, drifts goals, diverges world models. Sapience holds the whole problem in one reasoning context, ten million tokens of it, past where frontier windows end, so the problem never needs to be split at all. Agents become optional workers, not a structural crutch.
The whole problem stays in one mind across long work, so objectives do not quietly drift out from under you.
What you established last month is still there: structured, citable, and current in every session and every tool.
It reasons from established knowledge and cites it, so it does not invent what it cannot support.
An illustration of what we measure. On multi-hop reasoning over stored context (matched protocol, three seeds), Sapience holds 81–86% from 4K to one million tokens and is measured out to ten million, while frontier models reading the full window fall from 73% at 4K to between 0 and 31% at one million (Claude Sonnet 4.6 across the sweep; GPT‑5.5, Gemini 3.1, DeepSeek V4F, Opus 4.7 at one million).
Reader named per row (Claude Sonnet, DeepSeek, Gemini): identical model both arms, only Sapience differs. Numbers locked, three seeds where marked. We publish the losses too; the last row is one of ours. And the obvious alternative fails: retrieval over a persistent store holds at scale; dumping the store into context does not.
One long-context, generalized reasoning architecture underneath every kind of work: science, engineering, code, analysis, writing.
The Knowledge Object is designed as an open standard: signed, credited, typed, interoperable with existing open knowledge formats. Because the format is open, your knowledge is never hostage to us. What stays ours is the intelligence that connects it.
The open-core company for the knowledge layer. The Knowledge Object standard is stewarded by CoreTx, our sister company, so the format your knowledge lives in is never hostage to us either.
sa·pi·ence n. 1. wisdom; the trait our species is named for. 2. knowledge grounded in experience, compounded when minds connect.
Not a smarter individual. A collective that gets wiser.
A brain for each of us. The beginnings of a collective one.
Ask for a place and we send an invite code. The build takes about ten minutes: no setup, no API key, one line builds your brain from your recent work and gives you a private link. The first thousand builders get the network free, for life. The first discoveries will be theirs. Come for the independence. Stay for the network.
Prefer email? oliver@coretx.ai
Once your code arrives: Mac and Python 3, your history is read locally, the asking and connecting run on us. Not a terminal person? There is a one-click Mac installer. Prefer to see one first? Ask a live example brain your own question.
read on your laptop, never uploadedConnect over MCP and the loop closes: your AI reads what you know mid-conversation and writes what you learn back, so every session starts smarter than the last.
Your link is private and unguessable. During the beta, hosted demo brains auto-delete after 14 days of inactivity; a brain on your own machine is yours for good. For a fully private or on-prem deployment, talk to us.