An AI that learns for a living, built on one rule: nothing becomes knowledge until it survives verification. The AI drafts a candidate fact; a second, independent check must agree before it's admitted; only facts that pass join its memory. Then daily exams — on questions it has never seen — prove the learning is real, not memorized.
Think of it as a sharp junior analyst paired with a strict fact-checker. The AI is only ever allowed to suggest a fact. A separate, independent check — multiple different models must agree, and a tie means no — decides whether it gets admitted as truth. When in doubt, the answer is no. The suggester is clever; the checker is strict; the two never collude.
Every candidate fact faces an independent review before it counts as knowledge. Rejected claims never enter the record — they're parked as open questions until they can be settled.
A web of roughly 12,400 verified facts, linked by meaning — each fact connected to its sources and to the reasoning behind it, so you can always ask "why does it believe this?" and get an answer.
For claims that can be tested, the mind makes a prediction, runs the actual calculation in a safe sandbox, and lets reality grade it. A wrong prediction becomes a corrected fact, not a buried mistake.
The honest question for any learning system: is it actually smarter than the model it started from? So we measured it — rigorously, with a control group. The first surprise: naively stuffing looked-up notes into every answer actually made things worse on subjects the AI already knew. Four disciplines made the improvement provable:
What the numbers mean: this system won't out-argue the world's biggest models on general trivia — that's not the job. The job is this: teach it your business, your products, your rules, and it converts that knowledge into near-perfect, provable recall. Mastering a subject reduces to feeding it that subject's facts.
Every morning it reads its own exam results and redirects its study time toward the gaps — including the subtle ones, like a subject where it aces the questions it studied but stumbles on ones it hasn't seen. "Memorized but not understood" goes straight to the top of the study plan.
Study time goes where the scores say it's needed. Subjects it has mastered get none; subjects where it's coasting on memorization get the most.
It writes its own questions about what it doesn't know, sends the hardest ones to a more powerful model for help — and banks only the verified answers as lasting lessons.
It watches its own vitals — how fast knowledge is growing, how well new facts connect, whether test scores are slipping — and schedules its own review sessions when they do.
The mind runs continuously under a supervisor that keeps every part of it alive and restarts anything that fails — with a hard spending cap on every learning cycle, so an autonomous AI never means an open tab.
AI models running on-siteIndependent fact-checking gateMemory linked by meaningDual data storesOutside help, human-gated