Research lab
A system that reads, guesses, tests and learns, on repeat. It runs on your own machines, so your data stays with you.
Each round starts smarter than the last.
Talk to usResearch in progress, not a product. Each item below says how far it has got.
Research moves as fast as the guess → test → learn loop. A person drives every turn, so it is slow.
Thousands of guesses at once. Months become days.
Each result feeds the next round.
You ask the question. The system runs the loop. Here is one turn.
You set the goal. It runs day and night. Every finding comes back with its evidence.
More papers than any lab could read.
Thousands at once.
Most are ruled out.
The next round builds on all of it.
Runs on your servers. Works with no internet. Nothing leaves.
Your data stays where it is.
Works fully offline.
Pharma, universities, results under embargo.
What the loop has found so far, with sources and honest limits.
Automated labs lean on a stand-in model to pick the next experiment. Those models fail quietly outside the data they saw. When does the stand-in stop being worth it?
Finding that line, with honest error bars.
AI research systems fool themselves: picked benchmarks, leaked answers, results chosen after the fact. How many of their “discoveries” hold up on a second look?
Counting how many survive pre-registration, false-discovery checks and replication, built into the loop.
Give the system everything published up to one year. Ask for ideas. Then check which ones the next year confirmed.
Building that test, with leaks from the future sealed off.
The math holds up. Real chips leak through power use and timing, and AI already pulls keys out of them in the lab.
Measuring how much margin hardened code really has.