Pattern Hunter, alive at 3am.
A 24/7 autonomous engine that scans the world's public data — academic papers, patent filings, SEC documents, news feeds, on-chain data, government records — looking for hidden correlations no one has noticed yet. The system does not stop when no one is watching.
A telescope for hidden correlations.
Most genuinely valuable discoveries live in the space between fields. A signal in one domain — earnings-call language, patent citation graphs, urban density — that predicts something in another. These correlations exist in the data; what doesn't exist is anyone with the time, breadth, or patience to look for them.
Pattern Hunter is that someone. It scans dozens of public data sources continuously, generates its own statistical hypotheses, runs falsification tests, and publishes both the findings and the dead ends. It never tires of the work, never gets bored of a domain, and never stops mid-investigation.
The product is the process.
Most analytics products ship the answer and hide the work. Pattern Hunter does the opposite: the visible reasoning of the engine is the product. You watch it think, you see what it tried, you see what it rejected, and you can correct it.
Designed to be wrong, in public.
The hard part of pattern discovery isn't finding correlations — any sufficiently broad scan will surface thousands. The hard part is distinguishing real signal from noise, and being honest about it when you can't. Pattern Hunter's surface is engineered around that distinction.
Auto counter-narratives
Every published discovery ships with an automatically generated counter-narrative: the strongest alternative explanation, the most plausible confound, the data that doesn't fit. Not in an appendix — on the discovery card.
Public hit-rate
Every confirmed pattern is tracked over time. The page displays the engine's running hit-rate across all published findings. No hiding behind cherry-picked successes.
The graveyard
Patterns that were published, then contradicted by later data, are not deleted. They are moved to a publicly browsable graveyard with the original claim, the contradicting evidence, and a post-mortem. Retraction is part of the system, not an embarrassment.
Methodology open by default
Every discovery ships with dataset sources, statistical method, parameters, and the code that produced it. You can re-run the analysis. You can disagree with the choices. Both are intended.
Representative findings.
The following are illustrative of the kinds of cross-domain correlations Pattern Hunter is built to surface. Specific numbers are representative of the engine's intended output, not yet-confirmed findings.
A research process, not a black box.
The engine is autonomous, but it is not closed. Any user — independent researcher, domain expert, motivated amateur — can propose hypotheses for testing, donate datasets the engine doesn't yet have access to, or critique the methodology of any published finding.
Contributions go through the same falsification suite as the engine's own hypotheses. Confirmed contributions are permanently attributed on the discovery card, with the contributor's name and the role their input played — flagged the anomaly, supplied the dataset, caught the methodological error.
This is not a forum. It is a real process with a real attribution model.
The engine is coming online.
Request early access — be among the first to watch the system think.