Data infrastructure for real-world predictions.

Our agents find and check the sources.

Describe the prediction in your own words. Agents break it down and search our catalog, provider APIs, the open web, and even your own files. Every candidate is checked for coverage, cadence, and licence.

Then they build and deploy the dataset.

Agents write the code that ingests every source, cleans it, and joins it into one table your model can train on.

Zero ops

You approve the dataset once. It deploys to our infrastructure and keeps itself updated.

One clean API

Whatever the sources were, you get the latest rows from one endpoint, in the same shape every time.

One place to put public data into production.

You watch every step happen in the app, from the search to the approval. Our cloud keeps the dataset live, and your model reads it through one endpoint.

We know this is a real problem.

We built this for ourselves first. Our prediction-market strategies trade on it. Every source we added cost real engineering time, and AI did not change that.

Ten teams on the SDK

We exposed it as an SDK that puts live and historical data behind one schema. Ten teams use it and one pays us.

Then we built the harness

Every team asks for different sources. Writing each adapter by hand does not scale, so the harness does it instead.

Public data is free. We get paid to run it.

People already search for public datasets, and the results range from a clean API to a zip on an FTP. We take the most searched ones that are hardest to use, turn them into a proper API and publish them free.

Free, so they find us

Anyone looking for that data lands on our version. They can use it without paying us anything.

Paid when they fork it

Teams putting a model in production soon need another source or a different shape. They run that dataset on our infrastructure and pay for compute, storage and bandwidth.