The models are ready.
The data is not.

A pharmaceutical process engineer running a 2,000-liter fermentation generates readings from a dozen instruments, each writing a different format, each timestamped against a different clock. The run's context — media lot, feed strategy, who changed the setpoint at 3 a.m. — lives in a notebook.

Multiply that by four decades and a few hundred instruments. The result isn't a data lake. It's scree: technically all present, structurally unusable. Fine-tuning cannot fix a missing join key. Neither can a bigger model.

This is not a modeling problem. It's a substrate problem — and it's identical in a biologics suite, a flavor house, a 300mm fab, and a polymer lab.

A lith is a layer of rock. A datalith is a layer of record.

Datalith was founded on an observation that keeps repeating across industries: the companies with the most valuable process data are the least able to use it. Not because they lack ambition or engineers, but because the data was never captured with a model in mind. It was captured to satisfy a regulator, or a lab notebook, or a machine that shipped in 1998.

We started with bioprocess because it is the hardest version of the problem: high-value runs, low run counts, brutal regulatory constraints, and instruments that barely acknowledge each other. BioReact proved the substrate. Flavorworks, Wafermind, and MaterialOS are the same proof, in the same order, in the next four industries.

The picks
and shovels
of industrial
AI.

Every dollar spent on industrial AI models is downstream of a dollar that has to be spent on data substrate first. That spend is currently going to internal engineering teams writing parsers. It is a category that gets bought, not built — as soon as something exists that is worth buying.

Datalith's structure is deliberate: one engine amortized across four verticals, each entered through a self-serve product that scientists adopt before procurement ever hears the name. Land with a single team, expand to the site, then the network.