Where we work
Regulated, complex, data-heavy industries — where the data is messy, traceability matters, and the domain has to be understood before it can be modeled. Four of them, and the same problem underneath each.
Four industries, in the order we know them best
Pharma & life sciences
The clues to the next result sit across public databases, internal lab systems, and years of past experiments. This is where we have done our deepest work.
Manufacturing & industrial
Machines outlive their documentation, and supplier data nobody can compare quietly costs money every month.
Chemicals & industrial biotechnology
Strain engineering runs on a loop — design, build, test, learn. The loop only moves as fast as the data coming back from it.
Healthcare & clinical research
Two studies ask the same question and record it differently. We make them analyzable together, provenance intact.
One thing, described in pieces
Every one of these industries has a single real-world thing whose description is spread across systems that never had to agree with each other.
| Industry | The thing | Where its pieces live |
|---|---|---|
| Pharma & life sciences | A compound | Public databases, internal lab systems, years of past experiments |
| Manufacturing & industrial | A machine, by serial number | As-built records, retrofit history, service reports, supplier data |
| Chemicals & biotech | A strain | Strain database, lab notebooks, process historian, the run spreadsheet |
| Healthcare & clinical research | A patient cohort | Study databases, registries, routine-care records |
The domains could not be less alike
The problem underneath them is the same one: information about a single thing — a compound, a machine, a strain, a patient cohort — spread across systems that were each a sensible decision on their own.
Learning the domain well enough to know what that thing actually is takes longer than building the platform. It is also the part nobody can skip.
Your industry not listed? The approach travels. Learning the domain is the work either way.
Does your world look like this?
We would like to hear how the problem shows up for you.
Get in touch