We build the applications instead. The model sits behind the app, not in front of the employee: governed, scoped and tuned to one job, so the work comes out right every time and you can prove how.
Give a thousand people a prompt box and you get a thousand different answers, none of them logged. The work that matters — pricing, contracts, catalogs, customers — needs the same answer every time, from data the person is allowed to see, in a form you can defend later. That is an application problem, not a prompting problem.
What most firms have
What we build
Every one of these is the same pattern: a governed application with a task-tuned model behind it. Two are ours. The third is yours.
Classification and taxonomy, done by machine.
Route documents, tickets, records and products into your existing taxonomy. Millions of items, one consistent set of rules, no manual triage.
Point it at a messy corpus and it proposes the taxonomy itself, or cleans product data across thousands of SKUs for marketplace category trees.
▸ in production at a global consumer-goods leader since Apr 2026
Know how your creative will hold attention, before the budget is spent.
Upload an ad and get a predicted attention and involvement ranking for the platform you are about to publish on, while the creative can still change.
This is where our modelling stack runs deepest: our own calibrated models, trained on measured human attention and validated against external holdouts.
▸ calibrated heads · measured human attention · external holdout
The work you would never hand to a chat box.
Clause extraction across a contract estate. Root-cause clustering on a support queue. Any process where the answer has to be consistent, the data has to stay scoped, and someone will eventually ask how it was produced.
We build the application around the process, train the model on the task, and leave you the audit trail.
▸ start with the job that costs your team the most hours
General models are good at almost everything and excellent at almost nothing. The work we govern is the opposite: narrow, repetitive and unforgiving. The same input has to produce the same output every time, at volumes where a one-in-a-hundred error is thousands of mistakes nobody will ever find.
So we train for the task. Our work with the Barcelona Supercomputing Center gives us the compute to do that properly rather than approximate it through a general-purpose API — which also means the model can run where the data is allowed to be, and the cost per request stays predictable as volume grows.
Tell us the process that has to be right every time. We will show you what it looks like as a governed application.