we train models that belong to you.
most ai workloads outgrow generic apis. we begin with your real data and production failures, train a custom model, and prove the gain on a benchmark built from your own logs. we start small: a free two-week eval, yours either way.
evidence
we trained this one for ourselves.
long-form video is a consistency problem. hold one face, one room, one light across minutes, not seconds. we built the dataset, the architecture, and the training runs for it ourselves — our proof of capability, and a data-generation pipeline we now point at your problem. the opening take runs four minutes without a cut. press play and check.
between the towers4:00 the salt flat40s a dog at the park10s a city by the sea1:00 the coast road30s a dog off the leash20s the same road, at sundown40s between the towers4:00 the salt flat40s a dog at the park10s a city by the sea1:00 the coast road30s a dog off the leash20s the same road, at sundown40s
how it works
benchmark first, then training, then deployment.
01 / benchmark
the benchmark.
your production logs and failures become the benchmark for your workload. every change after that is measured, not guessed.
02 / training
the training.
data generation, architecture work, fine-tuning — whatever moves the number. the gain is proved against that benchmark, run for run.
03 / deployment
the loop.
run the model on your own infra, or we host it and charge per token. new production data feeds the benchmark, and the next run has a number to beat.
where we start
an eval before anything else.
two weeks, free. we take your production logs and failures and build a benchmark for your workload. you keep it either way. if we train, that benchmark is the number we have to beat.
the founders train the models. you write to them directly.
for investors: write to the founders →