Company
Allod exists because the institutions with the most valuable data are the ones least able to train on it.
We install a company's own AI datacenter inside their building, run small task-specific models on it, and improve those models every night on that company's own work. The hardware is leased as one package. The weights belong to the customer. Nothing crosses the property line.
The argument
Four claims, in order. If you accept the first three, the fourth is not a choice.
- 01
On a narrow, repeated workflow, a small task-specific model beats a frontier API.
It has seen the exceptions, the house style, and the precedent. It runs in single-digit hundreds of milliseconds instead of seconds, at a fraction of the energy and a fraction of the cost. This is not a controversial claim any more; it is the reason every serious lab ships small models.
- 02
Building one requires training on proprietary data.
The advantage is entirely in the data. A small model trained on public text is a worse frontier model. A small model trained on twenty thousand of your own adjudicated cases is a different kind of object, and there is no way to get one without those cases.
- 03
No serious regulated company will send that data to a model provider.
Not because their engineers are timid. Because the provider's retention terms cannot be audited from the outside, because the regulator has a view, and because a company that is a vendor today can be a competitor in three years. Every bank has watched that happen in another category.
- 04
Therefore the only architecture that works puts the hardware, the data, and the weights in the customer's building.
Not a virtual private cloud. Not a dedicated tenancy in someone else's datacenter. The building. Once you accept 01 through 03, this conclusion is forced, and almost nobody is building for it because it means delivering freight instead of shipping an API.
How we operate
Five rules we would rather lose a deal than break.
- We ship hardware.
- There is a pallet, a loading dock, a lift, and a two-day install. Companies avoid this because it does not scale like software. It is also the moat.
- The gate is not negotiable.
- An adapter that does not beat the incumbent on the customer's own eval set does not ship. Not with a waiver, not for a demo, not because it is Friday. The moment we override the gate once, the number on the results page stops meaning anything.
- We publish the method with the number.
- A result without its evaluation set, its n, and its adjudication procedure is marketing. We would rather show a smaller honest delta than a large unfalsifiable one.
- We say when it will not work.
- Workflows with no correction signal, or fewer than a few hundred cases a month, or genuinely novel judgment every time, are bad fits. We turn those down on the first call. Selling a rack into a workflow that cannot improve produces one unhappy reference and no second deployment.
- The customer owns the weights.
- Including after they stop paying us. If our only retention mechanism is that leaving is painful, we have built the wrong company.
Who we are
Team
Placeholder — to be filled in
Team entries go here. Three to five people, name, role, and one line each on the relevant prior work — datacenter build-out, model training, or regulated-industry delivery. Nothing else.
We are early and we are small, which is a real fact about buying from us rather than something to talk around. What that means in practice: you will deal with the people who build the thing, the roadmap is short enough that your workflow can shape it, and the second question on your first call should be about our balance sheet. Ask it. The answer to what happens if we fail is in the first FAQ entry and in the contract: you keep the weights, and they run without us.
Talk to us
If you run a workflow like this, we want the call.
Thirty minutes with an engineer, no deck. Or write to hello@allod.us and it will reach a person.