Where tbd-0 fits.
Typical situations where a smaller, sovereign model is the better choice.
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A customer-facing agent with predictable cost
SituationYour support or sales agent handles thousands of conversations a day. With token-based pricing, every turn of a long conversation costs more than the last, and the monthly bill is hard to forecast.
With tbd-0Constant context and per-turn billing keep the cost of each turn flat. You know what a conversation costs before it starts.
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On-premise AI for a security-critical application
SituationDefense, critical infrastructure or internal security tooling. The data can’t leave your network, so a cloud API is not an option.
With tbd-0We deploy the model on your own hardware. Our models are small and run on AMD and Intel hardware, so you don’t depend on scarce GPUs.
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Private data that must not go to the US
SituationPersonal, health or confidential business data that you can’t hand to a provider under US jurisdiction.
With tbd-0Our models run on European infrastructure with no US subprocessor, so the US Cloud Act doesn’t apply. We don’t train on your data.
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An expert model for machine manufacturing
SituationTechnical documentation, service reports, specifications. General-purpose models don’t know the terms of your field and get details wrong.
With tbd-0tbd-0-mm is trained for the language of engineering and production. It knows the field, so it can be smaller and still more precise.
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A coding assistant that keeps your code in Europe
SituationYour developers want AI help, but your source code can’t go to a US provider.
With tbd-0tbd-0-cs is built for coding and software development and runs in our European cloud or on your own servers.
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Long conversations and long documents
SituationContract reviews, research or support threads that run for hours. With a growing context, models get slow and lose details in the middle.
With tbd-0The context stays the same size, so there is no length limit and nothing gets lost.
A different use case?
Tell us about it. If a general-purpose model isn’t enough, it might be our next expert model.