AI models
Language models that know your company
A public model knows a lot about the world and nothing about your pricing rules, your contracts or your internal acronyms.
We connect models to your documents and data, fine-tune them when that is not enough, and run them where your data is allowed to live.
Company context
What the model needs to learn about you
Our approach
Retrieval first, fine-tuning when it earns its cost
Most projects start with retrieval (RAG): the model looks up your documents when it answers, so an updated policy takes effect the same day. Fine-tuning helps when you need a fixed output format or heavy domain vocabulary, and it costs more to train and keep current.
Private and local LLMs
Run the model where your data already lives
If confidential or regulated data cannot go to a public AI API, we deploy open-weight models in your cloud account or on your own servers. This costs more to run than a pay-per-call API, and you give up some model choice in exchange for control.
Private cloud
The model runs in your own cloud account (AWS, Azure, GCP), separate from shared inference services.
Dedicated infrastructure
Reserved GPU capacity for steady, high-volume workloads, where paying by the hour works out cheaper than paying per request.
On-premise
Servers in your own building, for data that is not allowed to leave it. You take on the hardware and its upkeep.
What private deployment gives you
Model choice
Not tied to one model provider
No single model is best for every task.
A contract-review assistant may need a large hosted model, while internal document search can run on a small open model on your own hardware. We choose per workload, based on accuracy, cost, latency and where the data is allowed to go.
The model sits behind an interface in the application, so switching providers later means re-running your evaluations, not rewriting the system.
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