Sandlake gives enterprises with proprietary data and internal know-how a dedicated, isolated GPU pod to self-host inference and fine-tune their own models. Nothing is sent to a public model provider, and nothing leaves the environment you control.
Every prompt to a public model is a round trip through someone else's servers: logged, retained, sometimes kept to improve their next model. That's not a risk you manage with a policy; it happens the moment you hit send. Enterprises sitting on proprietary data and internal know-how are stuck with a bad trade: keep it out of AI entirely, or get ahead by leaking exactly what makes them competitive.
Single-tenant GPU capacity provisioned for your workload, not a shared endpoint processing everyone else's prompts alongside yours.
Distill your internal documents, codebase, and institutional know-how into a model that actually knows your business, without that data ever leaving the pod.
Query the model from inside your own environment. No prompts logged by a third party, no data used to train someone else's public model.
Whatever the data or the domain, it comes down to keeping inference off a public model provider, and turning internal know-how into a model of your own.
Run inference on your own dedicated hardware. Prompts, documents, and outputs never reach a third-party API. There's no provider in the loop to log, retain, or train on them.
Fine-tune an open-weight model, or train a vertical model from scratch, on your proprietary data. Everything runs entirely inside your own environment, never used to improve anyone else's model.