Built for mid-size banks, payment processors, and credit unions. Sandlake gives you dedicated, single-tenant infrastructure and a platform to build, test and run your own models, so your data never leaves an environment you control.
Financial institutions hold some of the most valuable proprietary data in any industry, and AI adoption still lags. Two structural barriers explain why.
Data cannot go to public model providers. Sending proprietary data to a public model is a non-starter under data sovereignty, model risk and third-party vendor risk requirements.
GPU infrastructure is a different discipline. Model risk functions are built for statistical and ML governance, not for running GPU clusters. Standing up dedicated infrastructure is a capability most institutions need to grow.
AI built on your own data pays back where your institution feels the most pressure: the cost of operations, the strength of risk control, and how fast customers get an answer.
Models take the first pass on high-volume review, so your institution handles more volume without growing headcount at the same pace.
Every case gets a consistent first review and a documented record, so fewer cases slip through and every decision can be traced.
Onboarding, lending and merchant approvals move faster, and staff spend more of their time with customers and members.
And the model behind it is yours: trained on your data, improving as your data grows, and owned by you. That is your moat.
Whichever service you take, your data never leaves an environment you control.
Lease a dedicated GPU pod for training, fine-tuning and inference, operated by Sandlake. No shared hardware, no co-mingled data.
Your team already builds and governs models. Sandlake works alongside it to fine-tune a large model on your own data, then keeps it current. The model and its weights belong to you.
Run ongoing inference, priced per use. Metered access to the finished model on infrastructure isolated to one tenant.