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Teams use one model for everything without testing which tasks need it or which data should never leave.
Contracts or regulations may bar data from leaving an approved environment.
The data path is explained after the pilot, and the answer is no.
Leaderboards cannot show how a model handles your documents.
Private models change hardware, operations and speed costs.
We run the same tasks across candidate models and compare failures, speed, cost and data handling. Our own stack runs four local models for side-by-side testing.
Agree with security, legal and business owners on which data may go where, and under what controls.
Define what a usable answer looks like for extraction, classification, drafting and reasoning.
Run the same examples across small, open weight and reference models.
Decide where models, prompts, logs and data live, with matching access rules.
Send routine work to the private model, with a fallback or human review when confidence is low. Fine-tune only when tests show the need.
We pick a workload, its data boundary and its quality bar.
We compare models on the same examples, including hard cases, and price the full setup.
We launch behind access controls, monitor failures and keep the fallback visible.
The data path is agreed before the build.
Sensitive work runs where the data cannot leave.
Your own documents decide whether a small model is enough.
Model fees are weighed against hardware and operations.
Failure cases are found before customers see them.
A stronger model or a person stays available.
You decide where data and logs live.
The same tests rerun when models change.
Client engagement, anonymized
Before a global confectionery maker chose any AI model, its chief information security officer needed to know what data would be used and where it would go. We built a four-pillar data trust framework covering data classes, where each may be processed, access and audit controls, and vendor governance. Each proposed use case was then mapped to an approved data path in a 16-slide briefing for security leadership.
An AI language model far smaller than frontier models, able to run on modest hardware or in a private environment. Small models are often fast and accurate on focused tasks.
A model whose trained parameters are published, so you can host it yourself, control data and logs, and fine-tune it within its license.
Run a privately hosted model in your own cloud or data center, send sensitive tasks only to it, and keep prompts, logs and documents inside the same boundary.
For many focused tasks it is, but only testing on your own examples will tell. We compare small models with a stronger reference on real cases.
Start with better prompts and retrieval, which are cheaper and easier to update. Fine-tune when tests show a consistent gap that examples can fix.
Bring the task and a few examples. We will test whether a private model meets the bar.
Evaluate your model optionsOr email us at admin@pointonezero.com.