Private, Local AI for Regulated Businesses
Small Pond Software, LLC provides AI delivery services for growing companies, with a focus on private, local, data-sovereign AI for Northern Colorado businesses. In a short interview, Patrick described the kinds of companies that cannot or will not use public AI providers, the data worries that keep coming up, and a common misconception about what a model actually is.
Who hesitates to use public AI
Patrick said the hesitation is not limited to one industry. "There are several different types of companies that are either prevented or hesitant to use public AI providers," he said. "Most are either regulated such as laws firms or have strict data privacy requirements such as financial services."
That framing matters for any business weighing a cloud AI service against a local setup. For law firms and financial services, the question is often not whether AI could help but whether the data involved can leave the organization at all. When the answer is no, a public cloud model is not a practical starting point.
The data-leak worry that keeps coming up
When businesses ask whether they can use AI without sending customer data to a cloud service, Patrick said a specific concern surfaces repeatedly. "Most businesses want to reference their own proprietary data that isn’t available in public cloud AI models," he said. "They worry the data will still leak out."
The point is not just privacy in the abstract. Companies want AI systems that can work with their own internal knowledge, and they are uneasy about whether that knowledge stays contained. That concern is central to the data-sovereign approach Small Pond Software, LLC describes for Northern Colorado businesses.
A model is not the whole system
Patrick also pushed back on a common assumption about foreign models. "Some companies assume foreign models are aligned to and report to their countries of origin," he said. "Models are the brains of an AI system and they still need a body and hand to be useful. They cannot function without the body, which is the system built around the model and can be controlled."
That distinction between the model and the surrounding system is a useful one for decision-makers. The model is one component; the system built around it is where control, access, and data handling are actually determined. Readers who want background on how these components fit together can look at large language model basics, but Patrick's point is practical: evaluating a model alone does not tell you how the overall system will behave.
What companies get wrong early
The interview questions asked which part companies most often get wrong when deciding early between local AI and a cloud service. Patrick's answers point to a pattern: businesses focus on the model and the provider while underestimating the system around them. For regulated firms and companies with strict privacy requirements, the more useful early question is what data the system will touch and who controls the environment it runs in.
That is the territory Small Pond Software, LLC works in. More about the company's approach is available at Small Pond Software.
Practical takeaways
- Identify whether your industry or data falls under regulatory or privacy constraints before comparing AI options.
- Ask specifically whether proprietary data can be used without leaving your control.
- Treat the model as one part of a larger system, and evaluate the system's controls alongside the model itself.
- For Northern Colorado businesses, the relevant question is often private, local, data-sovereign delivery rather than a generic cloud AI comparison.