American‑made Super Intelligence: AI your team can learn, trust and deploy.
What it is
our practice for adopting AI safely. We assess and prepare your data, train your people, choose the right model for each task, build and run custom AI agents, and host private AI where the data cannot leave your control, with governance and monitoring throughout.
What it is not
a single product, a chatbot we resell or a promise that AI replaces your staff. It is a service delivered by named engineers, and a person approves any sensitive decision.
Super Intelligence is our AI adoption practice. It covers the two things every company needs: learning, so your people know what AI can and cannot do, and deployment, so the right model runs the right task on data you are comfortable exposing to it.
We have trained and run local large language models, we work with frontier models, and we are a partner of a leading US AI company. Knowing both sides lets us recommend what fits the task instead of what fits a vendor.
We help companies with the two goals of AI adoption: learning and deployment. We train your teams, choose the right model for each task, and deploy it where your data is safe.
Most companies either pay frontier-model prices for simple tasks or keep AI away from the sensitive work where it would help most. Neither is necessary. We train your people, match each task to the right model and run sensitive work where your data stays under your control.
The usual mistakes mirror each other. Some teams send everything to the most expensive model, including tasks a small model handles well. Others ban AI from confidential work, which is exactly where document review, drafting and analysis save the most time. A routing approach avoids both.
Private deployment is a core capability. Models can run on hardware you own or in a private cloud, so privileged, regulated or competitive information stays under your control. We help you decide which data may reach a frontier model, which must stay local, and how to enforce that automatically.
Training is hands-on and role-specific. Leadership learns where AI changes strategy and risk; staff learn practical prompting for their own work; technical teams learn how to build, evaluate and monitor what they deploy.
Governance comes with the deployment. We write a short, usable AI policy, set which tools and data classes are allowed for which roles, and keep logs so you can show how AI is used. For law firms, medical practices and financial firms, that record is as important as the model.
Our Apocalypse IT Package puts a private language model to work for any business that must keep running without the internet, and our track record includes an on-premises model for a regional bank (illustrative) and ALM and CECL models built in-house for a mid-size bank (illustrative). For a deeper look, read "Local, Private or Frontier?" on deciding which data may reach which model, "Local AI vs Frontier APIs" on the cost, privacy and governance of running open-weight models on your own GPUs, and, for lawyers, "ABA Formal Opinion 512 and AI for Law Firms."
Running your own models means building a place for the GPUs: power at 30 kW or more per rack, cooling, fiber and a fabric fast enough for them. Our Datacenter & Colocation Builds practice does that work.
AI data readiness comes first. Before a copilot or agent is switched on, we review what it could reach: file and mailbox permissions, sensitivity labels, data loss prevention rules and configuration drift. We treat this as the main defense against a copilot exposing data it should never have been able to read.
After launch, managed AI operations keep the system honest. We run, monitor and tune the agents and models we deploy, review their logs, watch usage and cost, and keep each one inside its permissions. Private AI can run in infrastructure you own or in colocation we design, which suits finance, legal and healthcare data.
Two GPU servers run the large model as active-active replicas, so either can be patched or fail without an outage. A third handles smaller models, embeddings and OCR. The inference tier has no route to the internet, and every prompt and response is written to write-once storage.
A mixture-of-experts model keeps every expert in GPU memory but computes only a few per token. In a DeepSeek-V3‑class model each layer holds 256 routed experts plus one shared expert; a small router picks the best 8 for each token. All 671B parameters stay resident, about 37B are computed per token, and the experts are spread across the node's 8 GPUs so every GPU contributes to every token. Public architecture figures; the picked cells are an example.
Every request is classified by sensitivity, so confidential data stays on models inside your walls while routine work can use the best frontier AI.
Leadership and team sessions to find the highest-value uses.
One use case, measured against today's cost and quality.
Your people learn to use and improve the tools themselves.
Roll out to further teams with governance and cost controls.
We start with a workshop that produces a ranked list of use cases, then pilot one with real data and measurable results. Training and scale-up follow once the pilot has shown what works in your environment.
Direct answers to the questions we hear most. Something else on your mind?
Send us a messageYes. We deploy local and private models for information that must stay inside your network and route only less sensitive work to cloud models.
All major models, from frontier models from leading US AI labs to open-weight models you run on your own hardware. We are a partner of a leading US AI company.
Yes. Training is a large part of the practice. We teach teams to use AI in their own work and to build their own prompts and tools.
Frontier models run on a provider's cloud and are the most capable. Private models run in infrastructure you control. Local models run on your own hardware. We choose per task based on how sensitive the data is. Our article on private AI explains the options.
It depends on the plan, the provider's data terms and what your professional rules allow. We help you set a policy that names which tools may touch which data, and we deploy private options for privileged or regulated material.
Practical use: writing prompts, checking outputs, protecting confidential data and building simple tools for your own work. We tailor sessions to your roles, such as attorneys, advisers or operations teams.
Yes. We connect models to your documents and tools in a controlled way, with access limited to what each user may already see.
Begin with a short review of your goals, data sensitivity and current tool use. We then propose a training and deployment plan in writing. Pricing is discussed during scoping.
We help draft one that fits your business and regulators' expectations, covering approved tools, data handling and human review. Your counsel should review it.
Yes, where the data cannot leave your control. Private AI can run in infrastructure you own or in colocation we design, which suits finance, legal and healthcare data. We choose the model for each task and add governance and monitoring. Hosting arrangements are scoped with you.
Speak with a security engineer today. The first assessment call is free and confidential.
