Huntsville, Alabama · On-Premise AI

Your team is already using AI.

The question isn't whether to allow it — that decision was made for you months ago. The question is whether the data they're pasting into it is leaving your building. For CUI, ITAR data, patient records, and privileged files, it can't.

No sales sequence. No demo gatekeeping. One conversation.

Hardened AI shield emblem
Built for CMMC Level 2 CUI ITAR HIPAA Attorney-Client Privilege SEC & FINRA
The problem

Shadow AI is a compliance exposure, not an IT preference

Every organization I talk to has the same three facts in play at once. Most have only noticed the first one.

It's already happening

Staff are pasting contract language, patient notes, case files, and client financials into free chat tools right now. Nobody filed a ticket to ask. A survey won't surface it either — people don't report things they suspect they shouldn't be doing.

Cloud AI is architecturally disqualified

This isn't about whether you trust the vendor. It's that routing regulated data through any third-party cloud is the thing the framework prohibits. An enterprise agreement changes the paperwork. It does not change where the data goes.

A ban stops oversight, not usage

Prohibiting AI produces a policy document and zero behavior change — the usage moves to personal phones and home laptops where you have no visibility at all. You've traded a manageable risk for an invisible one.

The fix

A private AI assistant that runs inside your office

I install a complete AI system on a computer in your building. Your team uses it through a web browser — same experience as the tools they already know. Nothing leaves your network.

No subscriptions. No per-message fees. No vendor reading your prompts to improve their model. You own the hardware, the software, and the data. If I disappeared tomorrow, it would keep running.

See the technical detail →

What your team can actually do with it

  • Ask questions against your own document library
  • Draft correspondence, memos, and reports
  • Summarize long documents, transcripts, and filings
  • Answer policy and SOP questions from internal sources
  • Get code and technical assistance without source leaving
The demonstration that lands: unplug the internet. The AI keeps working. That's not a feature — it's proof of where the data was the whole time.
Honest comparison

Where cloud AI wins, and where it can't play

Cloud AI tools are genuinely excellent. For regulated data they're also unusable. Both things are true.

 Cloud AI (ChatGPT, Copilot, Gemini)Hardened AI
Where your data goesVendor's cloud infrastructureStays on your hardware
CUI / ITAR eligibleNoYes
PHI without a BAA questionNoYes
Privilege exposureThird-party disclosure riskNo third party involved
Works with no internetNoYes
Cost structurePer user, per month, foreverOne-time build, you own it
Audit trail you controlVendor-definedYours, on your server
Raw capability ceilingHighest availableStrong, and closing fast
Zero hardware neededYesNeeds a server or workstation

I'd rather you see the trade-off clearly now than discover it after a deployment.

Who this is for

Five kinds of organization, one shared constraint

Defense

Defense contractors & subs

CMMC assessments are coming and CUI handling is the practice most often missed. Internal document search, deliverable drafting, report summarization — none of it needs to touch a cloud.

Legal

Law firms

Associates are using AI whether the firm has a policy or not. Deposition summaries, research memos, contract review — inside your network, so privilege isn't the open question.

Medical

Medical practices

Note drafting, intake summarization, prior auth letters, coding assistance. PHI never leaves the practice, so the BAA conversation never has to start.

Financial

Advisors & CPAs

Client reports, document summarization, tax research, meeting prep. Portfolio data and MNPI stay behind your own door where your compliance manual already says they belong.

Agencies

Digital agencies

Your clients are asking for AI and some of them are regulated. White-label the deployment, keep the relationship, and let me handle the infrastructure underneath.

Not sure you're on this list?

If your organization handles information that would be a problem in someone else's hands, the same architecture applies. The call is free — that's the fastest way to find out.

Ask me
How engagements run

Four steps, no surprises

A free 30-minute call

What tools your people are using, what frameworks you operate under, what's actually slowing your team down. If there's no fit, I say so.

Infrastructure audit

A written assessment of your current exposure, what hardware you already have that can be used, and exactly what a deployment would involve. Yours to keep, whether or not you hire me for the build.

Deployment

I install and configure the full stack on your hardware, integrate it with your existing logins, load your documents, and verify it against the requirements the audit identified. Days, not months.

Training & handover

A working session with your staff so they actually use it, plus documentation. Ongoing maintenance is available on retainer — or take the keys and run it yourself.

Fair questions

The objections I hear most

We already have Microsoft Copilot.

Copilot is a strong product and I'm not going to pretend otherwise. It's also cloud-processed under Microsoft's terms. For CUI, ITAR data, PHI, or privileged material, the question was never whether Microsoft is trustworthy — it's whether routing that data through any third party satisfies the framework you're assessed against. In most cases it doesn't. Many organizations end up running both: Copilot for general work, a private system for the regulated material.

Our IT team can handle this.

They probably can. The question is when, and at the cost of what else. This is a specialized build with a lot of non-obvious failure modes — I've hit most of them already on my own infrastructure. I deliver in days without pulling your IT staff off the work they're already behind on, and I document everything so they own it afterward.

Open models aren't as good as the frontier ones.

For the hardest reasoning problems, that's still true. For what organizations actually use AI for — summarizing documents, answering questions from internal sources, drafting routine correspondence, cleaning up writing — current open models perform comparably, and the gap keeps narrowing. Good-enough-and-compliant beats best-in-class-and-prohibited.

What happens when the models get outdated?

New open models ship constantly. On retainer I evaluate them and deploy the ones worth deploying. Without a retainer, the system keeps working exactly as delivered and your IT team can update it using the documentation. Either way you're never waiting on a vendor's roadmap or absorbing a pricing change you didn't agree to.

Isn't this expensive?

It's a real project cost, and I won't pretend it isn't. Run the comparison though: per-seat cloud AI is a monthly bill that never stops and rises when the vendor decides it does. A private deployment is a one-time build plus optional support. Somewhere in year two the lines cross — and on your side of the cross, you own the asset. We'll do that math against your actual headcount on the call.

We'll just not use AI.

That's a policy, not a description of what's happening. I've yet to see an organization where a ban meant zero usage. What it reliably produces is usage you can't see, on devices you don't control, by people who now have a reason not to tell you. Giving your team a sanctioned tool is how you get visibility back.

Start with a conversation, not a contract

Thirty minutes, no cost, no obligation. It's entirely possible the answer is that you don't need anything from me yet — and I'll tell you that.

Book a Free 30-Minute Call