Private AI, for companies that can't send sensitive data to public tools.
What it is, when it's the right call, when it isn't, and how to deploy it without buying hype.
Book an AI Strategy CallWhat private AI means
Private or local model infrastructure. A governed knowledge layer over your documents and systems. Browser-based access for your staff. Monitoring and backups. Plus — and this is the part vendors skip — the policy and training around all of it.
What it is not
- Not a magically smarter model
- Not a replacement for your systems of record
- Not a reason to skip governance
- Not automatically safer if deployed without controls
It's an architecture and an operating model, not a GPU box.
The model is the easy part. The controls around it — who can query which data, how usage is monitored, who owns the roadmap — are what make private AI safe and useful.
The architecture in plain English
- Your team's existing laptops
- Private AI portal
- Private / local models
- Governed knowledge layer
- Your source systems — documents, email, practice management, ERP
No new hardware on desks, no data leaving your environment. Staff use a browser; everything behind it stays yours.
The four cases for it, and the four against
Right call when
- You handle regulated or privileged client data
- Contracts impose data-residency constraints
- Your IP is the business, and a leak is existential
- Leadership wants control it can prove to a regulator or a client auditor
Wrong call when
- Your data is mostly public anyway
- You can't name one genuinely sensitive workflow
- You're a team of 10 with no IT capacity
- Enterprise commercial tools with the right controls meet the bar at a tenth of the cost
What teams run on it
Internal knowledge assistant
Answers from your documents, not the open internet.
Client document Q&A
Interrogate contracts, filings, and case files without exposure.
Secure drafting
Proposals, memos, and letters grounded in your own precedents.
Policy assistant
Instant, cited answers on internal policy and procedure.
Workflow automation
Document-heavy processes automated inside your walls.
Executive research
Board-prep and market questions answered from data you've approved, not the open web.
How deployment works
The assessment validates the architecture
Two weeks to verify private AI is the right call for your data, budget, and workflows, before you buy anything.
Last Rev deploys the stack
Under a separate SOW: infrastructure, knowledge layer, portal, monitoring, backups.
Advisory governs adoption
Measurement, iteration, training, and the operating model that keeps it safe and used.
Common questions
Is private AI automatically safer than commercial tools?
No. A private model deployed without access controls, monitoring, and policy is just a more expensive way to be exposed. Safety comes from the governance and architecture around the model — private infrastructure is one component, and only sometimes the necessary one.
Do we need to buy GPUs and hire ML engineers?
Usually not. Private AI can run in your private cloud, and the operating burden is closer to running any internal system than to running a research lab. But someone must own it — if nobody in your company can, that's a signal the architecture may be wrong for you.
When are enterprise Claude, ChatGPT, or Copilot the better answer?
When your genuinely sensitive workflows are few, your data is mostly not regulated, and enterprise controls (no-training commitments, SSO, audit) clear your bar. That's a large share of companies, and when it's you, that's the recommendation you'll get from me.
We're not technical enough for this.
Your people already use AI informally; the question is whether it's governed and pointed at the right workflows. The assessment establishes that with evidence, and staff access is a browser page — no new tools on desks.
Before you buy anything, verify the architecture.
The AI Executive Assessment is a two-week, fixed-price way to confirm private AI is your right architecture, or to find out something cheaper clears the bar.
Book an AI Strategy Call Read the reasoning first: when private AI makes sense — and when it doesn't →