Private models
for practices that
cannot leak.
Custom AI systems that stay on-premise or in a managed Google Cloud Platform environment — trained on the language of your firm.
The usual path
Sensitive files go to a general cloud model.
A prompt box is easy. Privilege, drawings, and client lists travel with it.
The file leaves the building
Matter work, drawings, and client lists travel with the prompt — into a system you do not operate.
It may be retained, or used to train
A general cloud model is built to learn. Your records can become someone else’s weight update.
Privilege and confidentiality sit in the prompt
Once the work product is off-site, legal and professional consequences are no longer a design problem. They are an exposure.
The model still does not know the firm
You accept the risk and still get a generic instrument — not your terms, not your templates, not your taste.
One-way system
Stays
Client files
Closed corpus. Matter work, drawings, and lists never go to a public trainer.
Still available
Frontier models
Modern model capability is brought to the records — not the other way around.
Through
Architecture
On-premise hardware or a managed Google Cloud Platform solution. Designed before anyone opens a prompt.
Expert guardrails
The team constrains what the model may see, call, and return — curation, policy, and review, not a hope and a terms-of-service.
Capability comes in. Records do not go out.
For whom
Legal
Matter files and work product stay inside an environment you control. Privilege is a design constraint, not a policy footnote.
Architecture
Drawings, specifications, and client IP do not become someone else’s training data.
Consulting
Client-confidential work product remains on the system we design for your firm.
Small business
Customer lists, operations, and internal documents stay data-resident.
The practice
Design the system
Architecture first. An on-premise hardware solution, or a managed Google Cloud Platform solution. You choose.
Curate models and agents
The models that run, and the agents on top — research swarms, brief builders, intake — built for the practice, not a catalog.
Teach the model and the firm
Language modules so it knows your terms. Learning sessions so the staff actually uses it.
What does not leave
The model comes to the data.
Client files are not sent to a public trainer. We design data-resident systems: private deployment, federated in the sense that inference and adaptation happen where your records already live.
Nothing is claimed that is not held. No compliance stamp is borrowed. The work is to keep work product, privilege, and client IP inside the environment we architect.
On-premise hardware, or a managed Google Cloud Platform solution. You choose the assurance.
Ready to design the environment?
A short conversation about where your files can and cannot go — then an architecture, not a demo account.
Direct inquiries: inquiries@AIResearch.capital