Private enterprise AI
Large models, a knowledge base, and access control with audit, deployed in your own server room or a dedicated rack. Data never leaves, and it can run fully offline.
The challenge
- 01Confidential data cannot go to outside AI services.
- 02Departments and matters need separate permissions, and queries must be logged.
- 03There is no in-house AI team and no obvious place to start.
Reference architecture
How it rolls out
- 01
Assess
Understand headcount, data volume, confidentiality level and your server room.
- 02
Plan and quote
Hardware, models and service quoted line by line, after an NDA if you prefer.
- 03
Deploy on site
Install, connect the knowledge base, set permissions and audit, and train users.
- 04
Run it
Scheduled model upgrades, checks and fixes under an annual contract.
How a customer uses it
A law firm with eighty lawyers
- Before
- Lawyers wanted AI for contract review, but compliance banned uploading client documents to any outside service.
- After
- Models and a knowledge base in the firm's own server room, permissions by matter and every query logged: first-pass review takes far less time, and compliance can check the logs whenever it likes.
0
Data leaving the building
−60%
First-pass review time
6 weeks
From assessment to live
Design notes
Live in four to eight weeks
From assessment through deployment to training, usually four to eight weeks.
NDA first
We can sign a non-disclosure agreement before discussing data and processes.
Learn more
See the Labs Private AI page for everything that is delivered.