Legal AI your firm owns outright.
Eigenwelt trains a model on your firm's own matters, runs it inside your infrastructure, and hands you the weights. It sharpens every time a lawyer corrects it and remains under your control.
A rented assistant, or a model you own.
Generic legal assistants are rented: they run on a vendor's cloud, learn nothing specific to your firm, and disappear the day you stop paying. An Eigenwelt model is owned outright, and every line below stays yours.
| CAPABILITY | Rented assistant | Your firm's own model |
|---|---|---|
| Trained on your firm's own mattersLearns from the work only your firm can see, not the open internet or a shared corpus. | No | Yes |
| You own the model weights outrightThe model remains the firm's asset beyond any vendor subscription. | No | Yes |
| Runs inside your own infrastructureTraining and inference behind your wall, with your keys. | No | Yes |
| Privileged work never leaves the firmMatters are never sent to a third-party API or a shared cloud. | No | Yes |
| Improves from your lawyers' correctionsEvery reviewed edit becomes training signal that compounds your advantage. | No | Yes |
| Survives the end of a vendor contractThe model and everything it learned stay with the firm, not the provider. | No | Yes |
| Predictable, right-sized costA small specialist model with controlled inference costs. | No | Yes |
Private legal AI, answered plainly.
What is private legal AI for law firms?
Private legal AI is a model trained on one firm's own matters and run inside that firm's infrastructure. The firm owns the weights and improves the model through reviewed work and private evaluations.
How is an owned model different from a generic AI assistant?
A generic assistant is rented and remains a common denominator. An owned model is trained on the firm's private work, operated under its control, and becomes a durable asset as corrections and evaluations accumulate.
Do confidential matters leave the firm for training?
No. The owned-model setup keeps training data, model weights, and evaluations in the firm's controlled infrastructure. Exact deployment boundaries are agreed with the firm and its security requirements.
How does the model improve over time?
Reviewed edits become training signal, private rubrics measure whether the next model is actually better, and only successful checkpoints are promoted. The cycle repeats as new matters and corrections arrive.