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 never leaves your wall.
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 outrightNot a per-seat licence you lose the day you stop paying the vendor. | 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 instead of metered frontier-model spend. | No | Yes |
Legal AI, answered plainly.
What is private legal AI for law firms?
Private legal AI is a model trained on a single firm's own matters and run inside that firm's infrastructure, rather than a shared assistant hosted by a vendor. Eigenwelt builds these models so a firm owns the weights outright, keeps every matter behind its own wall, and improves the model continually as its lawyers correct it.
How is an owned model different from tools like CoCounsel or a generic AI assistant?
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 — trained on your matters, hosted in your infrastructure, and sharper every cycle. The knowledge that sets your firm apart never leaves your wall and never trains a competitor's model.
Does training a model mean my clients' confidential matters leave the firm?
No. Training and inference run inside your own infrastructure with your keys and your weights. Privileged matters are never sent to a third-party API or shared cloud, which is the core reason firms move from rented assistants to owned models.
Which practice areas can an owned legal model support?
A single firm AI routes each matter through one gateway to the practice models it needs — M&A, litigation, tax, IP, employment, property, antitrust, and compliance among them. Each practice model is trained and evaluated on that area's real work, so it reasons about your precedents, not generic internet text.
Why are top law firms building their own AI instead of buying it?
Firms like Kirkland have committed hundreds of millions to building their own AI platforms because owned models turn proprietary work into durable advantage. Eigenwelt makes that path available without a nine-figure budget: small, firm-specific models you own, run, and improve in-house.
How does the model get better over time?
Every partner correction becomes training signal. Eigenwelt re-trains on each cycle of real, reviewed matters, so the model's win rate on your firm's tasks climbs while the weights and evaluations that define it stay inside your infrastructure.