Most legal AI is rented. Yours doesn't have to be.
Generic assistants, vendor-trained legal models, and horizontal post-training platforms each solve a slice of the problem, yet keep critical assets outside the firm. See how an owned, firm-trained model compares.
Four ways to bring AI into a firm. Only one you own.
Generic assistants, vendor-trained legal models, and horizontal post-training platforms each solve a slice of the problem. An Eigenwelt model is the only approach where the legal-native depth, the data, and the weights all stay yours.
| CAPABILITY | Generic AI assistant | Vendor-trained legal model | Horizontal post-training platform | 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 | Partial | Partial | Yes |
| You own the model weights outrightThe model remains the firm's asset beyond any vendor subscription. | No | No | Partial | Yes |
| Runs inside your own infrastructureTraining and inference behind your wall, with your keys. | No | No | Partial | Yes |
| Legal-native architecture and evaluationsReasoning and benchmarks built for real legal work, not generic tasks. | No | Yes | No | Yes |
| Improves from your partners' correctionsEvery reviewed edit becomes training signal that compounds your advantage. | No | No | Partial | Yes |
| Privileged work never trains a shared modelYour matters never strengthen a system every competitor also rents. | No | No | Yes | Yes |
| Predictable, right-sized costSmall specialist models with controlled inference costs. | No | No | Partial | Yes |
Choosing between the options.
What are the alternatives to a privately owned legal AI model?
Firms broadly choose between generic assistants, vendor-trained legal models, horizontal post-training platforms, and a privately owned model trained on their own matters. The trade-off covers capability, ownership of the model and data loop, and control of the resulting advantage.
How is Eigenwelt different from a vendor-trained legal model?
A vendor-trained model can be legal-native, but the model and its accumulated advantage remain with the vendor. Eigenwelt builds a firm-specific model whose weights, private evaluations, and learning loop belong to the firm.
Why not use a horizontal post-training platform?
A post-training platform provides useful infrastructure, but the firm still needs a legal data model, security architecture, evaluations, training recipes, and end-user applications. Eigenwelt builds those layers as one legal-native stack.
Are generic AI assistants enough for a law firm?
They are useful for broad drafting and research, but they do not become the firm's proprietary model. An owned system adds structured private knowledge, firm-specific evaluations, and recurring training from reviewed work.